Generic Computer Vision Methods

Generic computer vision methods 1. General image segmentation methods 1. Clustering methods 2. Compression-based methods 3. Histogram-based methods 4. Region-growing methods 5. Split-and-merge methods 6. Partial differential equation-based methods 7. Graph partitioning methods 8. Multi-scale segmentation 9. Semi-automatic segmentation 10. Trainable segmentation 11. Segmentation benchmarking 2. Accumulation/voting methods 1. Hough transform

1.1. Adaptive Hough Transform 1.2. Hough transform of curves 1.3. Cascaded Hough Transform 1.4. Generalised Hough transform 1.5. Hierarchical Hough Transform 1.6. Maximum margin Hough transform 1.7. Probabilistic Hough Transform 1.8. Randomised Hough Transform 1.9. Surface finding 2. Tensor Voting 3. Diffusion/PDE/Time based evolution methods 1. Heat kernel -- see also scale-space which is based on linear diffusion and the Gaussian (heat) kernel 4. Eigendecompositions 5. Genetic algorithms/Genetic programming 6. Graph Methods 1. Graph representations 1.1. Adjacency graph 1.2. Association graph 1.3. Attributed Graph 1.4. Dynamic Feature Graph 1.5. Graph embedding 1.6. Hierarchical graph/Hypergraph representations 1.7. Laplacian smoothing 1.8. Median graph 1.9. Optimal Basis Graphs 1.10. Probabilistic graphical model, Probabilistic graph theory 2. Graph matching 2.1. Bayesian Graph Matching 2.2. Bipartite matching 2.3. Graph cuts 2.4. Graph kernel methods 2.5. Graph edit distance 2.6. Maximal cliques in Association graphs 2.7. Spectral decomposition methods 2.8. Subgraph isomorphism problem 3. Multidimensional scaling 7. Image pyramids and scale reduction 1. Adaptive Pyramids 2. Gaussian pyramids 3. Laplacian pyramids 8. Level sets 1. Level set trees 9. Minimum description length 10. Multiple Scales/Resolutions 1. Multiple-scale analysis 1.1. Multi-Scale Integration 2. Fractals 3. Ranklets 4. Scale space 5. Wavelets 5.1. Noiselets 11. Graph, networks and connectionist methods 1. Bayesian networks 2. Connectionist methods 3. Gaussian processes methods 4. Neural networks 5. Probabilistic graphical models 5.1. Expectation propagation 5.2. Belief propagation 5.3. Message passing 5.3.1. Variational message passing 5.3.2. Tree reweighted message passing 6. Radial basis function networks 7. Wavelet Networks 12. Regularization 13. Relaxation 1. Continuous 2. Discrete 3. Probabilistic/Stochastic 4. Linear programming relaxation 5. Lagrangian relaxation 14. Spatial indexing/hashing 15. Subpixel Methods 16. Super-resolution 17. Certainty/uncertainty representations 1. Bayesian networks 2. Discrete (See Relaxation) 3. Fuzzy logic 4. Intervals 5. Probabilities 18. Vision paradigms 1. Active vision 2. Geometric vision (See Vision Geometry and Mathematics and Geometric Representation of Model Features 3. Purposive Vision 4. Qualitative Vision 19. Vision system design and characterization 1. Propagation of uncertainty 2. Performance testing in vision 3. Receiver operating characteristic

Geometric feature extraction methods 1. Compressed image feature extraction 1. Camera motion estimation 2. Color Distributions/Descriptors 3. Edge detection in compressed images 4. Salient Points 5. Texture descriptors from compressed images 2. Connected-component labeling 3. Corner and interest Point feature detectors 1. The level curve curvature approach 2. FAST: Features from the Accelerated Segment Test 3. SIFT: Scale-Invariant Feature Transform

3.1. David Lowe's method 4. Forstner operator 5. Haralick operator

6. Harris/Plessey Corner Finder 6.1. Harris affine 6.2. Harris laplace 7. Histogram of oriented gradients 8. Moravec operator 9. Speeded Up Robust Features (SURF) 10. Shape context, Histogram of Shape Context (HoSC) 11. SUSAN corner detector 12. Wavelet-Based salient point detection 4. Curve fitting/Local curvature estimation 1. Circle fitting 2. Curve smoothing 3. Ellipse fitting 4. Hyperbola fitting 5. Edge detection and enhancement 1. Adaptive edge detection 2. Canny edge detector 3. Color edge detectors 4. Edge types 4.1. Edge type labelling 5. Energy function based edge detectors 6. Evaluation of edge detectors 6.1. Kadir–Brady saliency detector evaluation 6.2. Hessian affine region detector evaluation 7. Extended edge detection 8. First derivative, Gradient edge detection 9. High-pass filter edge enhancement 10. Hueckel and other parametric fitting edge detectors 11. Kirsch compass edge detector 12. Marr–Hildreth, Laplacian of Gaussian, Zero crossing, Difference of Gaussians 13. Moving edge detection 14. Multi-dimension edge detection 15. Multi-scale edge detectors 16. Optimal edge detectors (see also Canny edge detector) 17. Sobel operator 18. Prewitt operator 19. Range/Depth image edge detectors 20. Roberts Cross edge detector 21. Robinson edge detector 22. Second derivative operators 22.1. Laplace operator 22.2. The Laplacian of Gaussian 22.3. Difference of Gaussians 23. Subpixel edge detection (See Subpixel methods) 24. Walsh function 6. Edge/line/Contour feature following, grouping, linking and tracking 1. Pixel connectivity 2. Contour tracking 3. Dynamic programming 4. Edge thresholding and linking 5. Graph search 6. Hough transform 7. Hysteresis tracking 8. Paired boundaries, Paired contours 9. Edge relaxation 10. Search trees 11. Subjective/Illusory contours 11.1. Stochastic completion fields 7. Global structure extraction 1. Ribbons 2. Interest point detection 3. Symmetry lines, Symmetry planes 8. Feature histograms 1. Histogram analysis 2. Multi-dimensional feature histograms 3. Pairwise histograms 9. Image descriptors 1. Differential invariant

2. Visual descriptors 3. MPEG-7 descriptors 4. Color structure descriptor 5. Edge histogram descriptor 6. Color layout descriptor 10. Line detection 1. Image Ridges for Line Detection 11. Feature mensuration 1. Scene object size estimation 2. Subpixel/Superresolution Methods (See Subpixel methods) 12. Model-based feature detection/segmentation 1. Mumford-Shah Functional 13. Point or Pixel descriptions (See also Classification transforms) 1. Bar, line points 2. Blob/center-surround points 3. Gabor filters 3.1. Log-Gabor filters 4. Gaussian derivatives and the notion of a visual front-end 5. Receptive fields 6. Semantic texton forests 7. Steerable pyramids

14. Primal sketch 15. Blob and region detection 1. Bayesian network methods 2. Chroma keying 3. Facet detection 4. Maximally stable extremal regions 5. Region boundary extraction 6. Superpixels (wiktionary) 7. Texture-based region detection

16. Region detection methods 1. Divide and conquer 2. Region based, Model based segmentation 3. Recursive splitting 3.1. Implicit k-d tree

4. Region growing 5. Scale-space segmentation 6. Split and merge 7. Thresholding 8. Watersheds of gradient magnitude 9. Waterfall segmentation hierarchy 17. Ridge and valley detection 18. Hidden surface determination 19. Skew analysis and estimation 20. Spatial relationship detection 1. Collinearity 2. Coplanarity 3. Intersection/Cotermination 4. Relative orientation 21. Special feature extraction 1. Focus of expansion 2. Ground plane 3. Horizon detection

4. Occluding contour detection 5. Vanishing point 22. Structure tensor 23. Surface patches in volumes 1. Optimal surface detection 2. Zucker-Hummel surface detection operator 24. Surface segmentation from 2 1/2D or 3D data (see also range segmentation) 1. Curvature-based surface patch detection 2. Cylinder/Tubular structure detection 3. Planar facet/triangulation patch detection 3.1. Marching cubes 3.2. Marching tetrahedrons 3.3. Surface fitting 4. Planar surface models 5. Surface clustering/grouping 6. Reeb graph 7. Surface discontinuity detection 7.1. Curvature discontinuity detection 7.2. Depth discontinuity detection 7.3. Surface Orientation discontinuity detection 8. Surface fitting/Region growing 8.1. Cylinder patch extraction 8.2. Range data based region extraction 8.3. Quadric fitting 8.4. Surface shape classification 9. Surface shape parameter estimation 9.1. Cylinder extraction

9.2. Ellipsoid/Sphere 9.3. Free-Form Surface 9.3.1. Dual surface thin shell fitting 9.4. Detection of 3D objects (Planes and cylinders) 9.5. Quadric 9.6. Torus 10. Surface Triangulation

25. Surface shape (Shape-from-X methods) 1. Shape from Contours/Silhouettes 2. Shape from defocus 3. Shape from focus 4. Shape from geometric constraints 5. Shape from multimodal integration 6. Shape from line drawings 7. Monocular depth cues 8. Structure from motion 9. Shape from multiple sensors 10. Shape from perspective 11. Shape from photo-consistency 12. Shape from photometric Stereo 13. Shape from polarization 14. Shape from shadows 15. Shape from specularities 16. Shape from structure light

17. Shape from texture 26. Image texture 1. Texture boundary detection 2. Texture classification 3. Color texture 4. Fourier descriptors 5. Hierarchical textures 6. Shape texture/surface roughness characterization 7. Structural/syntactic texture representations 8. Statistical texture representations 8.1. Co-occurrence matrix (texture) 8.2. Edge frequency 8.3. Law's texture energy measures 8.4. Filter-based descriptors 8.5. Fractal analysis 8.5.1. Hausdorff measure 8.6. Local binary patterns 8.7. Local ternary patterns 8.8. Markov random fields 8.9. Moments of intensity 8.10. Run-length encoding 8.11. Spatial frequency 9. Texels 9.1. Texon/Texel invariants and representations 10. Texture gradients/Directions/Oriented patterns

11. Wavelet-based texture descriptors 27. Topological image description 28. Visual routines, empirical feature detectors 29. Volume detection 1. Voxel-based morphometry 2. Generalized cylinder detection 3. Superquadric detection 30. Wavelet moment invariants 1. Daubechies wavelet

Geometry and mathematics 1. Basic Representations 1. Coordinate systems 1.1. Cartesian coordinate system 1.2. Cylindrical coordinate system 1.3. Hexagonal coordinate system (see external links)

1.4. Log-Polar coordinate system 1.5. Polar coordinate system 1.6. Spherical coordinate system 2. Digital topology 3. Dual space 4. Homogeneous coordinates 5. Pose/Rotation/Orientation Representations 5.1. Axis-angle representation 5.2. Clifford algebra 5.3. Euler angles 5.4. Exponential map 5.5. Quaternion/Dual quaternion 5.6. Rotation matrix 5.7. Pitch/Yaw/Roll 2. Distance metrics 1. Affine distance 2. Algebraic distance 3. Bhattacharyya distance 4. Chi-square test/metric 5. Curse of dimensionality 6. Earth mover's distance 7. Euclidean distance 8. Fuzzy intersection 9. Hausdorff distance 10. Jeffrey divergence 11. Kullback–Leibler divergence 12. Mahalanobis distance 13. Manhattan/City block distance 14. Minkowski distance 15. Procrustes analysis 16. Quadratic form 17. Specific structural similarity 17.1. Curve similarity 17.2. Region similarity 17.3. Volume similarity 3. Elementary mathematics for Vision 1. Coordinate systems/Vectors/Matrices/Derivatives/Gradients/Probability 2. Derivatives in sampled images 4. Mathematical optimization 1. Golden section search 2. Lagrange multipliers/Constraint optimization 3. Multi-dimensional optimization 3.1. Random optimization 3.2. Global optimization 3.2.1. Ant colony optimization 3.2.2. Downhill simplex 3.2.3. Genetic algorithms 3.2.4. Graduated optimization 3.2.5. Markov random field optimization 3.2.6. Particle swarm optimization 3.2.7. Simulated annealing 3.3. Optimization with derivatives 3.3.1. Levenberg–Marquardt 3.3.2. Gradient descent 3.3.3. Quasi-Newton method 4. Model selection 5. Variational methods

5. Linear algebra for computer vision 1. Eigenfunction 2. Eigenvalues and eigenvectors 3. Principal component and Related Approaches 3.1. Dimensionality reduction 3.2. Linear discriminant analysis 3.3. Factor analysis 3.4. Fisher's linear discriminant 3.5. Independent component analysis 3.6. Kernel linear discriminant analysis 3.7. Kernel principal component analysis 3.8. Locality preserving projections 3.9. Non-negative matrix factorization 3.10. Optimal dimension estimation 3.11. Sufficient dimension reduction 3.12. Principal component analysis/Karhunen–Loève theorem 3.13. Principal geodesic analysis 3.14. Probabilistic principal component analysis 3.15. Rao–Blackwell theorem 4. Sammon projection 5. Singular value decomposition 6. Structure tensor 6. Multi-sensor/Multi-view geometries 1. 3D reconstruction 1.1. 3D shape from 2D projections 1.2. 3D reconstruction from multiple images 1.3. Slice-based reconstruction 2. Projective reconstruction 3. Baseline stereo 3.1. Narrow baseline stereo 3.2. Wide baseline stereo 4. Binocular stereo algorithms 4.1. Cooperative stereo algorithms 4.2. Binocular disparity 4.2.1. Subpixel disparity 4.3. Dense stereo matching approaches 4.4. Dynamic programming (stereo) 4.5. Feature matching stereo algorithms 4.6. Gradient matching stereo algorithms 4.7. Image rectification 4.7.1. Planar rectification 4.7.2. Polar rectification 4.8. Log-polar stereo 4.9. Multiresolution analysis 4.10. Panoramic image stereo algorithms 4.11. Phase matching stereo algorithms 4.12. Region matching stereo algorithms 4.13. Weakly/Uncalibrated stereo approaches 4.14. Spherical stereo 5. Epipolar geometry/Multi-view geometry 5.1. Absolute conic 5.2. Absolute quadric 5.3. Essential matrix 5.4. Fundamental matrix 5.5. Grassmannian space/Plücker embedding 5.6. Homography tensor 5.7. Transfer and novel view synthesis 5.8. Trifocal tensor 6. Image-based modeling and rendering 7. Plenoptic modeling 8. Image feature correspondence 8.1. Active stereo 8.2. Disparity gradient limit (feature correspondence) 8.3. Epipolar constraint 8.4. Feature contrast 8.5. Feature orientation 8.6. Grey-level similarity (feature correspondence) 8.7. Lipschitz continuity 8.8. Surface continuity 8.9. Surface smoothness 8.10. View consistency constraint 9. Scene reconstruction/Surface interpolation 9.1. Adaptive mesh refinement 9.2. Constrained reconstruction 9.3. Thin plate models 9.4. Texture synthesis/Texture mapping 9.5. Triangulation 9.6. Volumetric reconstruction 9.6.1. Visual hull 10. Trinocular (and more) stereo 7. Parameter Estimation 1. Bayesian methods 2. Constrained least squares 3. Linear least squares 4. Optimization 5. Robust techniques 8. Probability and Statistics for Computer Vision 1. Autoregression 2. Bayes estimator 3. Bayesian inference networks 4. Causal models 5. Correlation and dependence 6. Covariance and Mahalanobis distance in Vision 7. Dempster–Shafer theory 8. Multimodal distribution 9. Normal distribution 10. Heteroscedastic noise 11. Homoscedastic noise 12. Hidden Markov models 13. Probability axioms 14. Statistical hypothesis testing/Analysis of variance 15. Information theory 16. Kalman filters 16.1. Unscented Kalman filters 17. Canonical correlation 18. Kernel regression 19. Least mean squares estimation 20. Least median square estimation and estimators 21. Log-normal distribution 22. Logistic regression 23. Maximum likelihood 24. Model/Curve fitting 25. Monte Carlo method 26. Point process 27. Markov chain/Markov chain Monte Carlo methods 28. Markov random field 28.1. Applications 28.2. Conditional random fields 28.3. Multi-level Markov random fields 28.4. Optimization methods 28.4.1. Gibbs sampling 28.4.2. Graduated optimization 28.4.3. Graph cuts in computer vision 28.4.4. Iterated conditional modes 28.4.5. Simulated annealing 29. Mixture models and expectation-maximization (EM) 29.1. Gaussian mixture model 29.2. Categorical mixture model 30. Normalization 31. Non-parametric statistics 31.1. Non-parametric regression 31.2. Kernel density estimation 32. Poisson distribution 33. Density estimation 34. Random number generation 35. Robust estimators 9. Projective geometry/Projective transformations 1. Affine projection model/Affine transformation 2. Anamorphic projection/Catadioptric system 3. Central cylindrical projection 4. Orthographic projection 5. Map projection 6. Homography 7. Hierarchy of geometries 8. Perspective projection 9. Projective plane 10. Projective space 11. Real camera projection

12. Similarity matrix 13. Weak-perspective 13.1. Tomasi-Kanade factorization 10. Projective invariants/cross-ratio 1. Absolute points (points at infinity) 2. Affine invariants 2.1. Affine geometry of curves 3. Collineation 4. Conics/Quadrics 5. Coplanarity 6. Differential invariants 7. Duality 8. Integral invariants 9. Laguerre formula 10. Pencils 11. Quasi-invariants 12. Structural invariants 12.1. Cartan's equivalence method 11. Relational shape descriptions 1. Curves 1.1. Adjacency/Connectedness 1.2. Relative curvature 1.3. Relative length 1.4. Relative orientation 1.5. Separation 2. Regions 2.1. Adjacency/Connectedness 2.2. Relative area/size 2.3. Separation 3. Surfaces 3.1. Adjacency/Connectedness 3.2. Relative area/size 3.3. Relative orientation 3.4. Separation 4. Volumes 4.1. Adjacency/Connectedness 4.2. Relative orientation 4.3. Relative volume/size 4.4. Separation 12. Shape properties 1. Geometric Morphometrics 2. Kendall´s Shape Space 3. Points and local invariants 3.1. Scale-invariant feature transform 4. Curves and Curve Invariants 4.1. Affine curvature 4.2. Arc length 4.3. Bending energy 4.4. Chord distribution 4.5. Curvature, Torsion of a curve, Radius of curvature 4.6. Differential geometry, Frenet–Serret formulas 4.7. Invariant Points: Inflections/Bitangents 5. Image regions and region invariants 5.1. Compactness measure of a shape 5.2. Area 5.3. Perimeter 5.4. Center of mass, Centroid 5.5. Eccentricity, Elongatedness 5.6. Euler number/Genus 5.7. Extremal points 5.8. Feret's diameter 5.9. Fourier descriptors 5.10. Minimum bounding rectangle 5.11. Image moments 5.11.1. Affine moments 5.11.2. Bessel-Fourier moments 5.11.3. Binary moments 5.11.4. Color moments 5.11.5. Central moments 5.11.6. Eigenmoments 5.11.7. Fourier-Mellin moment invariants 5.11.8. Gaussian-Hermite moments 5.11.9. Texture moments 5.11.10. Hahn moments 5.11.11. Krawtchouk moments 5.11.12. Legendre moments 5.11.13. Orthogonal moments 5.11.14. Racah moments 5.11.15. Chebyshev moments 5.11.16. Zernike and velocity moments 5.12. Orientation 5.13. Sphericity 5.14. Rectangularity 5.15. Rectilinearity 5.16. Roundness 5.17. Topological invariants 5.17.1. Euler characteristic 6. Differential geometry of surfaces 6.1. Parametric surfaces 6.2. Common shape classes and representations 6.2.1. Cone representations 6.2.2. Cyclide 6.2.3. Cylinder representations 6.2.4. Ellipsoid/Sphere Representations 6.2.5. Thin plate splines 6.2.6. Plane representations 6.2.7. Polyhedra representations 6.2.8. Quadric representations 6.2.9. Torus representations 6.3. Fundamental surface forms 6.3.1. First fundamental form 6.3.2. Second fundamental form 6.4. Gauge coordinates 6.5. Hessian 6.6. Laplace–Beltrami operator 6.7. Metric derivative 6.8. Principal curvature and directions and other local shape representations 6.8.1. Deviation from flatness 6.8.2. Gauss–Bonnet surface description 6.8.3. Gaussian curvature 6.8.4. Koenderink's shape classification 6.8.5. Mean curvature 6.8.6. Minimal surface 6.8.7. Parabolic points 6.8.8. Ridges 6.8.9. Umbilics 6.9. Quadratic variation 6.10. Ricci flow 6.11. Surface area 6.12. Surface normals and tangent planes 6.13. Orientability 7. Symmetry 7.1. Affine symmetry

7.2. Bilateral symmetry 7.3. Rotational symmetry 7.4. Skew symmetry 8. Volumes 8.1. Elongatedness 8.2. 3D moments and moment invariants 9. Volume 13. Transformations (geometric), registration and pose estimation methods 1. Poste estimation

2. 2D to 2D pose estimation 2.1. Methods 3. 2D to 3D pose estimation

3.1. Methods 4. 3D to 3D pose estimation 4.1. Methods 5. Affine transformation 5.1. Minimal data estimation 6. Bundle adjustment 7. Euclidean transformation 7.1. Least-square euclidean transformation estimates 7.2. Minimal data euclidean transformation estimation 7.3. Robust euclidean transformation estimates 8. Homographic transformation 8.1. Least-square homography transformation estimates 8.2. Robust homography transformation estimates 9. Kalman filter pose estimation methods 10. Partially constrained pose 10.1. Incomplete information 10.2. Intrinsic degrees of freedom 11. Projective transformation 11.1. Direct linear transformation 11.2. Robust estimates 12. Similarity transformation 13. Articulated body pose estimation Image physics related concepts 1. Color and reflectance 1. Albedo, Irradiance, Radiance, Reflectance, Luminance 2. Bidirectional reflectance distribution function 3. Color difference 4. Color vision, Colorimetry 4.1. Illumination, lightness, color constancy, reflectance recovery and shading correction 4.2. Color correction 4.3. Color normalization 5. Color representation systems 5.1. CIE 1931 color space 5.2. Color spaces and color space conversions 5.3. YIQ color space 5.4. Principal component basis space 6. Dichromatic reflectance model 7. Empirical color representations 7.1. Statistical colour representations 7.2. Basis function color representations 8. Reflectance map 9. Special worlds 9.1. Mondrian 9.2. Monochrome 2. Image content, structure and formation 1. Image content and formation 2. Neighborhood operation 3. Photometric content 3.1. Gamma correction 3.2. Hue/White balance correction 3.3. Quantigraphics and multiple observations 3.4. Saturation correction 4. Spatial frequency and sampling 5. Image quantization and compression 3. Light and illumination 1. Elementary physics of light and illumination 1.1. Electromagnetic radiation, Electromagnetic spectrum 1.2. Elementary effects: Diffraction, Interference, Reflection, Refraction, Transmittance 1.3. Elementary manipulation: Collimation, Diffraction gratings, Diffusion, Mirrors, Prisms 1.4. Standard sources: Light sources 2. Source geometry 2.1. Backlighting 2.2. Area light source 2.3. Diffuse reflection 2.4. Line light source 2.5. Point light source 2.6. Summary of different light sources 3. Special situations 3.1. Illumination techniques for improving observation 3.2. Mutual illumination and interreflections 3.3. Polarization 3.4. Shadow and highlight enhancement 3.5. Spectral filtering 4. Special sources 4.1. Acoustic Sonar 4.2. Infrared 4.3. Laser 4.4. Scanning electron microscope 4.5. Synthetic aperture radar 4. Image noise and noise in video 1. Distributions 1.1. Gaussian noise 1.2. Salt and pepper noise 1.3. Speckle noise 1.4. Uniform noise 2. Noise sources 2.1. Amplifier noise 2.2. Heteroscedastic noise 2.3. kTC noise 2.4. Electronic noise 2.5. Photon noise 2.6. Quantization noise 2.7. Thermal noise 5. Optics 1. Optical transfer function 2. Modulation transfer function 3. Basic geometric optics 4. Catadioptric optics 5. Depth of field 6. Depth of focus 7. Focal length 8. Focus invariant imaging 9. Image control: Focus, Aperture, Exposure/Shuttering, Gain, Offset 10. Image distortion 11. lenses 12. Telecentric lenses 13. Vignetting 6. Sensor response 7. Surface shape physics 1. Empirical surface models 2. Surface classes 2.1. Fractal surfaces 2.2. Lambertian surfaces 2.3. Phong reflection 2.4. Specular reflection 3. Texture

Image Processing Architectures & Control Structures 1. Architectures for visual processing 1. Markup languages

2. Attention, Foveation, Saccade 1. Focus of attention 2. Gaze-contingency paradigm 3. Visual salience 3. Behavior-based control 1. Behavior-based robotics 4. Blackboard system 5. Classes of vision systems 1. Continuous process systems 2. Real-time systems/Video rate systems 3. Single image processes 6. Expert system control/Knowledge based system 7. Hierarchical control systems 1. Top-down and bottom-up systems 2. Model-based systems 8. Parallel processing 9. Sequential/Serial processing 10. Visual search Image transformations and filters 1. Image enhancement 1. Artistic effects 2. Brightness adjustment 3. Contrast adjustment 3.1. Histogram equalization 3.2. Contrast stretching 4. Edge sharpening 5. Histogram equalization/Adaptive histogram equalization 6. Quantile normalization 7. Saturation adjustment 8. Upsampling 2. Distance and skeleton 1. Distance transform 2. Eccentricity transform 3. Laplacian eigenspace 4. Medial axis 5. Morphological skeletons 6. Topological skeletons 7. Local symmetry 8. Principal component encoding 9. Shock (mechanics) 9.1. Shock filter, Shock tree, Shock graph 9.2. Shock response spectrum 10. Smoothed local symmetry 3. Geometric transformations 1. Euclidean: Rotation, Translation, Reflection 2. Subsampling, Interpolation, zooming 3. Rectification 4. Image scaling, Shear transformation, Affine transformation, Projective transformation 5. Image warping 4. Global transforms 1. Discrete cosine/Discrete sine transforms 2. Fourier transform 2.1. Frequency domain filtering 2.2. Homomorphic filtering 2.3. Non-uniform Fourier transform 2.4. Fourier optics 2.5. Log-polar/Polar Fourier transform 3. Haar transform 4. Hartley transform 5. Hadamard/Walsh transform 6. Histogram transformation 6.1. Histogram equalization 6.2. Adaptive histogram equalization 6.3. Image histogram 6.4. Color histogram 7. Karhunen-Loeve transform 8. Radon transform, Mojette transform 9. Ridgelet transform 10. Slant transform 11. Modified wavefield transform 12. Trace transform 13. Wavelet transform 5. Image and Video compression 1. Adaptive coding 2. Arithmetic coding 3. Block Truncation Coding, Gif, TIFF, Lempel–Ziv–Welch, Huffman coding 4. Color image compression 5. Differential Pulse Code Modulation (DPCM) 6. Feature extraction from compressed images 7. Fractal compression 8. Hierarchical compression 9. Lossy compression 10. Lossless compression 11. Image quality evaluation/comparison 12. JPEG 13. PNG 14. Model-based coding 15. Motion coding, Video coding 16. MPEG 17. Predictive methods 18. Stereo image compression 19. Vector quantization 20. Wavelet/Scalar quantization 6. Image stabilization 7. Local operator transforms 1. Adaptive filtering 2. Composite filtering 3. Convolution 3.1. Normalized convolution 3.2. Separable templates 3.2.1. Gaussian blur 4. Difference of Gaussians 5. Differentiation filtering 6. Frequency filtering 6.1. High-pass filter 6.2. Low-pass filter 6.3. Matched filter 7. Image noise reduction and restoration 8. Adaptive smoothing 8.1. Anisotropic filtering 8.2. Anscombe transform 8.3. Moving average smoothing 8.4. Bayesian filtering 8.5. Bilateral filtering 8.6. Brightness distortion correction 8.6.1. Helicon filter 8.7. Conservative smoothing 8.8. Crimmins smoothing 8.9. Deconvolution 8.10. Diffusion methods 8.10.1. Diffusion equation 8.10.2. Anisotropic diffusion 8.11. Edge-preserving smoothing 8.12. Gaussian smoothing 8.13. Global filters 8.13.1. Tikhonov regularization 8.13.2. Maximum entropy methods 8.14. Kalman filter based noise reduction 8.15. Alpha beta filter 8.16. Phase-locked loop 8.17. Kuwahara filter 8.18. Bayer filter 8.19. Lee's local statistics filter 8.20. Local nonlinear image restoration 8.21. Median filtering 8.22. Median flow filtering 8.23. Median least variance/Median coefficient of variation filters 8.24. Markov chain Monte Carlo 8.25. Multispectral images 8.25.1. Multichannel/Multispectral filtering 8.26. Exponential smoothing 8.27. Partial Differential Equations (PDEs), Diffusion methods 8.27.1. Geometric flow, Ricci flow 8.27.2. Tangential diffusion 8.28. Order statistic filters 8.29. Savitzky–Golay smoothing filter 8.30. Scale space filter 8.31. Spline smoothing 8.32. Temporal averaging 8.33. Wiener filter 8. Morphological transformations 1. Binary mathematical morphology 2. Boolean convolution 3. Conditional dilation 4. Dilate/Erode transformation 5. Fuzzy morphology/Soft morphology 6. Grayscale morphology 6.1. Grayscale dilation, Grayscale erosion, Umbra dilation, Umbra erosion 6.2. Greylevel, Greyscale morphological opening, closing 6.2.1. Opening 6.2.2. Closing 7. Morphological smoothing 8. Morphological gradient 9. Morphological laplacian 10. Hit-or-miss transform 11. Morphological segmentation 12. Morphological opening, Morphological closing 13. Region-filling, Propagation 14. Thinning, Thickening 15. Top-hat transform 16. Watershed transform 9. Pixel classification 1. Color, Multispectral based 2. Curvature, Shape based 3. Edge type labeling 4. Intensity based 5. Shadow type labeling 6. Texture based 10. Point binary image operator transforms 1. Image arithmetic 1.1. Image operators: Addition 1.2. Image operators: Bitshift 1.3. Image operators: Blending 1.4. Image operators: Division 1.5. Image operators: Multiplication 1.6. Image operators: Subtraction 2. Binary operations 2.1. Image operators: AND/NAND 2.2. Image operators: NOT/INVERT 2.3. Image operators: OR/NOR 2.4. Image operators: XOR/XNOR 11. Point unary image operator transforms 1. Clipping 2. Pixel logarithm and exponential 3. Gamma correction 4. Ordinal transformation 5. Thresholding 5.1. Adaptive thresholding 5.2. Edge image thresholding 5.3. Balanced histogram thresholding 5.4. Multiband thresholding 5.5. Quantization techniques 5.6. Threshold selection 12. Segmentation, Grouping transforms 1. Property basis 1.1. Chroma keying 1.2. Intensity based segmentation (See Region detection -> thresholding) 1.3. Motion based segmentation (See Motion field->Region segmentation/decomposition) 1.4. Surface shape based segmentation (See Curvature-based surface patch detection) 1.5. Texture based segmentation (See Texture-based region segmentation) 2. Structures 2.1. Curve segmentation (See Boundary/Line/Curve segmentation) 2.2. Blob detection 2.3. Surface segmentation (See Surface segmentation from 2 1/2D or 3D data) 2.4. Volume segmentation 3. Technologies 3.1. Clustering based segmentation 3.2. Connected components/Blob extraction 3.3. Model based feature detection/Model based segmentation 3.4. Minimum description length 3.5. Region growing based segmentation 3.6. Relaxation labeling 3.7. Rule-based/Expert-system based segmentation 3.8. Thresholding based segmentation Introductory visual neurophysiology 1. Crossmodal linkages 1. Audition 1.1. Sensory substitution 2. Olfaction 3. Touch 4. Sensory substitution 5. Mirror neuron 6. Language 6.1. Audio-visual speech recognition 7. Synesthesia, Neural basis of synesthesia 2. Neurophysiological methods 1. Disturbances and disorders 1.1. Lesion 1.2. Scotoma 1.3. Blind spot 1.4. Binasal hemianopsia 2. Bitemporal hemianopsia 3. Color blindness 3.1. Achromatopsia 3.2. Dichromacy 3.3. Monochromacy 4. Agnosia 4.1. Visual agnosia 4.2. Auditory agnosia 4.3. Color agnosia 5. Staining/Histology 6. Electrophysiology 6.1. Intracellular recording 6.2. Extracellular recording 6.3. Iontophoresis 7. Neuroimaging 7.1. Optical imaging of intrinsic signals 7.2. Calcium imaging 7.3. Photofragment-ion imaging 7.4. Voltage-sensitive dye imaging 7.5. Functional Magnetic Resonance Imaging (fMRI) 7.5.1. Haemodynamic response 7.5.2. BOLD response 7.6. Positron emission tomography (PET) 7.7. Single-photon emission computed tomography (SPECT) or (SPET) 8. Electroencephalography (EEG) 9. Transcranial magnetic stimulation (TMS) 10. Magnetoencephalography (MEG) 11. Molecular biology 12. Genetics 13. Neuropharmacology, Neuropsychopharmacology 3. Visual system structure

1. Neurons 2. Visual pathway 2.1. Dorsal and ventral stream 2.2. Modularity of mind 2.2.1. Visual modularity 2.2.2. Language module 3. Cortical column 4. Neural coding 5. Receptive fields 6. Extra-classical receptive fields and surround modulation 7. Topographic maps 8. Binding & Synchronization 8.1. The binding problem 8.2. Neural binding 8.3. Neural oscillations 9. Cortical magnification 10. Lateralization of brain function 11. Attention 4. Visual system components 1. Neuroanatomy 2. The eye 2.1. Structure and anatomy 2.2. Function 2.3. Eye movements 2.3.1. Saccades and Microsaccades 3. The retina 3.1. Development 3.2. Anatomy and Structure 3.3. Blind spot 3.4. Fovea 3.5. Circuitry 3.5.1. Photoreceptors 3.5.2. Horizontal cells 3.5.3. Bipolar neurons/cells 3.5.4. Amacrine cells 3.5.5. Ganglion cell 3.5.6. Glial cell 4. Function and computations 4.1. Light and dark adaptation 4.2. Lateral inhibition 4.3. Color vision 5. Lateral Geniculate nucleus (LGN) 5.1. Development 5.2. Anatomy and structure 5.3. Function and computations 5.4. Parvocellular and Magnocellular pathways 6. Visual cortex 6.1. Plasticity and development 6.1.1. Neuroplasticity 6.1.2. Synaptic plasticity 6.1.3. Spike-timing-dependent plasticity 6.1.4. Hebbian theory 6.1.5. Long-term potentiation 6.1.6. Long-term depression 6.2. Primary visual cortex (V1) 6.2.1. Anatomy and structure 6.2.2. Neuron types 6.2.2.1. Pyramidal cells 6.2.2.2. Granule cells 6.2.3. Connectivity 6.2.4. Simple cells, Complex cells, Hypercomplex cells 6.2.5. Neural coding 6.2.6. Parvocellular and Magnocellular pathways 6.2.7. Cortical layers 6.2.8. Contrast sensitivity 6.2.9. Selectivity and mapping of stimulus features 6.2.9.1. Ocular dominance 6.2.9.2. Geniculate ganglion 6.2.9.3. Orientation 6.2.9.4. Direction 6.2.9.5. Spatial frequency 6.2.9.6. Colour 6.2.9.7. Binocular vision 6.3. Extrastriate cortex, Brodmann area 18, Brodmann area 19 6.3.1. V2 6.3.2. Visual cortex#Third visual complex, including area V3 6.3.2.1. Dorsomedial area 6.3.2.2. Ventral tegmental area 6.3.3. V4 6.3.4. Dorsal Prelunate (DP) 6.3.5. V5/MT 6.4. Higher visual cortical areas 6.4.1. Medial superior temporal area 6.4.2. Parietal lobe 6.4.2.1. LIP (Lateral intraparietal) 6.4.2.2. VIP (Ventral Intraparietal) 6.4.2.3. MIP (Medial Intraparietal) 6.4.2.4. AIP (Anterior Intraparietal) 6.4.2.5. CIP (Caudal Intraparietal) 6.4.3. Brodmann area 7 6.4.4. Inferotemporal cortex 6.4.5. Broadman area 22 6.4.6. Cuneus 6.4.7. Superior Frontal Sulcus (SFS) 6.4.8. Superior temporal sulcus 6.4.9. Frontal Eye Fields (FEF) 7. Superior colliculus 8. Pulvinar nuclei 9. Caudate nucleus Introductory visual psychophysics/psychology 1. Attention 2. Subjective constancies 1. Color constancy 2. Lightness constancy 3. Shape constancy 4. Size constancy 5. Distance constancy 6. Location constancy 3. Disorders/disturbances 1. Aniseikonia 2. Achromatopsia 3. Scotopic sensitivity syndrome 4. Blindness 4.1. Acquired vision 4.2. Blindsight 4.3. Change blindness 4.4. Color blindness 4.5. Repetition blindness 5. Visual agnosias 5.1. Apperceptive agnosia 5.2. Associative agnosia 5.3. Color agnosia 5.4. Mirror agnosia 5.5. Prosopagnosia 4. Design of experiments 1. Detection theory 2. Fourier analysis 3. Interstimulus interval 4. Psychometric function 5. Spatial frequency 6. Statistics 7. Stevens' power law 8. Temporal frequency 9. Weber–Fechner law 5. Experimental stimuli 1. Autostereograms 2. Gratings 3. Optical illusions 3.1. Motion aftereffects 3.2. Ambiguous picture 3.3. Illusory contours 4. Random dot stereograms 5. Rapid Serial Visual Presentation (RSVP) 6. Phantom eye syndrome 6. Eye movements 1. Saccades and Microsaccades 2. Vestibulo-Ocular Reflex (VOR) and Pursuit movement 7. General concepts 1. Adaptation 2. Aftereffects 3. Contrast sensitivity 4. Saccadic suppression 5. Temporal resolution 5.1. Change blindness 5.2. Flicker fusion 6. Visual acuity 8. Perception 1. Color perception 1.1. Scotopic vision 1.2. Unique hues 2. Depth perception 2.1. Accommodation 2.2. Binocularity 2.3. Color depth 2.4. Convergence 2.5. Distance fog 2.6. Horopter 2.7. Occlusion 2.8. Parallax 2.9. Peripheral vision 2.10. Retinal disparity/Stereopsis 3. Face perception 4. Motion perception 4.1. Optical flow 4.2. Structure from motion 4.3. Time to contact(see depth from motion) 4.4. Vection 5. Multimodal perception 5.1. Synesthesia 6. Object Perception 6.1. Object recognition in cognitive neuroscience 7. Space perception 8. Texture perception 9. Perceptual organization 1. Figure–ground determination 2. Gestalt laws of grouping 10. Psychophysical methods 1. Discrimination 2. Forced choice methods 3. Just-noticeable difference 4. Method of adjustment 5. Method of constant stimuli 6. Method of limits 7. Staircase procedures 8. Thresholds 11. Theoretical perspectives 1. Affordances 2. Gestalt theory 3. Gibson's theory 4. Marr's theory 5. Unconscious inference 12. Visual search 13. Visual short term memory Motion and time sequence analysis related concepts 1. Active vision 1. Kinetic depth 2. Image stabilization 3. Surface reconstruction 4. Time to contact 5. Visual servoing 2. Appearance change analysis 3. Change and moving object detection 1. Background modelling 1.1. Finger tracking 1.2. ViBE algorithm 2. Change detection in compressed image/video data 3. Change detection in non-standard images: Panoramic, Omnidirectional 4. Detection with a changing background 5. Foreground modelling 6. Image differencing 7. Moving camera change detection 8. High-speed cameras 9. Shadow removal, Moving shadow detection 9.1. The Stauffer and Grimson algorithm 4. Depth/Range image temporal sequence analysis 5. Image sequence fusion 1. Image mosaics 2. Image stabilization 3. Super-resolution 6. Motion field 1. Depth estimation 2. Edge/Discontinuity detection 3. Hierarchical motion field estimation 4. Region segmentation/decomposition 4.1. Motion layer/Multiple motion segmentation 5. Particle image velocimetry 7. Motion property estimation 1. General motion estimation 2. Observer motion, Egomotion estimation 3. Periodicity estimation 4. Planar motion estimation 5. Instance centre of rotation 6. Pure translation 7. Linear motion estimation 8. Non-rigid motion analysis 1. Human pose and motion estimation 9. Optical flow 1. Affine flow 2. The aperture problem 3. Methods for determining optical flow 3.1. Area based methods 3.2. Binocular methods 3.3. Contour based methods 3.4. Correlation based optical flow estimation 3.5. Gradient based optical flow estimation 3.6. Feature based optical flow estimation 4. Optical flow boundary, discontinuity estimation 5. Color optical flow 6. Optical flow field calculation 7. Optical flow histogram 8. Information extraction 8.1. Egomotion estimation 8.2. Epipole location 8.3. Focus of expansion 8.4. Obstacle detection 8.4.1. Parking sensors 9. Multigrid methods 10. Normal flow 11. Optical flow constraint equation 12. Optical flow smoothness constraint 13. Range flow 14. Scene flow/Surface motion 15. Structure from optical flow 10. Sensor, Camera Motion estimation 11. Spatio-Temporal reasoning 1. Epipolar plane analysis 2. Spatio-temporal corner/Interest point detector 3. Spatio-temporal filters 3.1. Singular spectrum analysis 4. Representations 4.1. Small motion models 5. Temporal shape matching 5.1. Spatio-Temporal Relationship Match (SRM) 5.2. Temporal geometric shape model matching 5.3. Temporal property model matching 6. Temporal spatio-velocity transform 12. Structure from motion/Structure and motion 1. Articulated object segmentation 2. Classical structure from motion 3. Model-based (facial) motion capture/Model-based shape capture 4. Multi-frame structure estimation 4.1. Critical motions 4.2. Initialization 4.3. Euclidean reconstruction 5. Nonlinear recursive methods 6. Rigid bodies 6.1. Rigid body segmentation 7. Rigidity constraint 7.1. Kruppa equations 8. Structure consistency constraint 9. Structure factorization 9.1. Tomasi–Kanade factorization 9.2. Factorization with uncertainty 10. Temporal factorization 13. Temporal event analysis 1. Activity analysis 1.1. Temporal action segmentation 1.2. Instantaneous activity recognition 1.3. Long term activity 1.3.1. Hidden Markov Model matching 1.3.2. Hidden Semi-Markov Model matching 1.3.3. Rule-based/Syntactic model matching 1.3.4. Trajectory model matching 2. Novelty detection 3. Self-similarity matrix 14. Tracking 1. Articulated object tracking 2. Binocular tracking 3. Discontinuous events tracking 4. Feature tracking approaches 4.1. Contour tracking, Active contour tracking 4.2. Appearance-based tracking 4.3. Edge tracking 4.4. Fiduciary marker tracking 4.5. Optical flow-based target tracking 4.6. Pose based tracking 4.7. Feature-based tracking 4.8. Template-based tracking 4.9. Temporal stereo tracking 4.10. Texture-based tracking 5. Moving camera based tracking 6. Multiple target tracking 6.1. Feature-based tracking 6.2. Optical flow analysis 6.3. Template-based tracking 7. Tracking information fusion 7.1. Condensation/Particle filter tracking 7.2. Tracking using Bayesian belief propagation 7.3. Tracking using hidden markov models 7.4. Tracking using Kalman filters 8. Vergence maintenance Non-sequential realization methods 1. Systolic arrays 1. Digital signal processing/Digital signal processor 2. Artificial neural networks 3. Hopfield networks 4. Kohonen networks 5. Perceptron networks 6. Radial basis function networks 7. Support vector machines 2. Parallel processing approaches 1. Multiple Instruction Multiple Data (MIMD)/Multiprocessing 1.1. Data parallelism/Domain decomposition methods 2. Single Instruction Multiple Data (SIMD) 2.1. Massively parallel systems 3. Vector processors 3. Programmable logic approaches 1. Reconfigurable computing 4. VLSI approaches 1. Vision chips Object, world and scene representations

1. Full object representations 1. Flat 2. Hierarchical, by parts, structural decomposition, subcomponent representation 3. 3D object representations 2. Functional representations 3. Geometric representation of model features 1. CAD representations 2. Curve representations 2.1. Chain codes 2.2. Circles 2.3. Conics 2.4. Methods of contour integration 2.5. Crack codes 2.6. Curvature primal sketch 2.7. Curvature scale space 2.8. Cross section functions 2.9. Edgelet, Contourlet 2.10. Ellipse representations 2.11. Intrinsic equations 2.12. Line representations 2.13. Line moments 2.14. Phi-S curves, tangent angle functions 2.15. Polyline, Polycurves, Polygonal approximations 2.16. Radius vector functions 2.17. Signatures 2.18. Active contour models/Snakes 2.18.1. Gradient Flow Vector (GFV) snakes 2.19. Spline Representation 2.20. Superellipse representations 2.21. Supershapes 2.22. Support functions 2.23. Torsion of a curve 2.24. Torsion scale space 2.25. Wavelet descriptors 2.26. Width functions 3. Geometric representation of model features/Points 3.1. Symplectic geometry 3.2. Local scale descriptions 3.3. Point distribution models 3.4. Non-linear point distributions 3.5. Surflet 4. Region representations 4.1. Convex hull 4.2. Region cross section functions 4.3. Delaunay triangulation/Voronoi diagrams 4.4. Grey-level distribution models 4.5. 4.6. Occupancy grids 4.7. Polygon mesh 4.8. Trees 4.8.1. Octrees 4.8.2. Quadtrees 5. Surface representations 5.1. Algebraic point set surfaces 5.2. Conformal mapping 5.3. Local point/patch representations 5.4. Mean and Gaussian curvature 5.5. Rubbersheeting 5.6. Thin plate splines 5.7. Algebraic topology 5.8. Planar patches/faces, Edges, Vertices, Boundary representations 5.9. Principal curvature sign classes 5.10. Bézier surface 5.11. Sparse grid 5.12. Spherical images 5.13. Subdivision surface 5.14. B-spline 5.15. Surface triangulation, Surface meshes 6. Object centered representations 6.1. Generalized cones 6.2. Geon structural description 7. Objects/Volume representations 7.1. 3D skeletons 7.2. Aspect graph matching 7.3. Balloons 7.4. Constructive solid geometry 7.5. Set-theoretic modeling 7.6. Spheres 7.6.1. Surfaces of constant Gaussian curvature 7.6.2. Schwarzschild coordinates 7.7. Extended Gaussian Images

7.8. Generalized cylinders 7.9. Cones 7.10. Medial surfaces 7.11. Quadrics 7.12. Hyperquadrics 7.13. Superquadrics 7.14. Shape histogram 7.14.1. Local Energy-based Shape Histogram (LESH)

7.15. Spin images 7.15.1. Spherical spin images 7.16. Spherical harmonics 7.17. Supershapes 7.18. Boundary representation 7.19. Tetrahedral representations 7.20. Volumetric frequency 7.21. Voxels, Octrees 7.22. Wire-frame representations 4. Logical and symbolic representations 1. Frames 2. Knowledge representation 2.1. Description logic 2.2. KRL 3. Predicate calculus 3.1. Predicate logic 3.2. First-order logic 4. Relational model 5. Semantic nets 5. Multi-scale representation approaches 1. Fractals 2. Scale space 3. Wavelets 6. Non-rigid model representations 1. Active appearance models 2. Active shape models 3. Implicit shape models 4. Point distribution models 5. Deformable shapes 5.1. Active contour models 5.2. Deformable surfaces 5.3. Deformable volumes 6. Structural rigidity 7. Time-varying meshes 7. Non-symbolic representations 1. Eigenspace representations 2. Interest points 3. Intrinsic images 4. Light fields, Image-based modeling and rendering 8. Procedural representations 1. Production rule representations 2. Visual routines 9. Shape classes/Shape families 10. Temporal representations 1. Short-term activity representations 1.1. Motion history/Energy models 1.2. Volume motion templates 2. Long-term activity representations 2.1. Global representations 2.1.1. Hidden Markov models/Finite state models 2.1.2. Target trajectory models 11. Types of models 1. Active appearance models 2. Color appearance models 3. Geometric models 4. Graph models 4.1. Exponential random graph models 5. Relational models 12. Viewer centered representations, Viewpoint-dependent representations 1. Iconic image models 2. Aspects/characteristic views

3. Tesselated viewsphere approximations 3.1. Geodesic domes Recognition and registration methods 1. Statistical classification methods 1. Bayesian classifier 2. Contextual image classification 3. Decision trees 4. Learning classifier systems 5. Feature-based 6. Fuzzy classification 7. Hough forest 8. k-nearest neighbor classification 9. Linear and higher order discriminant functions 10. Markov random field based classification 11. Minimum distance estimation 12. Multi-classifier fusion 13. Neural networks 14. Sparse coding 15. Vector quantization based classification 2. General reasoning methods used in vision 1. Ambiguous images 3. Geometric model matching, Feature correspondence, Shape correspondence 1. General matching control and search algorithms 1.1. Hypothesize and test 1.2. Heuristic search 1.3. Recognition by components 1.4. Interpretation trees and other search tree variations 1.5. Rule-based systems 1.6. Theorem proving 1.7. RAST algorithms 2. General recognition methods 2.1. Appearance-based, Iconic, View-based recognition 2.2. Boltzmann machine, Hopfield net, Simulated annealing 2.3. Constraint-based matching 2.4. Context-based matching 2.5. Template matching, Elastic matching, Deformable template 2.6. Geometric hashing 2.7. Image stitching 2.8. Image registration 2.9. Inverse compositional method 2.10. Iterated closest point/Iterative closest point 2.11. MAPSAC 2.12. Model based recognition 2.13. Multi-scale contour segmentation 2.14. Non-rigid alignment 2.15. Object categorization 2.15.1. Cognitive neuroscience of visual object recognition 2.15.2. Bag of words 2.15.3. Structured models 2.15.4. Subcategory recognition 2.15.5. Vocabulary trees 2.16. Object Size/Scale/distance estimation 2.17. Polygon matching 2.18. Pose clustering 2.19. RANSAC 2.20. Relaxation labeling 2.21. Softassign algorithm 2.22. Structural description 2.23. Template and cross-correlation matching 4. Identity verification/alignment 1. Geometric feature proximity 2. Pose consistency 5. Model based indexing, invocation 6. Special feature matching 1. 2D to 2D point feature matching 1.1. Proximity matrices 2. 2D to 3D point feature matching 3. 3D to 3D point feature matching 4. 2D, 3D point to structure matching 5. Aspect graph matching 6. Boundary/contour/curve/edge matching 6.1. TERCOM 7. Line matching 8. Phase matching 9. Property-based matching 10. Optical/Appearance flow matching 11. Polygon matching 12. Needle-map matching 13. Region matching 14. Spatial pyramid matching 15. Surface matching 16. Texture classification 17. Texture matching 18. Volume matching 7. Syntactic pattern matching 1. 1D/String matching 2. 2D/Pattern grammars Scene understanding/image analysis methods 1. Situated cognition 2. Affordance 3. Appearance prediction 4. Figure-ground separation 5. High-level vision 6. Light source detection 7. Line labeling 8. Occlusion understanding and recovery 9. Perceptual organization, Perceptual grouping 1. Hierarchical organization 2. Subcomponent detection 10. Types of scenes 1. Manhattan world scenes 11. Region labeling 12. Scene completion 13. Shadow understanding Sensor fusion, registration and planning methods 1. Sensor fusion and registration types 1. CT scan, MRI, fMRI, NMR, PET scan 2. Structure from motion 3. Kinetic depth 4. Range and intensity 4.1. High dynamic range imaging 5. SAR, Digital maps 6. Visible and infrared 6.1. Visible spectrum 6.2. Infrared spectrum 6.3. MVIRI 2. Information fusion 3. Multi-image intensity image registration 4. Multi-view range data registration and fusion 5. Next view planning/prediction 6. Sensor networks 1. Sensor networks calibration 2. Distributed target tracking & fusion 7. Sensor path planning 8. Simultaneous Localization And Mapping (SLAM) 9. Static sensor placement/parameter determination 1. Multidimensional sampling 10. Fusion Using Kalman Filters System models, calibration and parameter estimation methods 1. Camera calibration 1. Camera pose estimation 1.1. Camera auto-calibration, Camera self calibration, Closure phase 1.1.1. Critical motions, relations, scenes 1.1.2. Zoom lens calibration 1.2. Camera calibration using calibration targets 2. Monocular camera calibration 3. Camera resectioning 2. Eye–hand coordination and calibration 3. Illumination field calibration 4. Image distortion, models and correction 1. Chromatic aberration 2. Defocus aberration 3. Diffraction/Interference fringes 4. Ringing artifacts 5. Fisheye lens 6. Radial lens distortion 7. Underwater lens calibration 5. Pinhole camera, intrinsic and extrinsic camera models 6. Radiometric calibration 7. Structured light source calibration Visual learning related methods and concepts 1. Observational learning 1. Discrete observational learning 2. Probabilistic observational learning 2. Geometric feature learning 3. Joint natural language and image data learning 1. NLP learning techniques 4. Learning technologies 1. Bayesian learning/Probabilistic model learning 1.1. Bayesian principal component analysis 1.2. Latent variable learning 1.3. Variational Bayesian methods 2. Clustering 2.1. Fuzzy clustering 2.2. Clustering coefficient 2.3. Hierarchical clustering 2.4. k-means clustering 2.4.1. Hierarchical k-means clustering 2.5. Mean-shift clustering 2.6. Neural gas clustering 2.7. Parametric/Non-parametric clustering 2.8. Pattern matrices 2.9. Proximity matrices 2.10. Self-organizing feature maps/Kohonen maps 2.11. Superparamagnetism clustering 3. Gaussian mixture models, Expectation-Maximization (EM) 4. Ensemble learning 4.1. Bootstrap aggregating 4.2. Boosting 4.2.1. AdaBoost 4.2.2. DenseBoost 4.2.3. TextonBoost 4.3. Extremely random trees (Extra-trees) 4.4. Random forests 4.5. Vector boosting 5. Feature selection 6. Gaussian process learning and classification 7. Genetic programming/Genetic algorithms 8. Neural networks 9. Principal component analysis 10. Support vector machines 10.1. Kernel methods 10.2. Kernel trick 10.3. Structured SVM 10.4. Relevance vector machine 11. Semi-supervised learning 12. Vector quantization 5. Shape model learning 1. Range data fusion 2. Space carving 3. Structured learning 3.1. Architectural models 4. Volumetric model recovery 5. Voxel coloring 5.1. Marching cubes 6. Property learning 1. Spatio-temporal patterns

Calendar of Computer Image Analysis, Computer Vision Conferences Organization and Updates of the Conference Entries File Last Updated: 01/10/2014 00:24:41

Copyright © © 2013 Jump to the Current Week All underlines are links. The short name is linked to the conference web site, which should have the most up to date information for that meeting. The Call for Papers entry is usually linked to a local copy of the text based version of the call for papers. Conference information is reliable to the extent it is provided. To add or update information send mail to: [email protected]. or for limited information only use: The multistep Comments Form Home Page.

Search the conference listings by conference name, topic, or other keyword.

Schedule for current conference deadlines from approximately the current date through the next 3 months. Full Conference Calendar by Year. You can sort by Date, Due Date, Name. Direct links to the next meeting of regular major conferences: [ICCV | CVPR | ECCV | ACCV | ICIP | ICPR | WVM (WACV) | The current Week] Google KMZ file for historic conference locations -- Including ICCV, CVPR, ICPR, ICIP, ACCV, BMVC, CRV, etc.

2014 Calendar 2015 Calendar 2016Calendar Top 7 Deadlines Special Issues

2013 Full List 2014 Full List 2015 Full List 90 Day Deadlines Archives to 1994

The complete Computer Vision Conference RSS feed. or 3 subsets: Current Meetings. Deadlines. Changes. The Conference Update Blog contains occasional (i.e. not daily) comments on additions, the recent updates, and changes to both the Bibliography and Conference pages. It will include other things I find of interest. The direct link above is only for the category or Conferences, the other categories are also available once you are there. Updates for roughly the last 60 days If no comment, then a new entry, For a complete update list see the most recent year's archive

Thu Jan 9 no more updates until February Due to travel

Video Networks Extension CVIU Thu Jan 9 IV Extension VIEW ICCV 2017 Location Special

Sat Dec Mobile Vision Sensor Special Issue SISE 28 Fri Dec CIARP EMRICMR Workshop MIUA ICCVIA 20

Mon Dec IR2S ICMR Workshop 9

Sat Dec 7 Image CLEF, Life CLEF MVML MHCI

Mon Dec CV Summer School 2

Medical Color Mon Dec ICPRAM Doctoral THUMOS Info, ICCV Workshop Imaging Summer Imaging ICISP 2 Consortium School Session

Mon Nov BMVA Vision Summer School 25

Sat Nov PCV ECCV Workshop ICCP 23

The summary calender does not list all conferences. Workshops associated with conferences and other less related meetings, are listed in the full list below. Search the conference listings by conference name or topic. Old entries moved to the Archive monthly.

2014 (Partial) Monthly Conference Calendar P indicates paper due date. Check the full listing for other meetings, workshops assoicated with major conferences, and special issues.

Due Januar Februar Marc Apri Ma Jun Jul Augus Septembe Octobe Novembe Decembe 2014 201 y y h l y e y t r r r r 3

P: VISAPP Sep 5-8 ______18

P: MMM Aug 8-10 ______9

Photonics P: _ 1-6 ______Jul 22

P: MMEDIA Sep _ 23-27 ______28

P: ICPRAM Oct _ _ 6-8 ______8

P: MMSys Sep _ _ 19-21 ______27

P: WACV Sep _ _ 24-26 ______2

P: ICMR Dec _ _ _ 1-4 ______2

P: SSIAI Dec _ _ _ 6-8 ______16

CRV _ P: 3 _ _ 7-9 ______

14- Geospatial P: 12 ______16

P: 21- GEOBIA Nov ______23 18

P: 28- IWCIA Nov ______30 15

8- IV _ P: 10 ______11

P: 17- CVPR Nov ______19 1

ICISP _ _ P: 8 _ _ _ 30- -2 _ _ _ _ _

MIUA _ _ P: 17 _ _ _ 9- _ _ _ _ _ 11

P: 14- ICME Dec ______18 3

16- AMDO _ _ P:21 ______18

SIGGRAP ______10-14 _ _ _ _ H

P: S_SSPR ______20-22 _ _ _ _ 15

P: ICPR Dec ______24-28 _ _ _ _ 20

GCPR _ _ _ _ P: 2 _ _ _ 1-5 _ _ _

P: GCPR ______2-5 _ _ _ 11

ECCV _ _ P: 7 _ _ _ _ _ 5-12 _ _ _

ICIP P: 31 ______27-30 _ _

P: ACCV ______1-5 _ 15

CIARP _ _ _ _ P: 2 _ _ _ _ _ 2-5 _

NIPS ______1-4

Due Januar Februar Marc Apri Ma Jun Jul Augus Septembe Octobe Novembe Decembe 2014 201 y y h l y e y t r r r r 3

Due 2015 January February March April May June July August September October November December 2014

P: 7- CVPR ______TBD 12 P: CAIP ______2-4 _ _ _ TBD

P: ICCV ______7-13 TBD

CVPR _ _ _ _ _ TBD ______

Paper Deadlines for the Major Computer Vision Meetings. See the chart above for more or the full listing for even more meetings.

What is Deadline Name Conference Date Location required

Past: November 1, 2013 Full Paper CVPR 2014 June 17-19, 2014 Columbus, Ohio

Past: December 20, Stockholm, Full Paper ICPR 2014 August 24-28, 2014 2013 Exentsion Sweden

Register September 5-12, Zurich, March 7, 2014 ECCV 2014 Abstract 2014 Switzerland

Singapore November June 15, 2014 Paper ACCV 2014 1-4, 2014

May 2, 2014 Registration BMVC 2014 September 1-5, 2014 Nottingham UK.

The deadlines below have passed. Wait until next year for these.

Past: April 12, 2013 Full Paper ICCV 2013 December 1-8, 2013 Sydney, Australia WACV 2014, Steamboat Past: September 2, 2013 WACV PETS, March 24-26, 2014 Springs, CO WRV, UCCV)

Past: September 21, 2012 FG 2013 April 22-26, 2013 Shanghai, China

September 15-18, Melbourne, Past: February 5, 2013 Paper ICIP 2013 2013 Australia

January 2014

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International Conference on Computer Vision Theory and Applications

VISAPP 2014 Lisbon, Portugal Conference Venue

Paper deadline: Past: September 18, January 5-8, 2014 Call for papers. 2013 Extension (2)

January 5-8, 2014 Position Paper deadline: Past: October 15, 2013 Call for Position papers.

2nd International Conference on Photonics, Optics and Laser Technology

PHOTOPTICS 2014 Lisbon, Portugal Conference Venue

January 7-9, 2014 Paper deadline: Past: July 2, 2013 Call for papers.

International Conference on MultiMedia Modeling

MMM 2014 Dublin, Ireland Guinness Storehouse

January 8-10, 2014 Paper deadline: Past: August 9, 2013 Extension Call for papers.

The Third International Video Browser Showdown Paper deadline: Past: September 16, 2013 Call for Competition (VBS21014) Demos 1st Winter School on Multimedia Processing and Registration deadline: Past: September 16, 2013 Call January 6-8, 2014 Applications (WMPA 2014) for Participation

February 2014

Photonics West

Photonics West 2014 San Francisco, CA Conference Venue

Paper deadline: Past: July 22, February 1-6, 2014 Call for papers. 2013

Annual Interdisciplinary Conference

AIC 2014 Jackson Hole, Wyoming Teton Village

Paper deadline: Past: September February 2-7, 2014 Call for papers. 20, 2013

EuroCOW the Calibration and Orientation Workshop

EuroCOW 2014 Barcelona, Spain Castelldefels

Paper deadline: Past: July 24, February 12-14, 2013 Call for papers. 2013

The Sixth International Conferences on Advances in Multimedia

MMEDIA 2014 Nice, France Conference Venue

Paper deadline: Past: September February 23-27, 2014 Call for papers. 28, 2013 March 2014

International Conference on Signal and Imaging Systems Engineering

SISE 2014 Coimbatore, India Conference Venue

March 2, 2014 Paper deadline: February 15, 2014 Call for papers.

7 th International Conference on Bio-inspired Systems and Signal Processing BIOSIGNALS 2014 Angers, Loire Valley, France ESEO

Paper deadline: Past: September March 3-6, 2014 Call for papers. 19, 2013

3rd International Conference on Pattern Recognition Applications and Methods ICPRAM 2014 Angers, Loire Valley, France ESEO

Paper deadline: Past: October 8, March 6-8, 2014 Call for papers. 2013 Extension

Paper deadline: Past: November March 6-8, 2014 Call for Position Papers. 19, 2013 Extension

Doctoral Consortium Paper March 6-8, 2014 deadline: Past: December 10, 2013 Call for Participation.

Workshop on Image Processing

WIP 2014 Part of International Conference on Havana, Cuba Operations Research

Paper deadline: Past: January 10, March 11-14, 2014 Call for papers. (PDF) 2014 ACM Multimedia Systems Conference

MMSys 2014 Singapore Conference Venue

Paper deadline: Past: September March 19-21, 2014 Call for papers. 27, 2013

Paper deadline: Past: November March 19-21, 2014 Dataset Track. 11, 2013

NOSSDAV 2014: ACM Workshop on Network and Operating Paper deadline: Past: December March 19, 2014 Systems Support for Digital Audio 13, 2013 Call for Papers and Video

IEEE Winter Application and Computer Vision Conference

WACV 2014 Steamboat Springs, CO Conference Venue

Paper deadline: Past: December 10, 2013 Second Round March 24-26, 2014 Announcement. Submissions. First Round: Paper deadline: Past: September 2, 2013

International Conference on Computer Vision and Image Analysis applications ICCVIA 2014 Ras Al Khaimah, UAE Conference Venue

Paper deadline: Past: January 21, March 25-27, 2014 Call for papers. 2014

April 2014

ACM International Conference on Multimedia Retrieval

Glasgow, Scotland, UK Conference Venue ICMR 2014 Paper deadline: Past: December 2, April 1-5, 2014 Call for papers. 2013

April 1-4, 2014 Doctoral Symposium Deadline: Past: December 14, 2013

1st International Workshop on Paper deadline: Soon: February 1, April 1, 2014 Image Retrieval in Remote 2014 Call for Papers Sensing (IR2S 2014)

International Workshop on Paper deadline: Soon: February 1, April 1, 2014 Environmental Multimedia 2014 Call for Papers Retrieval 2014 (EMR 2014)

April 1 or 5, 2014 Workshop Proposals Deadline: Past: November 15, 2013

Site Proposal Deadline: Past: Call for Sites. September 21, 2012

Southwest Symposium on Image Analysis and Interpretation

SSIAI 2014 San Diego, California, USA Conference Venue

Paper deadline: Past: December April 6-8, 2014 Call for papers. 16, 2013

EvoApplications 2014 track on Evolutionary Computation in Image Analysis, Signal Processing and Pattern Recognition EvoIASP 2014 Granada, Spain Part of the EvoStar conference

Paper deadline: Past: November April 23-25, 2014 Call for papers. 11, 2013

May 2014

Show this month on a map The Fifth IEEE International Conference on Computational Photography

ICCP 2014 Santa Clara, CA, USA Conference Venue

Paper deadline: Past: December May 2-4, 2014 Call for papers. 13, 2013

11th Canadian Conference on Computer and Robot Vision

CRV 2014 Montreal, PQ, Canada Conference Venue

Paper deadline: Soon: February May 7-9, 2014 Call for papers. 3, 2014

Google Earth KMZ file for CRV Locations

ISPRS Symposium on Geospatial databases and location based services Geospatial 2014 Suzhou, China Conference Venue

Paper deadline: Past: January 12, May 14-16, 2014 Call for papers. 2014

Florida Artificial Intelligence Research Society Conference, FLAIRS 27

FLAIRS 2014 Pensacola Beach, Florida Conference Venue

Paper deadline: Past: November May 21-23, 2014 Call for papers. 18, 2013

5th International Conference on Geographic Object-Based Image Analysis GEOBIA 2014 Thessaloniki, Greece Makedonia Conference Hotel

Paper deadline: Past: November May 21-23, 2014 Call for papers. 18, 2013

16th The International Workshop on Combinatorial Image Analysis

IWCIA 2014 Brno, Czech Republic Brno University of Technology Paper deadline: Past: November May 28-30, 2014 Call for papers. 15, 2013

Spring Conference on Computer Graphics

SCCG 2014 Smolenice Castle, Slovakia Conference Venue

May 28-30, 2014 Paper deadline: March 2, 2014 Call for papers.

June 2014

22nd International conference on Computer Graphics, Visualization and Computer Vision WSCG 2014 Plzen (close to Prague), Czech Conference Venue Republic

June 2-5, 2014 Paper deadline: March 5, 2014 Call for papers.

2014 IEEE Intelligent Vehicles Symposium

IV 2014 Ypsilanti, Michigan, USA Conference Venue

Paper deadline: Past: January 24, June 8-11, 2014 Call for papers. 2014 Extension

IEEE Conference on Computer Vision and Pattern Recognition

CVPR 2014 Greater Columbus Convention Columbus, Ohio Center

Paper deadline: Past: November 1, Call for papers Additional June 17-19, 2014 2013 Information. Call for Proposals.

Sponsored by IEEE-CS TC PAMI. Google Earth KMZ file for CVPR Locations

12th International Workshop on Content-Based Multimedia Indexing

CBMI 2014 Klagenfurt, Austria Alpen-Adria Universität Klagenfurt

June 18-20, 2014 Paper deadline: February 16, 2014 Call for papers.

Sixth International Conference on Image and Signal Processing

ICISP 2014 Cherbourg, Normandy, France Conference Venue

Paper deadline: Soon: February June 30-July 2, 2014 Call for papers. 8, 2014

Special Session: Color Imaging Paper deadline: Soon: February 8, June 30-July 2, 2014 and Applications 2014 Call for Papers

BMVA Computer Vision Summer School 2014

BMVA Vision 2014 Swansea University, UK Conference Venue

June 30-July 4, 2014 Registration deadline: May 5, 2014 Call for participation.

July 2014

The 10th International Conference on Intelligent Environments

IE 2014 Shanghai, China Conference Venue

Paper deadline: Soon: January July 2-4, 2014 Call for papers. 31, 2014 Medical Image Understanding and Analysis

MIUA 2014 London, UK Conference Venue

July 9-11, 2014 Paper deadline: March 17, 2014 Call for papers.

Vision for Language and Manipulation

Vision Language 2014 London, UK 5 Southampton Street

July 11, 2014 Paper deadline: April 3, 2014 Call for papers.

International Computer Vision Summer School: From Fundamentals to Applications ICVSS 2014 Punta Sampieri, Sicily, Italy Hotel Village Baia Samuele

Enrollment deadline: March 31, July 13-19, 2014 Call for participation. 2014.

IEEE International Conference on Multimedia and Expo

ICME 2014 Chengdu, China Conference Venue

Paper deadline: Past: December 3, July 14-18, 2014 Call for papers. 2013 Full paper 6 days later

VIII Conf. on Articulated Motion and Deformable Objects

AMDO 2014 Palma de Mallorca, Spain Universitat de les Illes Balears

July 16-18, 2014 Paper deadline: March 21, 2014 Call for papers.

Medical Imaging Summer School: Medical Imaging meets Computer Vision MISS 2014 Favignana, Sicily, Italy Conference Venue

Application deadline: March 31, July 28-August 1, 2014 Call for Participation. 2014 August 2014

SIGGRAPH 2014

SIGGRAPH 2014 Vancouver, BC Conference Venue

August 10-14, 2014 Paper deadline: Call for papers.

International Conference on Machine Vision and Machine Learning

MVML 2014 Prague, Czech Republic Conference Venue

August 14-15, 2014 Paper deadline: March 15, 2014 Call for papers.

International Conference on Multimedia and Human-Computer Interaction MHCI 2014 Prague, Czech Republic Conference Venue

August 14-15, 2014 Paper deadline: March 15, 2014 Call for papers.

Statistical+Structural and Syntactic Pattern Recognition Workshop

S+SSPR 2014 Stockholm, Sweden Before ICPR

Site Proposal deadline: Past: June August 20-22, 2014 Call for Proposals. 15, 2012

International Conference on Pattern Recognition

ICPR 2014 Stockholm, Sweden Conference Venue

Paper deadline: Past: December August 24-28, 2014 Call for papers. 20, 2013 Visual observation and analysis of Paper deadline: May 1, 2014 Call August 24, 2014 animal and insect behavior (VAIB) for Papers

AMMDS: Activity Monitoring by August 24, 2014 Paper deadline: April 14, 2014 multiple distributed sensing

Google Earth KMZ file for ICPR Locations

11th International Conference on Signal Processing and Multimedia Applications SIGMAP 2014 Vienna, Austria Part of ICETE

August 28-30, 2014 Paper deadline: April 15, 2014 Call for papers.

September 2014

British Machine Vision Conference

BMVC 2014 Nottingham, England University of Nottingham

September 1-5, 2014 Paper deadline: May 2, 2014 Call for papers.

The 22nd European Signal Processing Conference

EUSIPCO 2014 Lisbon, Portugal Conference Venue

September 1-5, 2014 Paper deadline: February 17, 2014 Call for papers.

German Conference on Pattern Recognition

GCPR 2014 Münster, Germany Formerly the DAGM symposium.

September 2-5, 2014 Paper deadline: May 11, 2014 Call for papers. (PDF) European Conference on Computer Vision

ECCV 2014 Zurich, Switzerland Conference Venue

September 6-12, 2014 Paper deadline: March 7, 2014 Call for papers.

ISPRS Technical Commission III Paper deadline: April 13, 2014 Call September 5-7, 2014 Symposium Photogrammetric for papers. Computer Vision (PCV 2014)

Conference and Labs of the Evaluation Forum

CLEF 2014 Information School, University of Sheffield, UK Sheffield

September 15-18, 2014 Paper deadline: Call for papers.

Result deadline: May 1, 2014 September 15-18, 2014 Image CLEF Variable

Result deadline: May 1, 2014 September 15-18, 2014 Life CLEF Variable Information.

Artificial Intelligence Applications and Innovations

AIAI 2014 Rhodes, Greece Conference Venue

September 19-22, 2014 Paper deadline: April 22, 2014 Call for papers.

October 2014

15th International Computer Graphics Conference

VIEW 2014 Turin, Italy TorinoIncontra

October 14-17, 2014 Paper deadline: July 31, 2014 Call for papers. IEEE International Conference on Image Processing

ICIP 2014 Paris, France CNIT La Defense

Paper deadline: Soon: January October 27-30, 2014 Call for papers. 31, 2014

Google Earth KMZ file for ICIP Locations

November 2014

12th Asian Conference on Computer Vision

ACCV 2014 Singapore Conference Venue

November 1-5, 2014 Paper deadline: June 15, 2014 Call for papers.

Iberomerican Conference on Pattern Recognition

CIARP 2014 Port Vallarta, Guadalajara, Jalisco, Conference Venue México

November 2-5, 2014 Paper deadline: May 2, 2014 Call for papers.

December 2014

Neural Information Processing Systems NIPS 2014 Lake Tahoe, NV Conference Venue

December 1-4, 2014 Paper deadline: Call for papers.

Sponsored by IEEE-CS TC PAMI.

Google Earth KMZ file for CVPR Locations

International Conference on Comuter Vision

ICCV 2017 Venice Conference Venue

Date TBD Paper deadline: Call for papers.

Google Earth KMZ file for ICCV Locations

Other Calls for Papers

CVIU: Image Understanding for Real-world Distributed Video Networks

Special Issue Paper deadline: Past: January 20, Publication: Q4, 2014 2014 Extension

Call for papers (PDF). IEEE Sensor Journal: Distributed Smart Sensing for Mobile Vision

Special Issue Publication: Q4 2014 Paper deadline: March 1, 2014

Call for papers.

Conference Information Archives Archives

Maintained by Keith Price, [email protected]. To list an appropriate conference: email a text version of the summary information. A text version of the call for papers is also useful. Search the complete conference listings. List near-term deadlines. Browse summary listing. Home Page.Index for This Year (Past meetings)

This page has lots of images; a text-only page is also available.

See Vision 1's commercial resource listing for applications groups.

 A. B. Kogan Research Institute for Neurocybernetics - Lab for Neural Network Modeling in Vision Research  ANU Biorobotic Vision group

 ARTEMIS Project Unit Advanced research on multidimensional imaging systems : 3D/2D vision medical imaging telecommunications and multimedia

 Aachen University of Technology - Department of Technical Computer Science Specializes in human media technology and in knowledge based and trainable systems (computer vision and computational intelligence)

 Aachen University of Technology - Language Processing and Pattern Recognition (Computer Science VI) The object recognition group specializes in statistical image object recognition.

 Aalborg University - Computer Vision & Media Technology Laboratory

 Academia Sinica - Laboratories of Intelligent Systems

 Adelaide University - Computer Vision Lab Primarily researching (1) structure from motion and related geometric problems in computer vision, and (2) video surveillance and analysis. (See publications.)

 Amerinex Applied Imaging Inc.

 Aristotle University of Thessaloniki Computer Vision and Image Processing group

 Auckland U, Tamaki Campus, Computer Vision Unit at Tamaki

 Belarusian Academy of Sciences - Laboratory of Image Processing and Recognition  Berlin Technical University Computer Vision group

 Bilkent University - RETINA Vision and Learning Group

 Boston University Image and Video Computing Research group

 Brown Universtity - SHAPE Lab Shape representation, 3D object and scene reconstructions, Object recognition, Computer vision for Architecture, Archaeology, CAD, and beyond.

 Computational Interaction and Robotics Lab Our group is interested in understanding the problems that involve dynamic, spatial interaction at the intersection of vision, robotics, and human-computer interaction.

 CREATIS - Center for Research and Applications in Image and Signal Processing

 CRIN / INRIA Lorraine Image Synthesis and Analysis group

 CSSIP - Visual Processing Research Group

 Computer Vision and Imaging Group Model-based human tracking, robust methods and medical image understanding.

 Caltech Vision group

 Cambridge University Speech, Vision and Robotics group

 Cankaya University - Pattern Recognition and Image Processing Lab

 Cardiff University - Vision and Geometry Research Group Specialises in machine vision, automated inspection, 3d vision, geometric computing, medical imaging, and computer graphics  Carnegie Mellon Digital Mapping Lab

 Carnegie Mellon University / University of Karlsruhe - Interactive Systems Lab Specializes in multimodal human computer interaction, real-time face tracking, eye/gaze tracking, lipreading

 Carnegie Mellon Vision and Autonomous Systems Center

 Center for Applied Vision and Imaging Sciences

 Center for Biological and Computational Learning at MIT

 Technical University of Cluj-Napoca Image Processing and Pattern Recognition Group Our main activities are research and teaching in the fields of image processing, pattern recognition, computer vision, hardware design for image acquisition and processing.

 Colorado State University Computer Vision group

 Columbia University - Robotics Group Group

 Columbia University Automated Vision Environment, CAVE

 Computer Vision Group at HCMUNS

 IT University of Copenhagen - Image Group The Image Analysis (IA) group performs research within the foundation of image and shape analysis and primarily medical image analysis applications.

 Cornell Vision Group

 Curtin University AI and Computer Vision

 Cyclops Project - Research on Computer Vision Applications in Medicine The Cyclops Project is a German/Brazilian project aimed at the development of an intelligent envoironment for the support of diagnopsis-oriented medical image analysis tasks. The Project is supported by the German-Brazilian Cooperation Programme on Information Technology.

 Czech Technical University, Prague - Center for Machine Perception  DKFZ Heidelberg - Medical and Biological Informatics

 DLR - The Institute of Robotics and Mechatronics - Vision Group

 Projects include video OCR, handwriting recognition, face analysis

 Delft University Pattern Recognition Group

 Artificial Vision, Robotics and Intelligent Systems Group The main scope of the group is to perform and promote research in application problems that rise in the science of electrical and computer engineering, as well as in the production engineering one. Such applications are robotics, image processing, analysis and understanding, digital arts, database image retrieval, quality control, visual surveillance and intelligent sensory networks. The tools that the group uses to expand the front of the science and the corresponding research areas of interest are: Artificial Vision (including Machine Vision, Cognitive Vision and Robot Vision) Intelligent Systems (such as Fuzzy Systems and Artificial Neural Network) Sensor Data Fusion Pattern Recognition

 Dublin City University - Machine Vision Group Specializes in real time hand gesture recognition, pattern analysis and recognition, and vision-based systems.

 Dundee University - Computer Vision Group Research topics include human tracking, gesture recognition, monitoring for independent living, vision-based interfaces, medical image analysis and medical imaging

 ECRC User Interaction and Visualisation Group

 IBM Research - Exploratory Computer Vision Group

 EPFL - Computer Vision Laboratory We focus on modeling people and their motion from images and video sequences.

 ETH Zürich Image Science group

 ETH Zurich - Perceptual Computing and Computer Vision Group

 EUTIST Integrated Machine Vision Cluster EUTIST-IMV is a European Commission supported initiative to help companies to innovate and improve their businesses by using machine vision technology. The website introduces the on-going projects and gives practical examples of machine vision solutions for different industries and applications.

 Environmental Research Institute of Michigan (ERIM)

 Federal University of Santa Catarina - Intelligent Industrial Systems Group (Sistemas Industriais Inteligentes) Home-page of the Intelligent Industrial Systems group of the Federal University of Santa Catarina (UFSC), Brazil.

 Center for Applied Vision and Imaging Sciences The Center for Applied Vision and Imaging Sciences (CAVIS) at Florida State University is dedicated to research and education in computer vision, pattern recognition and applications.

 Foundation for Research and Technology - Hellas, Computer Vision and Robotics Lab

 Fraunhofer Institute for Computer Graphics Multimedia Systems and Image Processing dept

 French Ministry of Defense (DGA) - Geography, Imagery, and Perception Group The activities concerned are the processing and the exploitation of information, available mainly but not restrictively to images (visible, infrared, synthetic aperture radar), applied to robotics, image-based intelligence and geomatics.

 GE Research - Computer Vision Group Computer vision at GE includes basic and applied research in surveillance, aerial and broadcast video understanding; medical imaging; industrial inspection; and general image analysis.

 GET Computer Vision lab  Georgia Tech - Computational Perception Laboratory The Computational Perception Laboratory (CPL) was developed to explore and develop the next generation of intelligent machines, interfaces, and environments that can perceive, recognize, anticipate, and interact with humans.

 Graz University of Technology - Computer Graphics and Vision Group Focal points are Machine Vision, Image Analysis and Computer Graphics Applications are in areas such as machine vision in industry and medicine, 3D-modelling of objects, buildings and urban ensembles, and environmental remote sensing

 Halmstad University - Signal Analysis Group Basic research specialization: Orientation analysis, Symmetries and Tensors, local structure, texture and motion segmentation. Applied research specialization: Multimodal person authentication, face recognition, content based image retrieval, object recognition

 Hamburg University Cognitive Systems group (KOGS) (most info in German)

 Hamburg University IMA Research Group

 Harvard Robotics Lab

 Heart Institute of São Paulo Division of Informatics R&D group

 Hebrew University Computer Vision Lab

 Heriot-Watt University - Image Systems Engineering Laboratory

 Heriot-Watt University Vision and Image Processing  Honeywell - Video-Based Surveillance and Security Group This objective of the group is to invent, design, and integrate innovative video-based technologies into a distributed architecture to enhance the efficiency and capabilities of surveillance and security systems.

 Hungarian Academy of Sciences - Image and Pattern Analysis Group Textures and patterns: fundamental structural features Motion: feature-based tracking Image and video databases: retrieval Industrial inspection: shape defect detection

 Hunter College of CUNY - Computer Vision Laboratory

 Hunter College, City University of New York - Computer Vision and Graphics Laboratory

 IDIAP Computer Vision Group The computer vision group studies problems in machine visual perception, such as media annotation, people detection and human gesture tracking and recognition.

 IEN Galileo Ferraris Computer Vision lab

 IIT Delhi - Vision and Graphics Laboratory

 INRIA - Perception and Integration for Smart Spaces

 INRIA - Surgery, Informatics & Robotics (Chirurgie, Informatique & Robotique) research in the key areas of robotics surgery: modeling of deformable organs, planning and simulation of robotics procedures and safe & real-time integration.

 INRIA Rhone-Alpes - Models for Computer Vision  INRIA Vision Home Page, Sophia Antipolis center, INRIA Home Page

 INSA Lyon - Reconnaissance de Formes et Vision Pattern Recognition and Vision

 Indian Institute of Science - Computer Vision Lab

 Industrial Research Ltd Machine Vision

 Institute for Industrial Automation - Artificial Perception Group specializing in sensor integration and active perception

 Institute of Automation - National Laboratory of Pattern Recognition

 Institute of Clinical Physiology CNR - Computer Vision Group Development of new mathematical operators for the purpose of both understanding biological vision and developing real-time image- processing systems.

 Institute of Systems and Robotics - Computer Vision Laboratory

 Instituto Superior Tecnico - VisLab - Computer Vision Lab

 UCHIMURA & HU Laboratory

 Human and Computer Vision Laboratory

 Israel Computer Vision research alliance  Istituto Trentino di Cultura - Technologies of Vision Mission of TeV is to develop innovative techniques devoted to applicative topics, which currently include: Content based indexing of documents, images and videos, surveillance and biometic person identification.

 JPL Machine Vision and Tracking Sensors group

 KTH - Computational Vision and Active Perception Lab at KTH (Sweden's Royal Institute of Technology)

 Medical Image Computing Specializes in 2-D and 3-D medical image acquisition, manipulation, display, analysis, transmission, and archiving.

 Katholieke University Leuven VISICS

 Khoral Research, Inc creators of Khoros

 Kiel Cognitive Systems group

 King's College London - Image Processing Group Image processing and analysis

 Korea University - Center for Artificial Vision Research Specializes in biologically motivated computer vision

 Korean Research Groups in Visual Information Processing

 Laboratory of Motion Analysis and Virtual Reality Specialized in analysis and synthesis of human motion through image processing.

 Laval University Computer Vision and Systems Lab

 Lawrence Berkeley Lab Imaging and Distributed Computing

 Lawrence Berkeley National Laboratory - Imaging and Collaborative Computing Group Algorithms and software tools for scientific imaging applications.  Le2i - Laboratory of Electronics, Computer Science and Image Processing Webpage of the Le2i, a french research lab on computer vision and image processing

 Leeds University - Computer Vision Group Our specialities are in the areas of tracking and behaviour modelling, medical imaging, the use of colour in image coding and compression, and OMR and handwriting recognition.

 Lehigh University - Image Processing and Pattern Analysis Lab Mammogram Analysis, Image Database Retrieval, Gesture Recognition and Industrial Inspection.

 Lehigh University - Vision And Software Technology Laboratory Research includes 3D vision, real- time tracking, omni-directional processing, remote reality and teleoperation, super-resolution imaging, medical-imaging, multi-res imaging/algorithms, image-oriented user interface issues, IUE, CORBA, and DCE.

 Leiden University Imaging and Multimedia group

 Linköping University Division of Computer Vision

 Luebeck University of Medicine - Institute for Signal Processing Medical and industial image processing, pattern recognition and classification bio-signal processing,

 Lund University - Mathematical Imaging Group Computer vision, image analysis and tomography from a mathematical perspective.

 MIT - CS & AI Lab Vision Research

 MIT - Perceptual Science Group

 MIT-Media Lab Vision and Modelling Group

 McGill Centre for Intelligent Machines

 Michigan State Pattern Recognition and Image Processing lab  Microsoft Research

 Middle East Technical University - Image Processing and Pattern Recognition Group

 Middle East Technical University - Image Processing and Pattern Recognition Laboratory

 University of Minnesota Artifical Intelligence, Robotics and Vision Laboratory Specializes in human activity monitoring, intelligent transportation systems, and distributed robotics.

 NEC Computer Vision and Image Processing

 NRC (National Research Council of Canada) - Computational Video Group Stereo processing from off-the-shelf cameras, camera path computation (as seen on the logo), recognition and tracking from video, reconstruction from multiple cameras, ubiquitous video

 Nanyang Technological University - Vision and Control Research Program - vision-guided automation

 National Research Council of Canada - Visual Information Technology Group 3-D laser range sensing; geometric image processing; object and environment modeling of shape and reflectance; applications in computer graphics, manufacturing, robotics.

 National Technical University of Athens Computer Vision, Speech Communication and Signal Processing Group Multiscale image analysis, enhancement, feature extraction and object detection with algebraic, geometric and statistical methods. Analysis and modeling of shape, texture, color, and motion.  National University of Singapore - Computer Vision Research Group

 New York University - Vision Research concentration on human vision

 Niigata University - Yamamoto-Hoshino Laboratory

 North Carolina State University - Image Analysis Laboratory

 Northwestern University - Image and Video Processing Laboratory

 Norwegian University of Science and Technology - Signal Processing Group

 Notre Dame Vision-Based Robotics using Estimation

 Ohio State University - Computer Vision Laboratory Specializes in human activity analysis

 Ohio State University - Signal Analysis and Machine Perception Laboratory Perceptual organization, 3D vision, stereo.

 Ohio State University - Vision and Learning Group  Oxford University - Robotics Research Group Active Vision, Projective Geometry, Medical Image Analysis

 Oxford University - Visual Geometry Group Specializes in visual reconstruction from uncalibrated image sequences.

 Oxford University Active Vision lab, and Robotics Research group

 Parma University Computer Vision

 Penn State Computer Vision

 Politecnico di Milano - Image and Sound Processing Group

 Politecnico di Torino - Computer Graphic and Vision Group

 Postech Computer Vision Group - Pohang University of Science and Technology

 Precision Digital Images

 Purdue Robot Vision lab

 Purdue University - Video and Image Processing Laboratory (VIPER)

 Queen Mary and Westfield College Vision group  Queen's University of Belfast - Centre for Image and Vision Systems

 RADIUS - Research and Development for Image Understanding Systems

 Real-Time Vision Group at Fraunhofer IIS Real-Time Face Detection, Face Biometrics, Camera Tracking and 3D Reconstruction, Mobile Robot Vision

 Rensselaer Polytechnic Institute (RPI) Computer Science Vision Group

 Ritsumeikan University - Computer Vision Laboratory

 Rovira i Virgili University - Intelligent Robotics and Computer Vision Group Research Lines: Disassembly Planning, Multiagent Systems, Planning and Scheduling, Image Analysis and Processing, 3D Modeling, Real-Time Systems, Computer Architectures

 Rutgers University Image Understanding Lab

 SRI International 's Perception Program at its AI Center

 SUNY at Stony Brook - Computer Vision Lab

 Statistical Visual Computing Laboratory research in both fundamental and applied problems in computer vision, image processing, machine learning, and multimedia.

 Sarnoff - Vision Technology Group

 Seoul National University - 3D Visual Information Procesing Lab Computer vision research, especially in range image processing, object recognition.

 Sheffield Hallam University - Microsystems & Machine Vision Laboratory

 Sheffield Hallam University - Microsystems and Machine Vision Laboratory Microrobotic systems and real-time computer vision (Kindly remove previous link with regards to http://vision.eng.shu.ac.uk on the list)  Sheffield Hallam University Computer Vision, Pattern Recognition and Artificial Intelligence Group Research on 3D image acquisition, surface reconstruction, and image registration and fusion

 Simon Fraser University Computational Vision lab

 Smith-Kettlewell Eye Research Institute

 Stanford University - National Biocomputation Center Focus is on 3D imaging and visualization technologies for biomedical applications.

 Stanford Vision Lab

 Stanford Vision and Imaging Science and Technology

 Swiss Federal Institute of Technology - Computer Vision Group Computer vision group performs research in the fields of medical image analysis and visualization, shape modeling and visualization, and remote sensing.

 Swiss Federal Institute of Technology - Vision@IPM Group Specializes in vision- based quality control of industrial processes and automated, model-based image analysis.

 Swiss Group for AI and Cognitive Science

 TU Munich - Image Understanding Group

 TU Munich - Robot Vision Group vision for autonomous robots  Technical University Denmark - Dept of Mathematical Modeling Section for Image Analysis Development and use of methods and theory in practical applications: Biomedical Imaging, Industrial Vision, Material Science, and Remote Sensing.

 Technical University of Lisbon - Image Group

 Technical University of Vienna Pattern Recognition and Image Processing

 Technion Center for Intelligent Systems Technion -- Israel Institute of Technology

 Technion-Israel Institute of Technology - Vision Research and Image Science Laboratory Main fields of interest: Pattern recognition, Analysis of color images, Clinical applications of imaging systems, Image segmentation, Biological and computational vision systems, Computer graphics, Robot vision research, Virtual reality and stereoscopic vision.

 Photogrammetry Division - University of Tehran - Iran we work on vision systems in intelligent vehicles, vision metrology systems, softcopy workstations for mapping, Feature extraction in LIDAR or ALS data and we are interested in anythings which related to photogrammetry and comuter vision.

 Tel Aviv University - Computer Vision

 Telecom Paris - Image Processing and Understanding Group

 Trinity College Computer Vision and Robotics Group

 UBC Lab for Computational Intelligence

 UC San Diego Computer Vision and Robotics Research lab

 University of Houston's Visual Computing Lab  USC Computer Vision

 Universidad de Las Palmas de Gran Canaria - Mathematical Analysis of Images We are interested in applications of Partial Differential Equations to Computer Vision, Image Denoising and Enhancement, Optic Flow , Dense Disparity Map, 3-D Geometry Reconstruction, Medical Imaging, Mutiscale Analysis, etc..

 University College London - Laboratory of computational vision Computational, theoretical, and psychophysical Studies of biological and artificial visual systems

 University Jaume I - Computer Vision Group

 University Jaume I - Computer Vision Group Research on several areas of image analysis and pattern recognition.

 University Jaume I - REGEO Geometric Reconstruction Group Studying the problem of automatically generating 3D models from 2D sketches.

 University of Aberdeen - Parallel and Image Processing Research Group

 University of Algarve Vision Laboratory

 University of Amsterdam - Intelligent Autonomous Systems Group

 University of Amsterdam - Intelligent Sensory Information Systems The central research themes of ISIS are image databases and computer vision, particularly where the two themes meet. We do strategic and fundamental research regularly in a multi-disciplinary and applied setting

 University of Antwerp - Vision Lab

 University of Autonònoma de Barcelona Computer Vision Center  University of Bern Research Group on Computer Vision and AI

 University of Bielefeld - Applied Computer Science Group research in the area of pattern analysis, computer vision, and speech understanding and applications to bioinformatics and natural sciences

 University of Bielfeld - Neuroinformatics Group

 University of of Birmingham - Digital Systems and Vision Processing Group basic research in motion analaysis, unsupervised segmentation, model-based image interpretation, reconfigurable and novel architectures for image interpretation, speech analysis, speech synthesis and its application in medicine, industrial inspection and education.

 University of Bologna - Biometric Systems Lab The main research effort of the Biometric Systems Lab is devoted to develop efficient automatic systems for classification, identification and recognition of human characteristics, such as hand shape, fingerprint and face. Our ongoing contacts with industrial partners ensure that our research results will be tested in real applications.

 University of Bologna- Vision Mathematics Group Our team works at the use of topology and geometry in computer vision and robotic applications. We are mainly interested in the use of Size Functions and Size Theory for shape comparison.

 University of Bonn CSD III Computer Vision and Pattern Recognition group

 University of Bonn Institute for Photogrammetry

 University of Bremen Institute for Neurophysics

 University of Brighton - Applied Image Processing Resource Unit  University of Bristol Image Processing and Computer Vision Group

 University of California Berkeley Computer Vision group

 University of California Irvine Computer Vision lab

 University of California Irvine Vision Research

 University of California San Diego Visual Computing lab

 University of California Santa Barbara - Four Eyes Lab Research in "imaging, interaction, and innovative interfaces" (four I's) - primary focus on computer vision, HCI, and augmented reality.

 University of California Santa Barbara - Image Processing & Vision Research Labs

 University of California, Los Angeles - Vision Lab

 University of California, Riverside Visualization & Intelligent Systems Laboratory (VISLab)

 University of Cape Town Image Processing lab

 University of Central Florida Computer Vision lab  University of Chicago Vision and Robotics group

 University of Cologne Pattern Recognition group

 University of Copenhagen - Image Research Group

 University of Costa Rica - Image Processing and Computer Vision Research Laboratory (IPCV- LAB) Our current research projects include image segmentation, pose, shape, color, motion and mimic estimation of real objects for robotics, on-line inspection, in-situ microscopy and video compression.

 University of Edinburgh - Machine Vision Unit

 University of Erlangen - Computer Vision, Image Processing and Analysis

 University of Essex - Vision Group face recognition, autonomous vehicle navigation, motion and occlusion, edge finding

 University of Exeter - Pattern Analysis and Neural Networks Group Pattern Analysis and Neural Networks

 University of Florida Center for Computer Vision and Visualization

 University of Freiburg - Chair of Pattern Recognition and Image Processing

 University of Geneva Vision Group

 University of Genova - LIRA-Lab Laboratory for integrated advanced robotics

 University of Georgia - Visual and Parallel Computing Laboratory The goal of the VPCL is to advance the state of the art in the theory and applications of Visual Computing and Parallel Computing. Current projects deal with machine vision for inspection and production planning, image analysis of DNA microarrays, pattern recognition problems in DNA analysis, analysis of motion in video sequences and applications of parallel computing to the above problems.

 University of Glasgow - 3D-MATIC Research Laboratory By combining the science of 'photogrammetry' with digital camera technology, it is now possible to capture 3D models of people, animals and objects that are both metrically accurate and photo-realistic in appearance. Ongoing research within the Partnership is also exploring 3D data extraction from still images and movie sequences and the extension of the imaging technology to capture images in real time.

 University of Granada - Computer Vision Group Specializes in image representational models, distortion measures, target distinctness and image compression

 University of Granada Digital Image Analysis group

 University of Guelph - Robot Vision Group of Intelligent Systems Lab We are interested in exploring real-time dynamic visual processes (e.g., tracking, optical flow, binocular vision) cast in a particle filter framework. We also explore using these visual processes for autonomous robot control in conjunction with markovian planning techniques for various applications such as elderly or disabled aids, search and rescue robotics, intelligent automobiles,...

 University of Hannover - Institute for Photogrammetry and GeoInformation specialises in photogrammetry, remote sensing, and aerial image analysis, in connection with geographic information systems

 University of Hannover Institut für Theoretische Nachrichtentechnik und Informationsverarbeitung (TNT)

 University of Hawaii at Manoa - Image Sequence Processing Group Specializes in the application of vision models (particularly local frequency representations and segmentation-based models) to image and image sequence processing and computer vision.

 University of Heidelberg - Digital Image Processing Group Scientific Applications  University of Illinois Chicago - Computer Vision and Robotics Laboratory

 University of Illinois Urbana-Champaign Robotics and Computer Vision

 University of Iowa Division of Physiologic Imaging

 University of Jena Digital Image Processing group

 University of Koblenz Image Recognition lab

 University of Ljubljana - Computer Vision Laboratory

 University of Louisville - Computer Vision and Image Processing Lab Computer vision and Medical Imaging research

 University of Maryland Computer Vision Lab

 University of Massachusetts Amherst - Computer Vision Laboratory

 University of Massachusetts Amherst - Laboratory for Perceptual Robotics

 University of Melbourne Computer Vision and Machine Intelligence lab  University of Messina - Vision Lab Still image segmentation and real-time image analysis

 University of Miami - Underwater Vision and Imaging Laboratory

 University of Modena and Reggio Emilia - Image Processing Laboratory

 University of Modena and Reggio Emilia - Imagelab

 University of Montreal - Computer Vision & Geometric Modeling Lab

 University of Nevada - Computer Vision Laboratory

 University of North Carolina at Charlotte - Vision Group We are currently working on areas such as Gesture recognition, Vision based tracking for VR, and Skin Detection studies

 University of Nottingham - Image Processing and Interpretation Research Group The Image Processing & Interpretation (IPI) Research Group addresses basic issues in image processing and analysis, machine vision and artificial intelligence. The group combines theoretical and applied research, working within forcing domains provided by real problems and applications.

 University of Otago - Computer Vision Research Group

 University of Ottawa - Video, Image, Vision and Audio Research Group Categories include: Computer Vision, Image Processing, Video and Audio Processing and Coding.

 University of Oulu Machine Vision and Media Processing Group

 University of Paraná - Computer Vision and Image Processing Group Our current research focuses on range image segmentation, 3D modeling from range images, medical images processing, visualization and content-based image retrieval.  University of Pavia - Vision Lab Specializes in visual attention mechanisms; includes human-computer interfaces

 University of Pennsylvania - Vision Analysis and Simulation Technologies Laboratory We do research in computer vision (shape and motion estimation), computer graphics and medical image analysis

 University of Pennsylvania GRASP lab

 University of Pennsylvania Medical Image Processing Group

 University of Pisa - Industrial Vision Lab Artificial vision applications to manufacturing processes and product quality control.

 University of Plymouth - Robotic Intelligence Laboratory The lab focuses on problems related to the design of intelligent domestic and helper robots. These include artificial vision for object recognition and vision for spatial navigation, actions planning and sequencing, and natural language instruction dialogues with the user.

 University of Politecnica Madrid - Computer Vision Group Automatic visual automation in manufacturing three-dimesional vision visual information management systems

 University of Reading - Computational Vision Group

 University of Rochester - Vision and Robotics research Lab

 University of Rochester Center for Electronic Imaging Systems

 University of São Paulo - Creative Vision Group Specializes in person recognition using video sequences  University of São Paulo Cybernetic Vision Research group at the Instituto de Fisica de São Carlos

 University of Saskatchewan Computer Vision

 University of South Florida Image Analysis Research Group

 University of Southampton - Image, Speech, and Intelligent Systems

 University of Southamton Image, Speech and Intelligent Systems Group (ISIS)

 University of Southern California - Visual Processing Laboratory

 University of Surrey Vision, Speech, and Signal Processing Group

 University of Sussex COGS Vision Research

 University of São Paulo - Image Computing Group, Medical Physics

 University of Technology, Sydney - Computer Vision and Cluster Computing Lab Focusing on cluster-based computer vision within the Spiral Architecture.

 University of Tennessee, Knoxville - Imaging, Robotics, and Intelligent Systems lab

 University of Toronto - Computational Vision Group

 University of Twente - Laboratory for Measurement and Instrumentation  University of Udine - Machine Vision Lab

 University of Ulster Computer Vision and Image Processing Research group

 University of Utah - Center for Scientific Computing and Imaging

 University of Utah Robotics and Computer Vision

 University of Verona - Vision, Image Processing, and Sound Laboratory

 University of Virginia Computer Vision Research (CS)

 University of Washington - Information Processing Lab

 University of Washington Image Computing Systems Lab

 University of West Florida Image Analysis/Robotics Research Laboratory

 University of Western Australia Robotics and Vision research group

 University of Wisconsin Computer Vision group

 University of York Computer Vision and Pattern Recognition

 University of of California, San Diego - Computer Vision & Robotics Research Lab

 University of of Plymouth - Centre for Intelligent Systems

 University of of Texas - Laboratory for Vision Systems

 University of of Zagreb Image Processing Group

 University of the Balearic Islands - Computer Graphics and Vision Group  University of the West of England - Machine Vision Group Specialize in surface inspection

 Utrecht University - Image Science Institute Focus is on medical imaging

 Vanderbilt University Center for Intelligent Systems

 Vienna University of Technology - Pattern Recognition and Image Processing Group Object recognition, 3D Computer Vision, Graph theory in CV, AI methods in CV

 Computer Vision Group at Vietnam National University of HCMC - Univ of Natural Sciences Our research are concentrated on Object detection, recognition, tracking, Human activity recognition and tracking.

 Vincent Torre Lab at SISSA

 Virage, Inc.

 Computer Vision Group at Virginia Tech Applied research in computer vision and pattern recognition.

 Vision Systems Laboratory, RINCE/DCU: Centre for Applied Imaging and Vision Systems Research group

 Washington University St. Louis - CVIA Lab specializing in medical computer vision

 Weizmann Institute of Science - Computer Vision Lab

 Wright State University Intelligent Systems Lab

 Wright-Patterson Model Based Vision Lab  Yale School of Medicine Image Processing and Analysis Group

 Vision Lab at York University Research in the Vision Lab at York University concentrates on theoretical and applied aspects of computer vision, with a particular emphasis on stereo and motion analysis.

 York University - Center For Vision Research carries out research into sensory and motor processes, perception, and computer vision.

 York University Vision, Graphics and Robotics

 eyeTap Personal Imaging Lab The ePI Lab is a is a computer vision research and development lab focused on the area of personal imaging, mediated reality and wearable computers.

 Statistical Learning & Image Processing Genova University Research Unit Our research focuses on: (1) the study of mathematically sound methods for solving classification problems (2) the development of techniques for extracting visual information from images.

 Laboratory for imagery, vision, and artificial intelligence a team of multidisplinary reserachers on the field of artificial vision, pattern recognition, image processing, learning algorithms, genetic computing, artificial intelligence, and perception

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