DOCSLIB.ORG

Color and Shape Recognition

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Color and Shape Recognition View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by ethesis@nitr Color and Shape Recognition Smruti Saurav Shasani (111CS0607) Ramiya Ranjan Meher (111CS0137) Manoj Kumar Patra (111CS0465) Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela { 769 008, Odisha, India Color and Shape Recognition Thesis submitted in partial fulfillment of the requirements for the degree of Bachelor Of Technology in Computer Science and Engineering by Smruti Saurav Shasani (111CS0607) Ramiya Ranjan Meher (111CS0137) Manoj Kumar Patra (111CS0465) under the supervision of Dr. Pankaj Kumar Sa Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela { 769 008, Odisha, India May 2015 Department of Computer Science and Engineering National Institute of Technology, Rourkela Certificate This is to certify that the work in the thesis entitled Color and Shape Recognition by Smruti Saurav Shasani (111CS0607), Ramiya Ranjan Meher (111CS0137), and Manoj Kumar Patra (111CS0465), is a record of an original research work carried out by them under my supervision and guidance in partial fulfillment of the requirements for the award of the degree of Bachelor of Technology in Computer Science and Engineering. Date: May 11, 2015 Dr. Pankaj Kumar Sa Assistant Professor CSE Department NIT Rourkela Acknowledgement This thesis has been possible due to the help and endeavor of many people. Many individuals have helped throughout for the finishing of this task and each of their inputs has been valuable. Our deepest and sincerest gratitude goes to our supervisor, Dr. Pankaj Kumar Sa, Assis- tant Professor, Department of Computer Science & Engineering, for his guidance and encouragement throughout the period of the undertaking. He stimulated us to work on the topic and provided valuable informa- tion which helped in completing the project through various stages. We are also thankful to NIT Rourkela for the academic resources it has supplied us through the course of the project. We want to thank the staff from the office who have been sufficiently benevolent to extend their help towards the completion of this project. We would also like to take this opportunity to express our sincere gratitude to everyone who has provided us with inspirational words, ideas, constructive criticism, and above all, their invaluable time. Abstract The object "car" and "cat" can be easily distinguished by humans, but how these labels are assigned? Grouping these images is easy for a person into different categories, but its very tedious for a computer. Hence, an object recognition system finds objects in the real world from an image. Object recognition algorithms rely on matching, learning or pattern recognition algorithms using appearance-based or feature-based techniques. In this thesis, the use of color and shape attributes as an explicit color and shape representation respectively for object detection is pro- posed. Color attributes are dense, computationally effective, and when joined with old-fashioned shape features provide pleasing results for ob- ject detection. The procedure of shape detection is actually a natural extension of the job of edge detection at the pixel level to the diffi- culty of global contour detection. A tool for a systematic analysis of edge based shape detection is provided by this filtering scheme. This enables us to find distinctions between objects based on color and shape. Keywords: Color Models, Edge-based Shape Detection Contents 1 Introduction 1 1.1 Computer Vision . 2 1.1.1 OpenCV (the tool) . 2 1.2 Motivation . 3 1.3 Objective . 3 1.4 Thesis Organization . 4 2 Color Recognition 5 2.1 Color Models . 5 2.1.1 RGB Color Model . 5 2.1.2 CMYK Color Model . 8 2.1.3 HSV Color Model . 9 2.1.4 Grayscale . 10 3 Edge-Based Shape detection 11 3.1 Edge Detection . 13 3.1.1 Sobel Filter . 13 3.2 Line Detection . 14 3.2.1 Hough Transform . 15 3.2.2 Advantages and Disadvantages . 15 3.3 Regular Polygon Detection . 16 3.4 Circle Detection . 16 3.4.1 Circular Hough Transform . 16 4 Simulation and Results 19 4.1 Color Recognition . 19 4.2 Shape Recognition . 20 4.2.1 Polygon Recognition . 20 4.2.2 Circle Detection . 22 5 Conclusion and Future Work 24 List of Figures 2.1 RGB Color Wheel. 6 2.2 Schematic of RGB Color Cube. 7 2.3 CMY Color Wheel. 9 2.4 HSV Color Wheel. 10 3.1 Sobel Filter. 14 4.1 Test Image I for COLOR Recognition . 19 4.2 Test Image II for COLOR Recognition . 19 4.3 Input Image for POLYGON Detection . 20 4.4 Output Image I for POLYGON Detection . 21 4.5 Output Image II for POLYGON Detection . 21 4.6 Input Image I for CIRCLE Detection . 22 4.7 Output Image I for CIRCLE Detection . 22 4.8 Input Image II for CIRCLE Detection . 23 4.9 Output Image II for CIRCLE Detection . 23 List of Tables 2.1 RGB Numeric Representations . 8 2.2 RGB and HSV values of some primary colors . 10 Chapter 1 Introduction A solitary image conveys a ton of information in a brief while in light of the fact that we see an image at the same time, though reading or listening to frequently takes essentially more to process the same information. Colors and shapes work in amicability with one another to impart. Accordingly, a comprehension of shapes is key to comprehension the power of color. Shape detection obliges pre-programming in a mathematical depic- tion database of the shapes to detect. For instance, assume composing a program which can recognize a triangular shape, a square shape, and a circular shape. We can do it this way: Run contour identification for discovering the boundary line of every shape considered. Then the number of continuous edges is counted. A sharp deviation in line de- tection implies an alternate line does this by determining the average vector between adjoining pixels. In the event that three lines identi- fied, then it's a triangle. In the event that four lines, then a square. In the event that one closed line, then it is a circle. You can focus more information by measuring angles between lines (rhombus, square, equilateral triangle, etc.) Complex shapes obliges pattern recognition that is probability anal- ysis. For instance, assume our algorithm expected to perceive between 10 distinct natural products (just by shape), for example, an orange, a bunch of grapes, an apple, a cherry, and so on. How will this be achieved? Since, all of these are circular, however none of them is seamlessly circular. Also, neither all fruits appear to be identical. By 1 utilizing probability, an investigation can be run that says 'gracias, this natural product matches 85% of the attributes of a cherry, yet just 65% the qualities of an apple, so it's more probable a cherry. It is the computational form of an 'educated guess.' We can likewise say if some specific characteristic is available, then it has a 30% higher likelihood of being an orange. The characteristic can be a stem, for example, spikes like on a pineapple, or fuzziness like on a coconut, and so on. This strategy is known as feature detection. 1.1 Computer Vision It is the study of machines that have the capacity to extract infor- mation from an image that is important to tackle some assignment. As a scientific discipline, computer vision is concerned with the hypothesis behind artificial systems that extract information from images. The image information can take numerous structures, for example, video sequences, sees from different cams, or multi-dimensional information from a medicinal scanner. As a technological discipline, computer vi- sion tries to apply its speculations and models to the development of computer vision frameworks. Illustrations of uses of computer vision include system for: 1. Controlling processes (e.g., an industrial robot or an autonomous vehicle). 2. Detecting events (e.g., for visual surveillance or people counting). 3. Organizing information (e.g., for indexing databases of images and mage sequences). 4. Modelling objects or environments (e.g., industrial inspection, med- ical image analysis or topographical modelling). 5. Interaction (e.g., as the input to a device for computer-human interaction). 1.1.1 OpenCV (the tool) The OpenCV Library is principally gone for real-time computer vi- sion. Some sample zones would be Human-Computer Interaction (HCI); 2 Segmentation, Object detection, and Recognition; Gesture Recognition; Face Recognition; Motion Tracking, Ego Motion, and Motion Under- standing; SFM (Structure from Motion); and Mobile Robotics. The OpenCV Library is a gathering of low-overhead, elite operations performed on images. The OpenCV actualizes a wide mixture of tools for image interpretation. It is good with IPL (Intel Image Processing Library) that actualizes low-level operations on computerized images. Notwithstanding primitives, for example, filtering, banalization, pyra- mids, image statistics, OpenCV is basically an high-level library imple- menting algorithms for Camera Calibration (calibration techniques), Optical Flow (tracking), and feature (Feature detection), motion anal- ysis (Motion Templates, Estimators), Geometry, Contour Processing (shape analysis), View Morphing (3D reconstruction), object segmen- tation and recognition (Embedded Hidden Markov Models, Histogram, Eigen Objects). 1.2 Motivation There is a clear distinction between the two fruits \apple" and \ba- nana", the identification of this distinction for a human is easy, but how can a computer find the difference between those two objects. Identi- fication of an object from its two dimensional image brought up the real interest for this project. This is a paramount topic in the field of computer vision. 1.3 Objective In this project object recognition has been performed considering color and shape as its prime entities.
Load more

Recommended publications
  • Snakes, Shapes, and Gradient Vector Flow
    IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 7, NO. 3, MARCH 1998 359 Snakes, Shapes, and Gradient Vector Flow Chenyang Xu, Student Member, IEEE, and Jerry L. Prince, Senior Member, IEEE Abstract—Snakes, or active contours, are used extensively in There are two key difficulties with parametric active contour computer vision and image processing applications, particularly algorithms. First, the initial contour must, in general, be to locate object boundaries. Problems associated with initializa- close to the true boundary or else it will likely converge tion and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force to the wrong result. Several methods have been proposed to for active contours, largely solving both problems. This external address this problem including multiresolution methods [11], force, which we call gradient vector flow (GVF), is computed pressure forces [10], and distance potentials [12]. The basic as a diffusion of the gradient vectors of a gray-level or binary idea is to increase the capture range of the external force edge map derived from the image. It differs fundamentally from fields and to guide the contour toward the desired boundary. traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding The second problem is that active contours have difficulties snake is formulated directly from a force balance condition rather progressing into boundary concavities [13], [14]. There is no than a variational formulation. Using several two-dimensional satisfactory solution to this problem, although pressure forces (2-D) examples and one three-dimensional (3-D) example, we [10], control points [13], domain-adaptivity [15], directional show that GVF has a large capture range and is able to move snakes into boundary concavities.
    [Show full text]
  • Good Colour Maps: How to Design Them
    Good Colour Maps: How to Design Them Peter Kovesi Centre for Exploration Targeting School of Earth and Environment The University of Western Australia Crawley, Western Australia, 6009 [email protected] September 2015 Abstract Many colour maps provided by vendors have highly uneven percep- tual contrast over their range. It is not uncommon for colour maps to have perceptual flat spots that can hide a feature as large as one tenth of the total data range. Colour maps may also have perceptual discon- tinuities that induce the appearance of false features. Previous work in the design of perceptually uniform colour maps has mostly failed to recognise that CIELAB space is only designed to be perceptually uniform at very low spatial frequencies. The most important factor in designing a colour map is to ensure that the magnitude of the incre- mental change in perceptual lightness of the colours is uniform. The specific requirements for linear, diverging, rainbow and cyclic colour maps are developed in detail. To support this work two test images for evaluating colour maps are presented. The use of colour maps in combination with relief shading is considered and the conditions under which colour can enhance or disrupt relief shading are identified. Fi- nally, a set of new basis colours for the construction of ternary images arXiv:1509.03700v1 [cs.GR] 12 Sep 2015 are presented. Unlike the RGB primaries these basis colours produce images whereby the salience of structures are consistent irrespective of the assignment of basis colours to data channels. 1 Introduction A colour map can be thought of as a line or curve drawn through a three dimensional colour space.
    [Show full text]
  • Light and Color
    Chapter 9 LIGHT AND COLOR What Is Color? Color is a human phenomenon. To the physicist, the only difference be- tween light with a wavelength of 400 nanometers and that of 700 nm is Different wavelengths wavelength and amount of energy. However a normal human eye will see cause the eye to see another very significant difference: The shorter wavelength light will different colors. cause the eye to see blue-violet and the longer, deep red. Thus color is the response of the normal eye to certain wavelengths of light. It is nec- essary to include the qualifier “normal” because some eyes have abnor- malities which makes it impossible for them to distinguish between certain colors, red and green, for example. Note that “color” is something that happens in the human seeing ap- Only light itself paratus—when the eye perceives certain wavelengths of light. There is causes sensations of no mention of paint, pigment, ink, colored cloth or anything except light color. itself. Clear understanding of this point is vital to the forthcoming discus- sion. Colorants by themselves cannot produce sensations of color. If the proper light waves are not present, colorants are helpless to produce a sensation of color. Thus color resides in the eye, actually in the retina- optic-nerve-brain combination which teams up to provide our color sen- Color vision is sations. How this system works has been a matter of study for many years complex and not and recent investigations, many of them based on the availability of new completely brain scanning machines, have made important discoveries.
    [Show full text]
  • Gradient Magnitude Similarity Deviation: an Highly Efficient Perceptual Image Quality Index
    1 Gradient Magnitude Similarity Deviation: An Highly Efficient Perceptual Image Quality Index Wufeng Xue, Lei Zhang, Member IEEE, Xuanqin Mou, Member IEEE, and Alan C. Bovik, Fellow, IEEE Abstract—It is an important task to faithfully evaluate the perceptual quality of output images in many applications such as image compression, image restoration and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. Figure 1 The flowchart of a class of two-step FR-IQA models. We present a new effective and efficient IQA model, called ratio (PSNR) correlates poorly with human perception, and gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local hence researchers have been devoting much effort in structures in a distorted image suffer different degrees of developing advanced perception-driven image quality degradations. This motivates us to explore the use of global assessment (IQA) models [2, 25]. IQA models can be classified variation of gradient based local quality map for overall image [3] into full reference (FR) ones, where the pristine reference quality prediction. We find that the pixel-wise gradient magnitude image is available, no reference ones, where the reference similarity (GMS) between the reference and distorted images image is not available, and reduced reference ones, where combined with a novel pooling strategy – the standard deviation of the GMS map – can predict accurately perceptual image partial information of the reference image is available.
    [Show full text]
  • Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing Using Green Function Convolution
    Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution Dominique Beaini, Sofiane Achiche, Fabrice Nonez, Olivier Brochu Dufour, Cédric Leblond-Ménard, Mahdis Asaadi, Maxime Raison Abstract In computer vision, the gradient and Laplacian of an image are used in different applications, such as edge detection, feature extraction, and seamless image cloning. Computing the gradient of an image is straightforward since numerical derivatives are available in most computer vision toolboxes. However, the reverse problem is more difficult, since computing an image from its gradient requires to solve the Laplacian equation (also called Poisson equation). Current discrete methods are either slow or require heavy parallel computing. The objective of this paper is to present a novel fast and robust method of solving the image gradient or Laplacian with minimal error, which can be used for gradient-domain editing. By using a single convolution based on a numerical Green’s function, the whole process is faster and straightforward to implement with different computer vision libraries. It can also be optimized on a GPU using fast Fourier transforms and can easily be generalized for an n-dimension image. The tests show that, for images of resolution 801x1200, the proposed GFC can solve 100 Laplacian in parallel in around 1.0 milliseconds (ms). This is orders of magnitude faster than our nearest competitor which requires 294ms for a single image. Furthermore, we prove mathematically and demonstrate empirically that the proposed method is the least-error solver for gradient domain editing. The developed method is also validated with examples of Poisson blending, gradient removal, and the proposed gradient domain merging (GDM).
    [Show full text]
  • Colour Image Gradient Regression Reintegration
    Colour Image Gradient Regression Reintegration Graham D. Finlayson1, Mark S. Drew2 and Yasaman Etesam2 1 School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, U.K. [email protected] 2School of Computing Science, Simon Fraser University, Vancouver, British Columbia, Canada V5A 1S6, {mark,yetesam}@cs.sfu.ca Abstract much-used method for the reintegration task is the well known Suppose we process an image and alter the image gradients Frankot-Chellappa algorithm [3]. This method solves a Poisson in each colour channel R,G,B. Typically the two new x and y com- equation by minimizing difference from an integrable gradient ponent fields p,q will be only an approximation of a gradient and pair in the Fourier domain. Of course, many other algorithms hence will be nonintegrable. Thus one is faced with the prob- have been proposed, e.g. [4, 5, 6, 7, 8, 9]. lem of reintegrating the resulting pair back to image, rather than Note in the first place that any reintegration method leaves a derivative of image, values. This can be done in a variety of ways, constant of integration to be set – an offset added to the resulting usually involving some form of Poisson solver. Here, in the case image – since we start off with derivatives which would zero out of image sequences or video, we introduce a new method of rein- any such offset. Its value must be handled through a heuristic of tegration, based on regression from gradients of log-images. The some kind. strength of this idea is that not only are Poisson reintegration arti- But more fundamentally many reintegration methods will facts eliminated, but also we can carry out the regression applied generate aliasing of various kinds such as creases or halos in the to only thumbnail images.
    [Show full text]
  • Goethe's Theory of Colors Between the Ancient Philosophy, Middle Ages
    CULTURE, MEDIA & FILM | RESEARCH ARTICLE Goethe’s theory of colors between the ancient philosophy, middle ages occultism and modern science Victor Barsan and Andrei Merticariu Cogent Arts & Humanities (2016), 3: 1145569 Page 1 of 29 Barsan & Merticariu, Cogent Arts & Humanities (2016), 3: 1145569 http://dx.doi.org/10.1080/23311983.2016.1145569 CULTURE, MEDIA & FILM | RESEARCH ARTICLE Goethe’s theory of colors between the ancient philosophy, middle ages occultism and modern science 1 2 Received: 18 February 2015 Victor Barsan * and Andrei Merticariu Accepted: 20 January 2016 Published: 18 February 2016 Abstract: Goethe’s rejection of Newton’s theory of colors is an interesting example *Corresponding author: Victor Barsan, of the vulnerability of the human mind—however brilliant it might be—to fanati- Department of Theoretical Physics, cism. After an analysis of Goethe’s persistent fascination with magic and occultism, Horia Hulubei Institute of Physics and Nuclear Engineering, Aleea Reactorului of his education, existential experiences, influences, and idiosyncrasies, the authors nr. 30, Magurele, Bucharest, Romania E-mail: [email protected] propose an original interpretation of his anti-Newtonian position. The relevance of Goethe’s Farbenlehre to physics and physiology, from the perspective of modern sci- Reviewing editor: Peter Stanley Fosl, Transylvania ence, is discussed in detail. University, USA Subjects: Aristotle; Biophysics; Experimental Physics; Fine Art; Medical Physics; Ophthal- Additional information is available at the end of the article mology; Philosophy of Art; Philosophy of Science; Presocratics Keywords: ancient philosophy; Greek–Roman classicism; middle ages science; Newtonian science; occultism; pantheism; optics; theory of colors; primordial phenomenon (urphaeno men) 1. Introduction Light is one of the most interesting components of the physical universe.
    [Show full text]
  • Fuzzy Set Theoretical Approach to the Tone Triangular System
    JOURNAL OF COMPUTERS, VOL. 6, NO. 11, NOVEMBER 2011 2345 Fuzzy Set Theoretical Approach to the Tone Triangular System Naotoshi Sugano Tamagawa University, Tokyo, Japan [email protected] Abstract—The present study considers a fuzzy color system gravity of the attribute information of vague colors. This in which three input fuzzy sets are constructed on the tone fuzzy set theoretical approach is useful for vague color triangle. This system can process a fuzzy input to a tone information processing, color identification, and similar triangular system and output to a color on the RGB applications. triangular system. Three input fuzzy sets (not black, white, and light) are applied to the tone triangle relationship. By treating three attributes of chromaticness, whiteness, and II. METHODS blackness on the tone triangle, a target color can be easily A. Color Triangle and Additive Color Mixture obtained as the center of gravity of the output fuzzy set. In Additive color mixing occurs when two or three beams the present paper, the differences between fuzzy inputs and inference outputs are described, and the relationship of differently colored light combine. It has been found between inference outputs for crisp inputs and for fuzzy that mixing just three additive primary colors, red, green, inputs on the RGB triangular system are shown by the and blue, can produce the majority of colors. In general, a input-output characteristics between chromaticness, color vector can be described by certain quantities as a whiteness, and blackness as the inputs and redness (as one scalar and a direction. These quantities are referred to as of the outputs).
    [Show full text]
  • A Correlated Color Temperature for Illuminants
    . (R P 365) A CORRELATED COLOR TEMPERATURE FOR ILLUMINANTS By Raymond Davis ABSTRACT As has long been known, most of the artificial and natural illuminants do not match exactly any one of the Planckian colors. Therefore, strictly speaking, they can not be assigned a color temperature. A color of this type may, however, be correlated with a representative Planckian color. The method of determining correlated color temperature described in this paper consists in comparing the relative luminosities of each of the three primary red, green, and blue components of the source with similar values for the Planckian series. With such a comparison three component temperatures are obtained; that is, the red component of the source corresponds with that of the Planckian radiator at one temperature, its green component with that of the Planckian radiator at a second temperature, and its blue component with that of the Planckian radiator at a third temperature. The average of these three component temperatures is designated as the correlated color temperature of the source. The mean devia- tion of the component temperatures from the average temperature is used as a basis for specifying the color (chromaticity) departure of the source from that of the Planckian radiator at the correlated color temperature. The conjunctive wave length indicates the kind of color departure. CONTENTS Page I. Introduction 659 II. The proposed method 662 III. Procedure 665 1. The Planckian radiator evaluated in terms of relative lumi- nosity of the primary components 665 2. Computation of the correlated color temperature 670 3. Calculation of color departure in terms of sensation steps 672 4.
    [Show full text]
  • Raphics & Visualization
    Graphics & Visualization Chapter 11 COLOR IN GRAPHICS & VISUALIZATION Graphics & Visualization: Principles & Algorithms Chapter 11 Introduction • The study of color, and the way humans perceive it, a branch of: Physics Physiology Psychology Computer Graphics Visualization • The result of graphics or visualization algorithms is a color or grayscale image to be viewed on an output device (monitor, printer) Graphics programmer should be aware of the fundamental principles behind color and its digital representation Graphics & Visualization: Principles & Algorithms Chapter 11 2 Grayscale • Intensity: achromatic light; color characteristics removed • Intensity can be represented by a real number between 0 (black) and 1 (white) Values between these two extremes are called grayscales • Assume use of d bits to represent the intensity of each pixel n=2d different intensity values per pixel • Question: which intensity values shall we represent ? • Answer: Linear scale of intensities between the minimum & maximum value, is not a good idea: Human eye perceives intensity ratios rather than absolute intensity values. Light bulb example: 20-40-60W Therefore, we opt for a logarithmic distribution of intensity values Graphics & Visualization: Principles & Algorithms Chapter 11 3 Grayscale (2) • Let Φ0 be the minimum intensity value For typical monitors: Φ0 = (1/300) * maximum value 1 (white) Such monitors have a dynamic range of 300:1 • Let λ be the ratio between successive intensity values • Then we take: Φ1 = λ* Φ0 2 Φ1 = λ* Φ1=λ *Φ0 …
    [Show full text]
  • SHORT PAPERS.Indd
    VSMM 2008 VSMM 2008 – ofthe14 Digital Heritage–Proceedings Digital Heritage This volume contains the Short Papers presented at VSMM 2008, the 14th th International Conference on Virtual Systems and Multimedia which took Proceedings of the 14 International place on the 20 to 25 October 2008 in Limassol, Cyprus. The conference title was “Digital Heritage: Our Hi-tech-STORY for the Future, Technologies Conference on Virtual Systems to Document, Preserve, Communicate and Prevent the Destruction of our Fragile Cultural Heritage”. and Multimedia The conference was jointly organized by CIPA, the International ICOMOS Committee on Heritage Documentation and the Cyprus Institute. It also Short Papers hosted the 38th CIPA Workshop dedicated on e-Documentation and Standardization in Cultural Heritage and the second Euro-Med Conference on IT in Cultural Heritage. Through the Cyprus Institute, VSMM 2008 received the support of the Government of Cyprus and the European Commission and it was held under the Patronage of H. E. the President of the Republic of Cyprus. th International Conference on Virtual Systems andMultimedia Virtual Systems on Conference International 20–25 October 2008 Limassol, Cyprus M. Ioannides, A. Addison, A. Georgopoulos, L. Kalisperis (Editors) VSMM 2008 Digital Heritage Proceedings of the 14th International Conference on Virtual Systems and Multimedia Short Papers 20–25 October 2008 Limassol, Cyprus M. Ioannides, A. Addison, A. Georgopoulos, L. Kalisperis (Editors) Marinos Ioannides Editor-in-Chief Elizabeth Jerem Managing Editor
    [Show full text]
  • Third Harmonic Generation Microscopy Zhang, Z
    VU Research Portal Third harmonic generation microscopy Zhang, Z. 2017 document version Publisher's PDF, also known as Version of record Link to publication in VU Research Portal citation for published version (APA) Zhang, Z. (2017). Third harmonic generation microscopy: Towards automatic diagnosis of brain tumors. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. E-mail address: [email protected] Download date: 05. Oct. 2021 Third harmonic generation microscopy: towards automatic diagnosis of brain tumors This thesis was reviewed by: prof.dr. J. Hulshof VU University Amsterdam prof.dr. J. Popp Jena University prof.dr. A.G.J.M. van Leeuwen Academic Medical Center prof.dr. M. van Herk The University of Manchester dr. I.H.M. van Stokkum VU University Amsterdam dr. P. de Witt Hamer VU University Medical Center © Copyright Zhiqing Zhang, 2017 ISBN: 978-94-6295-704-6 Printed in the Netherlands by Proefschriftmaken.
    [Show full text]