2 Corners and Blobs Image Analysis Feature extraction: corners and blobs Christophoros Nikou
[email protected] Images taken from: Computer Vision course by Svetlana Lazebnik, University of North Carolina at Chapel Hill (http://www.cs.unc.edu/~lazebnik/spring10/). D. Forsyth and J. Ponce. Computer Vision: A Modern Approach, Prentice Hall, 2003. M. Nixon and A. Aguado. Feature Extraction and Image Processing. Academic Press 2010. University of Ioannina - Department of Computer Science C. Nikou – Image Analysis (T-14) Motivation: Panorama Stitching 3 Motivation: Panorama Stitching 4 (cont.) Step 1: feature extraction Step 2: feature matching C. Nikou – Image Analysis (T-14) C. Nikou – Image Analysis (T-14) Motivation: Panorama Stitching 5 6 Characteristics of good features (cont.) • Repeatability – The same feature can be found in several images despite geometric and photometric transformations. • Saliency – Each feature has a distinctive description. • Compactness and efficiency Step 1: feature extraction – Many fewer features than image pixels. Step 2: feature matching • Locality Step 3: image alignment – A feature occupies a relatively small area of the image; robust to clutter and occlusion. C. Nikou – Image Analysis (T-14) C. Nikou – Image Analysis (T-14) 1 7 Applications 8 Image Curvature • Feature points are used for: • Extends the notion of edges. – Motion tracking. • Rate of change in edge direction. – Image alignment. • Points where the edge direction changes rapidly are characterized as corners. – 3D reconstruction. • We need some elements from differential ggyeometry. – Objec t recogn ition. • Parametric form of a planar curve: – Image indexing and retrieval. – Robot navigation. Ct()= [ xt (),()] yt • Describes the points in a continuous curve as the endpoints of a position vector.