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Feature extraction: Corners and blobs

Characteristics of good features, applications, finding corners, corner detection basic idea, corner detection mathematics,. As the main contents of the lectures "Feature extraction: Corners and blobs". Invite you to consult for additional documents for the academic needs and research. | Feature extraction: Corners and blobs Why extract features? Motivation: panorama stitching We have two images – how do we combine them? Why extract features? Motivation: panorama stitching We have two images – how do we combine them? Step 1: extract features Step 2: match features Why extract features? Motivation: panorama stitching We have two images – how do we combine them? Step 1: extract features Step 2: match features Step 3: align images Characteristics of good features 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 Many fewer features than image pixels Locality A feature occupies a relatively small area of the image; robust to clutter and occlusion Applications Feature points are used for: Motion tracking Image alignment 3D reconstruction Object recognition Indexing and database retrieval Robot navigation Finding Corners Key property: in the region around a corner, image gradient has two or more dominant directions Corners are repeatable and distinctive C.Harris and M.Stephens. "A Combined Corner and Edge Detector.“ Proceedings of the 4th Alvey Vision Conference: pages 147--151. Corner Detection: Basic Idea We should easily recognize the point by looking through a small window Shifting a window in any direction should give a large change in intensity “edge”: no change along the edge direction “corner”: significant change in all directions “flat” region: no change in all directions Source: A. Efros Corner Detection: Mathematics Change in appearance for the shift [u,v]: Intensity Shifted intensity Window function or Window function w(x,y) = Gaussian 1 in window, 0 outside Source: R. Szeliski Corner Detection: Mathematics Change in appearance for the shift [u,v]: I(x, y) E(u, v) E(0,0) E(3,2) Corner Detection: Mathematics Change in appearance for the shift [u,v]: Second-order . | Feature extraction: Corners and blobs Why extract features? Motivation: panorama stitching We have two images – how do we combine them? Why extract features? Motivation: panorama stitching We have two images – how do we combine them? Step 1: extract features Step 2: match features Why extract features? Motivation: panorama stitching We have two images – how do we combine them? Step 1: extract features Step 2: match features Step 3: align images Characteristics of good features 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 Many fewer features than image pixels Locality A feature occupies a relatively small area of the image; robust to clutter and occlusion Applications Feature points are used for: Motion tracking Image alignment 3D reconstruction Object recognition Indexing and database retrieval Robot navigation .