Feature Detection and Tracking
1 Feature points on an image The technique for generic keypoint detection may be extended for performing traking of features in images sequences. The algorithm is based on the SSD test (Sum of Squared Diierence) which allows to deene precisely the concept of \reliable point feature". The technique is best suited for small motions, but it can be extended to larger displacements by being applied in a coarse-tone manner. Features are often deened as characteristic points on an image which are easily recognizable and representative of the structure of the scene being viewed. In order to develop a technique to estract features automatically we need a more operative deenition. First of all it is not easy to identify single points (pixels), so we associate to each point of the image (x; y) a small neighborhood (window) W(x; y). We say that the window W is a reliable window if the integral of the squared gradient of the image is above some threshold : if I(x; y; t) is the brightness of the image at the point(x; y) at time t, and rI(x 0 ; y 0 ; t 0) is the spatial gradient calculated at a point, then the window being reliable can be expressed as min Z W(x 0 ;y 0) rIrI T dxdy ! > ; (1) where min denotes the smallest singular value. In practical implementations the partial derivatives will be approximated by rst diierence operators and the integral by a nite sum. The expression in parenthesis is referred as Sum of Squared Diierence (SSD). Now we say that the point (x 0 ; y 0) is a feature point if W(x 0 ; y 0) is a reliable window. After this deenition it is easy to set an algorithm to extract features: set a threshold for all points in the image compute the SSD IF the minimum singular value of the SSD is bigger than the threshold THEN mark the point as a feature point.