Abstract

Extraction and matching of discriminative feature points in images is an important problem in computer vision with applications in image classi- fication, object recognition, mosaicing, automatic 3D reconstruction and stereo. Features are represented and matched via descriptors that must be invariant to small errors in the localization and scale of the extracted feature point, viewpoint changes, and other kinds of changes such as illumination, image compression and blur. While currently used feature descriptors are able to deal with many of such changes, they are not invariant to a generic monotonic change in the intensities, which occurs in many cases. Furthermore, their performance degrades rapidly with many image degradations such as blur and compression where the intensity transformation is non-linear. In this paper, we present a new feature descriptor that obtains invariance to a monotonic change in the intensity of the patch by looking at orders between certain pixels in the patch. An order change between pixels indicates a difference between the patches which is penalized. Summation of such penalties over carefully chosen pixel pairs that are stable to small errors in their localization and are independent of each other leads to a robust measure of change between two features. Promising results were obtained using this ap- proach that show signi ficant improvement over existing methods, especially in the case of illumination change, blur and JPEG compression where the intensity of the points changes from one image to the next.