Determining point correspondences between two views under geometric constraint and photometric consistency

Matching or tracking points of interest between several views is one of the keystones of many computer vision applications, especially when considering structure and motion estimation. The procedure generally consists in several independent steps, basically 1) point of interest extraction, 2) point of interest matching by keeping only the ``best correspondences'' with respect to similarity between some local descriptors, 3) correspondence pruning to keep those consistent with an estimated camera motion (here, consistent with epipolar constraints or homography transformation). Each step in itself is a touchy task which may endanger the whole process. In particular, repeated patterns give lots of false matches in step 2) which are hardly, if never, recovered by step 3). Starting from a statistical model by Moisan and Stival, we propose a new one-stage approach to steps 2) and 3), which does not need tricky parameters. The advantage of the proposed method is its robustness to repeated patterns