Significance Tests and Statistical Inequalities for Region Matching

Region matching finding conjugate regions on a pair of images plays a fundamental role in computer vision. Indeed, such methods have numerous applications such as indexation, motion estimation or tracking. In the vast literature on the subject, several dissimilarity measures have been proposed in order to determine the true match for each region. In this paper, under statistical hypothesis of similarity, we provide an improved decision rule for patch matching based on significance tests and the statistical inequality of McDiarmid. The proposed decision rule allows to validate or not the similarity hypothesis and so to automatically detect matching outliers. The approach is applied to motion estimation and object tracking on noisy video sequences. Note that the proposed framework is robust against noise, avoids the use of statistical tests and may be related to the a contrario approach.