Region-based tracking using affine motion models in long image sequences
CVGIP: Image Understanding
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Denoising by Statistical Area Thresholding
Journal of Mathematical Imaging and Vision
An a contrario Decision Framework for Region-Based Motion Detection
International Journal of Computer Vision
An A Contrario Decision Method for Shape Element Recognition
International Journal of Computer Vision
Bottom-up and top-down object matching using asynchronous agents and a contrario principles
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
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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.