3 research outputs found
Meaningful Matches in Stereovision
This paper introduces a statistical method to decide whether two blocks in a
pair of of images match reliably. The method ensures that the selected block
matches are unlikely to have occurred "just by chance." The new approach is
based on the definition of a simple but faithful statistical "background model"
for image blocks learned from the image itself. A theorem guarantees that under
this model not more than a fixed number of wrong matches occurs (on average)
for the whole image. This fixed number (the number of false alarms) is the only
method parameter. Furthermore, the number of false alarms associated with each
match measures its reliability. This "a contrario" block-matching method,
however, cannot rule out false matches due to the presence of periodic objects
in the images. But it is successfully complemented by a parameterless
"self-similarity threshold." Experimental evidence shows that the proposed
method also detects occlusions and incoherent motions due to vehicles and
pedestrians in non simultaneous stereo.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence 99,
Preprints (2011) 1-1
Significance tests and statistical inequalities for region matching
International audienceRegion 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