Segmentation with shape priors
Speaker: Tammy Riklin-Raviv , Tel-Aviv University
Date: February 21 2007
Time: 3:00PM to 4:00PM
Location: Seminar Room D463 (Star)
Contact: Mario Christoudias and Gerald Dalley, 3-4278, 3-6095, cmch@csail.mit.edu, dalleyg@mit.edu
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Segmentation with shape priors
Challenging object detection and segmentation tasks can be facilitated by the availability of a reference object. However, accounting for possible transformations between the different object views, as part of the segmentation process, remains difficult.
Recent statistical methods address this problem by using comprehensive training data. Other techniques can only accommodate similarity transformations.
We suggest a variational approach to prior-based segmentation, using a single reference object, in the presence of planar projective transformations.
The proposed algorithm detects the object of interest, extracts its boundaries, and concurrently carries out the registration to the prior shape.
Explicit prior shape information is not always available. Consider the simultaneous segmentation of two object views. When neither of the images contains sufficient information for correct object extraction - none of them can be used as a reliable prior for the other image. We therefore suggest an alternate minimization framework in which the evolving segmentation of each image provides a dynamic prior for the other. We call this process “mutual segmentation”.
When only a single image is given, but the object taken is known to be symmetrical, the symmetry property forms a significant shape constraint and thus can be used to facilitate segmentation. This task becomes nontrivial when the object undergoes a projective transformation. We introduce a novel method for the extraction of symmetrical object distorted by perspectivity. Information on the symmetry axis of the object and the distorting transformation is recovered, up to well-defined limits, as part of the segmentation process.
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