Mutual Segmentation with Level Sets

We suggest a novel variational approach for mutual segmentation of two images of the same object. The images are taken from different views, related by projective transformation. Each of the two images may not provide sufficient information for correct object-background delineation. The emerging segmentation of the object in each view provides a dynamic prior for the segmentation of the other image. The foundation of the proposed method is a unified level-set framework for region and edge based segmentation, associated with a shape similarity term. The dissimilarity between the two shape representations accounts for excess or deficient parts and is invariant to planar projective transformation. The suggested algorithm extracts the object in both images, correctly recovers its boundaries, and determines the homography between the two object views.