Shape-based mutual segmentation

Tammy Riklin-Raviv, Nir Sochen, Nahum Kiryati

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


We present a novel variational approach for simultaneous segmentation of two images of the same object taken from different viewpoints. Due to noise, clutter and occlusions, neither of the images contains sufficient information for correct object-background partitioning. The evolving object contour in each image provides a dynamic prior for the segmentation of the other object view. We call this process mutual segmentation. 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 suggested shape term incorporates the semantic knowledge gained in the segmentation process of the image pair, accounting for excess or deficient parts in the estimated object shape. Transformations, including planar projectivities, between the object views are accommodated by a registration process held concurrently with the segmentation. The proposed segmentation algorithm is demonstrated on a variety of image pairs. The homography between each of the image pairs is estimated and its accuracy is evaluated.

Original languageEnglish
Pages (from-to)231-245
Number of pages15
JournalInternational Journal of Computer Vision
Issue number3
StatePublished - 1 Sep 2008
Externally publishedYes


  • Level sets
  • Mutual segmentation
  • Perspective transformation
  • Planar projective homography
  • Shape

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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