Prior-Based Segmentation by Projective Registration and Level Sets

Object detection and segmentation 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 a challenge. Recent works address this problem by using comprehensive training data. Other approaches are applicable only to limited object classes or can only accommodate similarity transformations. We suggest a novel variational approach to prior-based segmentation, which accounts for planar projective transformation, using a single reference object. The prior shape is registered concurrently with the segmentation process, without point correspondence. The algorithm detects the object of interest and correctly extracts its boundaries. The homography between the two object views is accurately recovered as well. Extending the Chan-Vese level set framework, we propose a region-based segmentation functional that includes explicit representation of the projective ho...

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Tammy Riklin-Raviv, Nahum Kiryati, Nir A. Sochen
• Keywords: Computer Vision, ICCV 2005, Limited Object Classes, Prior-based Segmentation, Region-based Segmentation, Segmentation Process, Single Reference Object