NDLI: Shape-Based Mutual Segmentation

Content Provider	Springer Nature Link
Author	Riklin Raviv, Tammy Sochen, Nir Kiryati, Nahum
Copyright Year	2007
Abstract	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.
Starting Page	231
Ending Page	245
Page Count	15
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	79
Issue Number	3
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2007-12-29
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Mutual segmentation Level sets Shape Perspective transformation Planar projective homography Pattern Recognition Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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