GeoS: Geodesic Image Segmentation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2008

AUTHORS

Antonio Criminisi , Toby Sharp , Andrew Blake

ABSTRACT

This paper presents GeoS, a new algorithm for the efficient segmentation of n-dimensional image and video data. The segmentation problem is cast as approximate energy minimization in a conditional random field. A new, parallel filtering operator built upon efficient geodesic distance computation is used to propose a set of spatially smooth, contrast-sensitive segmentation hypotheses. An economical search algorithm finds the solution with minimum energy within a sensible and highly restricted subset of all possible labellings. Advantages include: i) computational efficiency with high segmentation accuracy; ii) the ability to estimate an approximation to the posterior over segmentations; iii) the ability to handle generally complex energy models. Comparison with max-flow indicates up to 60 times greater computational efficiency as well as greater memory efficiency. GeoS is validated quantitatively and qualitatively by thorough comparative experiments on existing and novel ground-truth data. Numerous results on interactive and automatic segmentation of photographs, video and volumetric medical image data are presented. More... »

PAGES

99-112

Book

TITLE

Computer Vision – ECCV 2008

ISBN

978-3-540-88681-5
978-3-540-88682-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-88682-2_9

DOI

http://dx.doi.org/10.1007/978-3-540-88682-2_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1047173815


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