| Coarse-to-fine segmentation and tracking using Sobolev active contours. | |
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MedLine Citation:
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PMID: 18369254 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Recently proposed Sobolev active contours introduced a new paradigm for minimizing energies defined on curves by changing the traditional cost of perturbing a curve and thereby redefining their gradients. Sobolev active contours evolve more globally and are less attracted to certain intermediate local minima than traditional active contours, and it is based on a well-structured Riemannian metric. In this paper, we analyze Sobolev active contours using scale-space analysis in order to understand their evolution across different scales. This analysis shows an extremely important and useful behavior of Sobolev contours, namely, that they move successively from coarse to increasingly finer scale motions in a continuous manner. This property illustrates that one justification for using the Sobolev technique is for applications where coarse-scale deformations are preferred over fine scale deformations. Along with other properties to be discussed, the coarse-to-fine observation reveals that Sobolev active contours are, in particular, ideally suited for tracking algorithms that use active contours. We will also justify our assertion that the Sobolev metric should be used over the traditional metric for active contours in tracking problems by experimentally showing how a variety of active contour based tracking methods can be significantly improved merely by evolving the active contour according to the Sobolev method. |
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Authors:
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Ganesh Sundaramoorthi; Anthony Yezzi; Andrea Mennucci |
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Publication Detail:
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Type: Journal Article; Research Support, U.S. Gov't, Non-P.H.S. |
Journal Detail:
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Title: IEEE transactions on pattern analysis and machine intelligence Volume: 30 ISSN: 0162-8828 ISO Abbreviation: IEEE Trans Pattern Anal Mach Intell Publication Date: 2008 May |
Date Detail:
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Created Date: 2008-03-28 Completed Date: 2008-06-10 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9885960 Medline TA: IEEE Trans Pattern Anal Mach Intell Country: United States |
Other Details:
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Languages: eng Pagination: 851-64 Citation Subset: IM |
Affiliation:
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School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. ganeshs@ece.gatech.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Artificial Intelligence* Image Enhancement / methods Image Interpretation, Computer-Assisted / methods* Motion Pattern Recognition, Automated / methods* Subtraction Technique* Video Recording / methods* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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