Document Detail


Minimum Near-Convex Shape Decomposition.
MedLine Citation:
PMID:  23546996     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Shape decomposition is a fundamental problem for part-based shape representation. We propose the Minimum Near-Convex Decomposition (MNCD) to decompose arbitrary shapes into minimum number of "near-convex" parts. The near-convex shape decomposition is formulated as a discrete optimization problem by minimizing the number of non-intersecting cuts. Two perception rules are imposed as constraints into our objective function to improve the visual naturalness of the decomposition. With the degree of near-convexity a user specified parameter, our decomposition is robust to local distortions and shape deformation. The optimization can be efficiently solved via Binary Integer Linear Programming. Both theoretical analysis and experiment results show that our approach outperforms the state-of-the-art results without introducing redundant parts, and thus leads to robust shape representation.
Authors:
Zhou Ren; Junsong Yuan; Wenyu Liu
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-3-27
Journal Detail:
Title:  IEEE transactions on pattern analysis and machine intelligence     Volume:  -     ISSN:  1939-3539     ISO Abbreviation:  IEEE Trans Pattern Anal Mach Intell     Publication Date:  2013 Mar 
Date Detail:
Created Date:  2013-4-2     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9885960     Medline TA:  IEEE Trans Pattern Anal Mach Intell     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Nanyang Technological University, Singapore.
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