Document Detail


Large Disparity Motion Layer Extraction via Topological Clustering.
MedLine Citation:
PMID:  20876022     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
In this paper, we present a robust and efficient approach to extract motion layers from a pair of images with large disparity motion. First, motion models are established as: 1) initial SIFT matches are obtained and grouped into a set of clusters using our developed topological clustering algorithm; 2) for each cluster with no less than three matches, an affine transformation is estimated with least-square solution as tentative motion model; and 3) the tentative motion models are refined and the invalid models are pruned. Then, with the obtained motion models, a graph cuts based layer assignment algorithm is employed to segment the scene into several motion layers. Experimental results demonstrate that our method can successfully segment scenes containing objects with large interframe motion or even with significant interframe scale and pose changes. Furthermore, compared with the previous method invented by Wills and its modified version, our method is much faster and more robust.
Authors:
Yongtao Wang; Junbin Gong; Dazhi Zhang; Chenqiang Gao; Jinwen Tian; Huanqiang Zeng
Publication Detail:
Type:  Journal Article     Date:  2010-09-27
Journal Detail:
Title:  IEEE transactions on image processing : a publication of the IEEE Signal Processing Society     Volume:  20     ISSN:  1941-0042     ISO Abbreviation:  IEEE Trans Image Process     Publication Date:  2011 Jan 
Date Detail:
Created Date:  2010-12-21     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9886191     Medline TA:  IEEE Trans Image Process     Country:  United States    
Other Details:
Languages:  eng     Pagination:  43-52     Citation Subset:  IM    
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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