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dc.contributor.authorLiu, Lifengen_US
dc.contributor.authorSclaroff, Stanen_US
dc.date.accessioned2011-10-20T04:37:48Z
dc.date.available2011-10-20T04:37:48Z
dc.date.issued1997-11-24
dc.identifier.citationLiu, Lifeng; Sclaroff, Stan. "Color Region Grouping and Shape Recognition with Deformable Models", Technical Report BUCS-1997-019, Computer Science Department, Boston University, November 24, 1997. [Available from: http://hdl.handle.net/2144/1619]
dc.identifier.urihttps://hdl.handle.net/2144/1619
dc.description.abstractA new deformable shape-based method for color region segmentation is described. The method includes two stages: over-segmentation using a traditional color region segmentation algorithm, followed by deformable model-based region merging via grouping and hypothesis selection. During the second stage, region merging and object identification are executed simultaneously. A statistical shape model is used to estimate the likelihood of region groupings and model hypotheses. The prior distribution on deformation parameters is precomputed using principal component analysis over a training set of region groupings. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with similarly colored adjacent objects. Furthermore, the recovered parametric shape model can be used directly in object recognition and comparison. Experiments in segmentation and image retrieval are reported.en_US
dc.language.isoen_US
dc.publisherBoston University Computer Science Departmenten_US
dc.relation.ispartofseriesBUCS Technical Reports;BUCS-TR-1997-019
dc.titleColor Region Grouping and Shape Recognition with Deformable Modelsen_US
dc.typeTechnical Reporten_US


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