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Symmetry detection via contour grouping

Ming, Yansheng; Li, Hongdong; He, Xuming

Description

This paper presents a simple but effective model for detecting the symmetric axes of bilaterally symmetric objects in unsegmented natural scene images. Our model constructs a directed graph of symmetry interaction. Every node in the graph represents a matched pair of features, and every directed edge represents the interaction between nodes. The bilateral symmetry detection problem is then formulated as finding the star subgraph with maximal weight. The star structure ensures the consistency...[Show more]

dc.contributor.authorMing, Yansheng
dc.contributor.authorLi, Hongdong
dc.contributor.authorHe, Xuming
dc.coverage.spatialMelbourne Australia
dc.date.accessioned2015-12-10T23:09:52Z
dc.date.createdSeptember 15-18 2013
dc.identifier.isbn9781479923410
dc.identifier.urihttp://hdl.handle.net/1885/63479
dc.description.abstractThis paper presents a simple but effective model for detecting the symmetric axes of bilaterally symmetric objects in unsegmented natural scene images. Our model constructs a directed graph of symmetry interaction. Every node in the graph represents a matched pair of features, and every directed edge represents the interaction between nodes. The bilateral symmetry detection problem is then formulated as finding the star subgraph with maximal weight. The star structure ensures the consistency between grouped nodes while the optimal star subgraph can be found in polynomial time. Our model makes prediction based on contour cue: each node in the graph represents a pair of edge segments. Compared with the Loy and Eklundh's method which used SIFT feature, our model can often produce better results for the images containing limited texture. This advantage is demonstrated on two natural scene image sets.
dc.publisherIEEE
dc.relation.ispartofseries2013 20th IEEE International Conference on Image Processing, ICIP 2013
dc.source2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
dc.titleSymmetry detection via contour grouping
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2013
local.identifier.absfor080100 - ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING
local.identifier.ariespublicationU3488905xPUB812
local.type.statusPublished Version
local.contributor.affiliationMing, Yansheng, College of Engineering and Computer Science, ANU
local.contributor.affiliationLi, Hongdong, College of Engineering and Computer Science, ANU
local.contributor.affiliationHe, Xuming, National ICT Australia
local.description.embargo2037-12-31
local.bibliographicCitation.startpage4259
local.bibliographicCitation.lastpage4263
local.identifier.doi10.1109/ICIP.2013.6738877
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2015-12-10T09:14:45Z
local.identifier.scopusID2-s2.0-84897748175
CollectionsANU Research Publications

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