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Winding number constrained contour detection

dc.contributor.authorMing, Yansheng
dc.contributor.authorLi, Hongdong
dc.contributor.authorHe, Xuming
dc.date.accessioned2015-12-13T22:34:44Z
dc.date.issued2015
dc.date.updated2015-12-11T09:23:48Z
dc.description.abstractSalient contour detection can benefit from the integration of both contour cues and region cues. However, this task is difficult due to different nature of region representations and contour representations. To solve this problem, this paper proposes an energy minimization framework based on winding number constraints. In this framework, both region cues, such as color/texture homogeneity, and contour cues, such as local contrast and continuity, are represented in a joint objective function, which has both region and contour labels. The key problem is how to design constraints that ensure the topological consistency of the two kinds of labels. Our technique is based on the topological concept of winding number. Using a fast method for winding number computation, a small number of linear constraints are derived to ensure label consistency. Our method is instantiated by ratio-based energy functions. By successfully integrating both region and contour cues, our method shows advantages over competitive methods. Our method is extended to incorporate user interaction, which leads to further improvements.
dc.identifier.issn1057-7149
dc.identifier.urihttp://hdl.handle.net/1885/76261
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.sourceIEEE Transactions on Image Processing
dc.titleWinding number constrained contour detection
dc.typeJournal article
local.bibliographicCitation.issue1
local.bibliographicCitation.lastpage79
local.bibliographicCitation.startpage68
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, College of Engineering and Computer Science, ANU
local.contributor.authoruidMing, Yansheng, u4873509
local.contributor.authoruidLi, Hongdong, u4056952
local.contributor.authoruidHe, Xuming, u4981609
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor080100 - ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationU3488905xPUB5106
local.identifier.citationvolume24
local.identifier.doi10.1109/TIP.2014.2372636
local.identifier.scopusID2-s2.0-84917692781
local.type.statusPublished Version

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