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Disparity energy model with keypoint disparity validation

dc.contributor.authorFarrajota, Miguel
dc.contributor.authorMartins, J. C.
dc.contributor.authorRodrigues, J. M. F.
dc.contributor.authordu Buf, J. M. H.
dc.date.accessioned2013-01-16T13:34:42Z
dc.date.available2013-01-16T13:34:42Z
dc.date.issued2011-10
dc.date.updated2012-12-27T15:58:25Z
dc.description.abstractA biological disparity energy model can estimate local depth information by using a population of V1 complex cells. Instead of applying an analytical model which explicitly involves cell parameters like spatial frequency, orientation, binocular phase and position difference, we developed a model which only involves the cells’ responses, such that disparity can be extracted from a population code, using only a set of previously trained cells with random-dot stereograms of uniform disparity. Despite good results in smooth regions, the model needs complementary processing, notably at depth transitions. We therefore introduce a new model to extract disparity at keypoints such as edge junctions, line endings and points with large curvature. Responses of end-stopped cells serve to detect keypoints, and those of simple cells are used to detect orientations of their underlying line and edge structures. Annotated keypoints are then used in the leftright matching process, with a hierarchical, multi-scale tree structure and a saliency map to segregate disparity. By combining both models we can (re)define depth transitions and regions where the disparity energy model is less accurate.por
dc.identifier.citationMiguel Farrajota; Martins, J.C.; Rodrigues, J.M.F.; du Buf, J.M.H. Disparity energy model with keypoint disparity validation, Trabalho apresentado em Portuguese Conf. on Pattern Recognition, In Proc. 17th Portuguese Conf. on Pattern Recognition, Porto, Portugal, 28 Oct., Porto, 2011por
dc.identifier.otherAUT: JRO00913; DUB00865;
dc.identifier.urihttp://hdl.handle.net/10400.1/2097
dc.language.isoengpor
dc.relationA neuro-dynamic framework for cognitive robotics: scene representations, behavioural sequences, and learning.
dc.subjectVisão humanapor
dc.titleDisparity energy model with keypoint disparity validationpor
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleA neuro-dynamic framework for cognitive robotics: scene representations, behavioural sequences, and learning.
oaire.awardURIinfo:eu-repo/grantAgreement/EC/FP7/270247/EU
oaire.citation.conferencePlacePortopor
oaire.citation.title17th Portuguese Conference on Pattern Recognitionpor
oaire.fundingStreamFP7
person.familyNameFarrajota
person.familyNameRodrigues
person.familyNamedu Buf
person.givenNameMiguel
person.givenNameJoao
person.givenNameHans
person.identifier.ciencia-id8A19-98F7-9914
person.identifier.orcid0000-0001-7970-4649
person.identifier.orcid0000-0002-3562-6025
person.identifier.orcid0000-0002-4345-1237
person.identifier.ridM-5125-2013
person.identifier.scopus-author-id55807461600
person.identifier.scopus-author-id6604075916
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
rcaap.rightsopenAccesspor
rcaap.typearticlepor
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relation.isAuthorOfPublication683ba85b-459c-4789-a4ff-a4e2a904b295
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