A study on discrimination of SIFT feature applied to binary images

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dc.contributor.authorSetitra, Insaf
dc.contributor.authorLarabi, Slimane
dc.date.accessioned2015-10-05T13:02:40Z
dc.date.available2015-10-05T13:02:40Z
dc.date.issued2015-10-04
dc.description.abstractScale Invariant Feature Transform (SIFT) since its first apparition in 2004 has been (and still is) extensively used in computer vision to classify and match objects in RGB and grey level images and videos. However, since the descriptor used in SIFT approach is based on gradient magnitude and orientation, it has always been considered as texture feature and received less interest when treating binary images. In this work we investigate the power of discrimination of SIFT applied to binary images. A theoretical and experimental studies show that SIFT can still describe shapes and can be used to distinguish objects of several classes.fr_FR
dc.identifier.isrnCERIST- DSISM/PR-15-000000030--dzfr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/775
dc.publisherCERIST
dc.relation.ispartofRapports de recherche internes
dc.relation.placeAlger
dc.structureSystèmes d'Information et Image en Santé S2ISfr_FR
dc.subjectClassificationfr_FR
dc.subjectMatchingfr_FR
dc.subjectSIFTfr_FR
dc.subjectShape discriminationfr_FR
dc.titleA study on discrimination of SIFT feature applied to binary imagesfr_FR
dc.typeTechnical Report
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