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dc.contributor.authorYilmaz, O.en_US
dc.contributor.authorDoerschner, K.en_US
dc.date.accessioned2016-02-08T11:02:29Z
dc.date.available2016-02-08T11:02:29Z
dc.date.issued2014en_US
dc.identifier.issn0932-8092
dc.identifier.urihttp://hdl.handle.net/11693/26621
dc.description.abstractSuccessful identification of specularities in an image can be crucial for an artificial vision system when extracting the semantic content of an image or while interacting with the environment. We developed an algorithm that relies on scale and rotation invariant feature extraction techniques and uses motion cues to detect and localize specular surfaces. Appearance change in feature vectors is used to quantify the appearance distortion on specular surfaces, which has previously been shown to be a powerful indicator for specularity (Doerschner et al. in Curr Biol, 2011). The algorithm combines epipolar deviations (Swaminathan et al. in Lect Notes Comput Sci 2350:508–523, 2002) and appearance distortion, and succeeds in localizing specular objects in computer-rendered and real scenes, across a wide range of camera motions and speeds, object sizes and shapes, and performs well under image noise and blur conditions.en_US
dc.source.titleMachine Vision and Applicationsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s00138-014-0610-9en_US
dc.subjectImage motionen_US
dc.subjectSpecularity detectionen_US
dc.subjectSurface reflectance estimationen_US
dc.titleDetection and localization of specular surfaces using image motion cuesen_US
dc.typeArticleen_US
dc.departmentDepartment of Psychologyen_US
dc.citation.spage1333en_US
dc.citation.epage1349en_US
dc.citation.volumeNumber25en_US
dc.citation.issueNumber5en_US
dc.identifier.doi10.1007/s00138-014-0610-9en_US
dc.publisherSpringeren_US
dc.identifier.eissn1432-1769


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