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Unsupervised clustering in Hough space for identification of partially occluded objects

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dc.contributor.authorYáñez-Suárez, Oscar, author
dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:18:38Z
dc.date.available2007-01-03T04:18:38Z
dc.date.issued1999
dc.description.abstractAn automated approach for template-free identification of partially occluded objects is presented. The contour of each relevant object in the analyzed scene is modeled with an approximating polygon whose edges are then projected into the Hough space. A structurally adaptive self-organizing map neural network generates clusters of collinear and/or parallel edges, which are used as the basis for identifying the partially occluded objects within each polygonal approximation. Results on a number of cases under different conditions are provided.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationYáñez-Suárez, Oscar and Mahmood R. Azimi-Sadjadi, Unsupervised Clustering in Hough Space for Identification of Partially Occluded Objects, IEEE Transactions on Pattern Analysis and Machine Intelligence 21, no. 9 (September 1999): 946-950.
dc.identifier.urihttp://hdl.handle.net/10217/989
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©1999 IEEE.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectHough space
dc.subjectunsupervised clustering SOM network
dc.subjectimage analysis
dc.subjectoccluded objects
dc.titleUnsupervised clustering in Hough space for identification of partially occluded objects
dc.typeText

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