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dc.contributor.authorHaker, Steven
dc.contributor.authorSapiro, Guillermo
dc.contributor.authorTannenbaum, Allen R.
dc.date.accessioned2009-05-21T19:15:47Z
dc.date.available2009-05-21T19:15:47Z
dc.date.issued2000-02
dc.identifier.citationSteven Haker, Guillermo Sapiro, and Allen Tannenbaum, "Knowledge-based segmentation of SAR data with learned priors," IEEE Transactions on Image Processing, Vol. 9, No. 2, February 2000, 299-301en
dc.identifier.issn1057-7149
dc.identifier.urihttp://hdl.handle.net/1853/27938
dc.description©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en
dc.descriptionDOI: 10.1109/83.821747
dc.description.abstractAn approach for the segmentation of still and video synthetic aperture radar (SAR) images is described in this note. A priori knowledge about the objects present in the image, e.g., target, shadow, and background terrain, is introduced via Bayes' rule. Posterior probabilities obtained in this way are then anisotropically smoothed, and the image segmentation is obtained via MAP classifications of the smoothed data. When segmenting sequences of images, the smoothed posterior probabilities of past frames are used to learn the prior distributions in the succeeding frame. We show with examples from public data sets that this method provides an efficient and fast technique for addressing the segmentation of SAR data.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectAnisotropic diffusionen
dc.subjectBayes' ruleen
dc.subjectKnowledgeen
dc.subjectLearning segmentationen
dc.subjectSynthetic Aperture Radar (SAR)en
dc.titleKnowledge-based segmentation of SAR data with learned priorsen
dc.typeArticleen
dc.contributor.corporatenameUniversity of Minnesota. School of Mathematics
dc.contributor.corporatenameUniversity of Minnesota. Dept. of Electrical and Computer Engineering
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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