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dc.contributor.advisorJeffrey H. Shapiro and Keh-Ping Dunn.en_US
dc.contributor.authorBlum, Matthew D. (Matthew David), 1976-en_US
dc.date.accessioned2005-08-22T18:28:51Z
dc.date.available2005-08-22T18:28:51Z
dc.date.copyright1999en_US
dc.date.issued1999en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/9453
dc.descriptionThesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.en_US
dc.descriptionIncludes bibliographical references (p. 95-97).en_US
dc.description.abstractThe problem of automatically recognizing an object in an image scene is very difficult. This thesis develops an image-based object recognition algorithm in which information from different features is combined using Dempster-Shafer reasoning. Specific attention is paid to cases in which only partial information is available because of occlusion or sensor limitations. The structure of the recognition system developed herein is as follows. First, some image processing techniques are used to filter out noise, detect edges, and find features in the raw image, Further preprocessing is performed to isolate objects of interest. Finally, Dempster-Shafer reasoning is used to combine evidence from the edge features into a working model of the objects seen in the raw image. The preceding object recognition system was tested on simulated data, dealing with sets of geometrical shapes, Two experiments were performed, one with un-occluded objects, and one with up to four occluded objects in each raw image. Its performance was compared to the Bayesian approach and human classification. Although Dempster-Shafer reasoning did not outperform human reasoning, it did perform considerably better than the Bayesian approach.en_US
dc.description.statementofresponsibilityby Matthew D. Blum.en_US
dc.format.extent97 p.en_US
dc.format.extent6462775 bytes
dc.format.extent6462534 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectElectrical Engineering and Computer Scienceen_US
dc.titleAutomatic target recognition based on collection of evidenceen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc43440908en_US


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