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dc.contributor.supervisor Peters, James F. (Electrical and Computer Engineering) en_US
dc.contributor.author Shahfar, Shabnam
dc.date.accessioned 2011-09-27T15:24:47Z
dc.date.available 2011-09-27T15:24:47Z
dc.date.issued 2011-09-27
dc.identifier.uri http://hdl.handle.net/1993/4941
dc.description.abstract This thesis represents a tolerance near set approach to detect similarity between digital images. Two images are considered as sets of perceptual objects and a tolerance relation defines the nearness between objects. Two perceptual objects resemble each other if the difference between their descriptions is smaller than a tolerable level of error. Existing tolerance near set approaches to image similarity consider both images in a single tolerance space and compare the size of tolerance classes. This approach is shown to be sensitive to noise and distortions. In this thesis, a new tolerance-based method is proposed that considers each image in a separate tolerance space and defines the similarity based on differences between histograms of the size of tolerance classes. The main advantage of the proposed method is its lower sensitivity to distortions such as adding noise, darkening or brightening. This advantage has been shown here through a set of experiments. en_US
dc.rights info:eu-repo/semantics/openAccess
dc.subject Image similarity measures en_US
dc.subject Tolerance spaces en_US
dc.subject Near images en_US
dc.subject Nearness measures en_US
dc.title Near Images: A Tolerance Based Approach to Image Similarity and its Robustness to Noise and Lightening en_US
dc.type info:eu-repo/semantics/masterThesis
dc.type master thesis en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.contributor.examiningcommittee Hossain, Ekram (Electrical and Computer Engineering) Blatz, James (Civil Engineering) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2011 en_US


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