Probabilistic contextual models for Object class recognition in uncontrived images.

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dc.contributor.authorHasanat, Mozaherul Hoque Abul
dc.date.accessioned2018-11-09T02:12:04Z
dc.date.available2018-11-09T02:12:04Z
dc.date.issued2011-05
dc.description.abstractKonteks merupakan suatu elemen penting dalam mendapatkan penjelasan yang bererti untuk sesuatu imej bagi kedua-dua sistem visual biologi dan buatan. Tesis ini mencadangkan permodelan hubungan konteks di antara objek dunia nyata di dalam imej yang tidak dibuat-buat bagi meningkatkan prestasi pengecaman kelas objek. Context is a vital element in deriving meaningful explanation of an image for both biological, as well as, artificial vision systems. This thesis proposes to model contextual relation among real-world objects in uncontrived images in order to improve object class recognition performance.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/7038
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectContextual modelen_US
dc.subjectUncontrived imagesen_US
dc.titleProbabilistic contextual models for Object class recognition in uncontrived images.en_US
dc.typeThesisen_US
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Pusat Pengajian Sains Komputer - Tesis