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dc.contributor.authorSung, Kah-Kayen_US
dc.date.accessioned2004-10-20T14:45:19Z
dc.date.available2004-10-20T14:45:19Z
dc.date.issued1996-03-13en_US
dc.identifier.otherAITR-1572en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6774
dc.description.abstractThis thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach first builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. We propose an {em active learning} formulation for function approximation, and show for three specific approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. We then simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems.en_US
dc.format.extent195 p.en_US
dc.format.extent20467529 bytes
dc.format.extent2831164 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAITR-1572en_US
dc.subjectAIen_US
dc.subjectMITen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputer Visionen_US
dc.subjectFace Detectionen_US
dc.subjectObject Detectionen_US
dc.subjectExample-based Learningen_US
dc.subjectActive Learningen_US
dc.titleLearning and Example Selection for Object and Pattern Detectionen_US


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