Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/19661
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dc.contributor.authorSuganthan P. N.en_US
dc.date.accessioned2009-12-14T06:20:24Z-
dc.date.available2009-12-14T06:20:24Z-
dc.date.copyright1996en_US
dc.date.issued1996-
dc.identifier.urihttp://hdl.handle.net/10356/19661-
dc.description.abstractThis research work describes in depth investigation into optimising connectionist models and their applications in rigid object and pattern recognition by attributed relational graph (ARG) matching. The ARG representation is chosen because it encodes relational semantic information in itself and performs well under clutter and partial occlusion. The matching of model and scene ARGs is performed using optimising con-nectionist models. Since the connectionist models offer parallel and distributed process-ing, and cost effective hardware implementation, optimising connectionist model-based recognition systems can be employed to solve practical recognition problems.en_US
dc.format.extent230 p.-
dc.language.isoen-
dc.rightsNANYANG TECHNOLOGICAL UNIVERSITYen_US
dc.subjectDRNTU::Engineeringen_US
dc.titleOptimising connectionist models and attributed relational graph matching for object recognitionen_US
dc.typeThesisen_US
dc.contributor.supervisorTeoh, Earn Khwangen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeDoctor of Philosophy (EEE)en_US
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