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dc.contributor.authorChong, C.
dc.contributor.authorTan, Tele
dc.contributor.authorLim, Fee-Lee
dc.contributor.editorY.Y. Tang
dc.contributor.editorG. Lorette
dc.contributor.editorD.S. Young
dc.contributor.editorH. Yang
dc.identifier.citationChong, C. and Tan, T. and Lim, F. 2006. A model-based approach for rigid object recognition, in Tang, Y.Y. et al (ed), 18th International Conference on Pattern Recognition, Aug 20-24 2006, pp. 116-120. Hong Kong: IEEE.

Most object recognition systems require large databases of real images for classifier training. To collect real images for this purpose is a difficult and expensive process. This paper introduces a unified framework based on the creation and use of synthetic images for training various classifiers to achieve recognition of real-world objects. A 3D model of the object (i.e. trolley in this case) is constructed from a minimum of two photographs. The constructed 3D model is used to automatically generate the relevant synthetic images that are subsequently used to train the Adaboost and support vector machine-based recognition systems. Experimental results obtained are very encouraging suggesting that synthetically generated images generated by our approach can augment the real training samples used in current recognition systems

dc.publisherIEEE Coputer Society Conference Publishing Services
dc.titleA model-based approach for rigid object recognition
dc.typeConference Paper
dcterms.source.titleProceedings of the 18th International Conference on Pattern Recognition Vol 3
dcterms.source.seriesProceedings of the 18th International Conference on Pattern Recognition Vol 3
dcterms.source.conference8th International Conference on Pattern Recognition
dcterms.source.conference-start-dateAug 20 2006
dcterms.source.conferencelocationHong Kong
dcterms.source.placeLos Alamitos, USA
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available

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