A model-based approach for rigid object recognition
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 t...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Coputer Society Conference Publishing Services
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/18574 |
| _version_ | 1848749784177836032 |
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| author | Chong, C. Tan, Tele Lim, Fee-Lee |
| author2 | Y.Y. Tang |
| author_facet | Y.Y. Tang Chong, C. Tan, Tele Lim, Fee-Lee |
| author_sort | Chong, C. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 |
| first_indexed | 2025-11-14T07:26:26Z |
| format | Conference Paper |
| id | curtin-20.500.11937-18574 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:26:26Z |
| publishDate | 2006 |
| publisher | IEEE Coputer Society Conference Publishing Services |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-185742022-10-27T07:56:24Z A model-based approach for rigid object recognition Chong, C. Tan, Tele Lim, Fee-Lee Y.Y. Tang S.P.Wang G. Lorette D.S. Young H. Yang 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 2006 Conference Paper http://hdl.handle.net/20.500.11937/18574 10.1109/ICPR.2006.103 IEEE Coputer Society Conference Publishing Services restricted |
| spellingShingle | Chong, C. Tan, Tele Lim, Fee-Lee A model-based approach for rigid object recognition |
| title | A model-based approach for rigid object recognition |
| title_full | A model-based approach for rigid object recognition |
| title_fullStr | A model-based approach for rigid object recognition |
| title_full_unstemmed | A model-based approach for rigid object recognition |
| title_short | A model-based approach for rigid object recognition |
| title_sort | model-based approach for rigid object recognition |
| url | http://hdl.handle.net/20.500.11937/18574 |