Component based recognition of objects in an office environment
We present a component-based approach for recognizing objects under large pose changes. From a set of training images of a given object we extract a large number of components which are clustered based on the similarity of their image features and their locations within the object image. The cluster...
| Main Authors: | , |
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| Language: | en_US |
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2004
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/7279 |
| _version_ | 1826213377528037376 |
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| author | Morgenstern, Christian Heisele, Bernd |
| author_facet | Morgenstern, Christian Heisele, Bernd |
| author_sort | Morgenstern, Christian |
| collection | MIT |
| description | We present a component-based approach for recognizing objects under large pose changes. From a set of training images of a given object we extract a large number of components which are clustered based on the similarity of their image features and their locations within the object image. The cluster centers build an initial set of component templates from which we select a subset for the final recognizer. In experiments we evaluate different sizes and types of components and three standard techniques for component selection. The component classifiers are finally compared to global classifiers on a database of four objects. |
| first_indexed | 2024-09-23T15:48:08Z |
| id | mit-1721.1/7279 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:48:08Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/72792019-04-15T00:40:27Z Component based recognition of objects in an office environment Morgenstern, Christian Heisele, Bernd AI computer vision object recognition component object recognition We present a component-based approach for recognizing objects under large pose changes. From a set of training images of a given object we extract a large number of components which are clustered based on the similarity of their image features and their locations within the object image. The cluster centers build an initial set of component templates from which we select a subset for the final recognizer. In experiments we evaluate different sizes and types of components and three standard techniques for component selection. The component classifiers are finally compared to global classifiers on a database of four objects. 2004-10-20T21:05:16Z 2004-10-20T21:05:16Z 2003-11-28 AIM-2003-024 CBCL-232 http://hdl.handle.net/1721.1/7279 en_US AIM-2003-024 CBCL-232 12 p. 3572823 bytes 962401 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | AI computer vision object recognition component object recognition Morgenstern, Christian Heisele, Bernd Component based recognition of objects in an office environment |
| title | Component based recognition of objects in an office environment |
| title_full | Component based recognition of objects in an office environment |
| title_fullStr | Component based recognition of objects in an office environment |
| title_full_unstemmed | Component based recognition of objects in an office environment |
| title_short | Component based recognition of objects in an office environment |
| title_sort | component based recognition of objects in an office environment |
| topic | AI computer vision object recognition component object recognition |
| url | http://hdl.handle.net/1721.1/7279 |
| work_keys_str_mv | AT morgensternchristian componentbasedrecognitionofobjectsinanofficeenvironment AT heiselebernd componentbasedrecognitionofobjectsinanofficeenvironment |