Attentional Selection in Object Recognition
A key problem in object recognition is selection, namely, the problem of identifying regions in an image within which to start the recognition process, ideally by isolating regions that are likely to come from a single object. Such a selection mechanism has been found to be crucial in reducing...
| Main Authors: | , |
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| Language: | en_US |
| Published: |
2004
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| Online Access: | http://hdl.handle.net/1721.1/7049 |
| _version_ | 1826197440923959296 |
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| author | Tanveer, S. Mahmood, F. |
| author_facet | Tanveer, S. Mahmood, F. |
| author_sort | Tanveer, S. |
| collection | MIT |
| description | A key problem in object recognition is selection, namely, the problem of identifying regions in an image within which to start the recognition process, ideally by isolating regions that are likely to come from a single object. Such a selection mechanism has been found to be crucial in reducing the combinatorial search involved in the matching stage of object recognition. Even though selection is of help in recognition, it has largely remained unsolved because of the difficulty in isolating regions belonging to objects under complex imaging conditions involving occlusions, changing illumination, and object appearances. This thesis presents a novel approach to the selection problem by proposing a computational model of visual attentional selection as a paradigm for selection in recognition. In particular, it proposes two modes of attentional selection, namely, attracted and pay attention modes as being appropriate for data and model-driven selection in recognition. An implementation of this model has led to new ways of extracting color, texture and line group information in images, and their subsequent use in isolating areas of the scene likely to contain the model object. Among the specific results in this thesis are: a method of specifying color by perceptual color categories for fast color region segmentation and color-based localization of objects, and a result showing that the recognition of texture patterns on model objects is possible under changes in orientation and occlusions without detailed segmentation. The thesis also presents an evaluation of the proposed model by integrating with a 3D from 2D object recognition system and recording the improvement in performance. These results indicate that attentional selection can significantly overcome the computational bottleneck in object recognition, both due to a reduction in the number of features, and due to a reduction in the number of matches during recognition using the information derived during selection. Finally, these studies have revealed a surprising use of selection, namely, in the partial solution of the pose of a 3D object. |
| first_indexed | 2024-09-23T10:47:42Z |
| id | mit-1721.1/7049 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:47:42Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/70492019-04-10T11:52:27Z Attentional Selection in Object Recognition Tanveer, S. Mahmood, F. A key problem in object recognition is selection, namely, the problem of identifying regions in an image within which to start the recognition process, ideally by isolating regions that are likely to come from a single object. Such a selection mechanism has been found to be crucial in reducing the combinatorial search involved in the matching stage of object recognition. Even though selection is of help in recognition, it has largely remained unsolved because of the difficulty in isolating regions belonging to objects under complex imaging conditions involving occlusions, changing illumination, and object appearances. This thesis presents a novel approach to the selection problem by proposing a computational model of visual attentional selection as a paradigm for selection in recognition. In particular, it proposes two modes of attentional selection, namely, attracted and pay attention modes as being appropriate for data and model-driven selection in recognition. An implementation of this model has led to new ways of extracting color, texture and line group information in images, and their subsequent use in isolating areas of the scene likely to contain the model object. Among the specific results in this thesis are: a method of specifying color by perceptual color categories for fast color region segmentation and color-based localization of objects, and a result showing that the recognition of texture patterns on model objects is possible under changes in orientation and occlusions without detailed segmentation. The thesis also presents an evaluation of the proposed model by integrating with a 3D from 2D object recognition system and recording the improvement in performance. These results indicate that attentional selection can significantly overcome the computational bottleneck in object recognition, both due to a reduction in the number of features, and due to a reduction in the number of matches during recognition using the information derived during selection. Finally, these studies have revealed a surprising use of selection, namely, in the partial solution of the pose of a 3D object. 2004-10-20T20:23:48Z 2004-10-20T20:23:48Z 1993-01-01 AITR-1420 http://hdl.handle.net/1721.1/7049 en_US AITR-1420 34420060 bytes 27940889 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Tanveer, S. Mahmood, F. Attentional Selection in Object Recognition |
| title | Attentional Selection in Object Recognition |
| title_full | Attentional Selection in Object Recognition |
| title_fullStr | Attentional Selection in Object Recognition |
| title_full_unstemmed | Attentional Selection in Object Recognition |
| title_short | Attentional Selection in Object Recognition |
| title_sort | attentional selection in object recognition |
| url | http://hdl.handle.net/1721.1/7049 |
| work_keys_str_mv | AT tanveers attentionalselectioninobjectrecognition AT mahmoodf attentionalselectioninobjectrecognition |