Distance-based Global Descriptors for Multi-view Object Recognition
The paper reports on a new multi-view algorithm that combines information from multiple images of a single target object, captured at different distances, to determine the identity of an object. Due to the use of global feature descriptors, the method does not involve image segmentation. The perform...
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| Veröffentlicht in: | Robotica 2020-01, Vol.38 (1), p.106-117 |
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| container_end_page | 117 |
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| container_title | Robotica |
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| creator | Kannappan, Prasanna Tanner, Herbert G. |
| description | The paper reports on a new multi-view algorithm that combines information from multiple images of a single target object, captured at different distances, to determine the identity of an object. Due to the use of global feature descriptors, the method does not involve image segmentation. The performance of the algorithm has been evaluated on a binary classification problem for a data set consisting of a series of underwater images. |
| doi_str_mv | 10.1017/S0263574719000493 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 0263-5747 |
| ispartof | Robotica, 2020-01, Vol.38 (1), p.106-117 |
| issn | 0263-5747 1469-8668 |
| language | eng |
| recordid | cdi_proquest_journals_2320906668 |
| source | Cambridge University Press Journals Complete |
| subjects | Algorithms Image segmentation Object recognition |
| title | Distance-based Global Descriptors for Multi-view Object Recognition |
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