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EnglishThis paper presents a novel approach for detecting multiple instances of the same object for pick-and-place automation. The working conditions are very challenging, with complex objects, arranged at random in the scene, and heavily occluded. This approach exploits SIFT to obtain a set of correspondences between the object model and the current image. In order to segment the multiple instances of the object, the correspondences are clustered among the objects using a voting scheme which determines the best estimate of the object's center through mean shift. This procedure is compared in terms of accuracy with existing homography-based solutions which make use of RANSAC to eliminate outliers in the homography estimation.
| Titolo: | Multiple object detection for pick-and-place applications |
| Autori: | |
| Data di pubblicazione: | 2009 |
| Handle: | http://hdl.handle.net/11381/2888464 |
| Appare nelle tipologie: | 4.1b Atto convegno Volume |
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