Multiple Object Segmentation for Pick-and-Place Applications

Abstract: This paper presents a novel approach for detecting multipleinstances 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.

Citation:

Piccinini, Paolo; Prati, Andrea; Cucchiara, Rita "Multiple Object Segmentation for Pick-and-Place Applications" Proceedings of IAPR CONFERENCE ON MACHINE VISION APPLICATIONS MVA2009, Yokohama, Japan, pp. 361 -366 , May 20-22, 2009, 2009

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