Abstract—This paper proposes a simple yet effective approach for segmenting multiple instances of the same object for a pick-and-place application. The considered objects present several challenges, such as low texture, semi-transparent container, moving parts, and severe occlusions. Real-time constraints must be met, calling for a good trade-off between accuracy and efficiency. For all these reasons, the proposed approach is based on SIFT features and a suitable modification of the 2NN matching procedure to increase the number of available matches. Moreover, in order to reduce false segmentations, ad-hoc algorithms based on overlap detection and color similarity are used.
Index Terms—Machine vision, pick-and-place automation, object multiple instance detection, SIFT.
Paolo Piccinini is with Department of Information Engineering, University of Modena and Reggio Emilia, Strada Vignolese, 905, I-41125 Modena, Italy (email: email@example.com)
Andrea Prati is with Department of Design and Planning of Complex Environments, University IUAV of Venice, Santa Croce 1957, I-30135 Venice, Italy (email: firstname.lastname@example.org)
Rita Cucchiara is with Department of Engineering “Enzo Ferrari”,University of Modena and Reggio Emilia, Strada Vignolese, 905, I-41125 Modena, Italy (email: email@example.com)
Cite: Paolo Piccinini, Andrea Prati, and Rita Cucchiara, "SIFT-based Segmentation of Multiple Instances of Low-Textured Objects," International Journal of Computer Theory and Engineering vol. 5, no. 1, pp. 41-46, 2013.