doi: 10.7763/IJCTE.2013.V5.643
SIFT-based Segmentation of Multiple Instances of Low-Textured Objects
Paolo Piccinini1
,
Andrea Prati2 ,
Rita Cucchiara3
- 1Department of Information Engineering, University of Modena and Reggio Emilia, Strada Vignolese, 905, I-41125 Modena, Italy
- 2Department of Design and Planning of Complex Environments, University IUAV of Venice, Santa Croce 1957, I-30135 Venice, Italy
- 3Department of Engineering “Enzo Ferrari”,University of Modena and Reggio Emilia, Strada Vignolese, 905, I-41125 Modena, Italy
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.
Keywords
- Machine vision
- pick-and-place automation
- object multiple instance detection
- SIFT
How to 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. https://doi.org/10.7763/IJCTE.2013.V5.643
Copyright & License
Copyright © 2013 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).