Abstract
GrabCut is a renowned algorithm for image segmentation. It exploits iteratively the combinatorial minimization of energy function as introduced in graph-cut methods, to achieve background foreground classification with fewer user’s interaction. In this paper it is proposed to extend GrabCut to carry out segmentation on RGB-D point clouds, based both on appearance and geometrical criteria. It is shown that an hybrid GrabCut method combining RGB and D information, is more efficient than GrabCut based only on RGB or D images.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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Sallem, N.K., Devy, M. (2013). Extended GrabCut for 3D and RGB-D Point Clouds. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_32
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DOI: https://doi.org/10.1007/978-3-319-02895-8_32
Publisher Name: Springer, Cham
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