Scale-aware object tracking with convex shape constraints on RGB-D images
Klodt M, Sturm J, Cremers D (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Book Volume: 8142 LNCS
Pages Range: 111-120
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: DEU
DOI: 10.1007/978-3-642-40602-7_12
Abstract
Convex relaxation techniques have become a popular approach to a variety of image segmentation problems as they allow to compute solutions independent of the initialization. In this paper, we propose a novel technique for the segmentation of RGB-D images using convex function optimization. The function that we propose to minimize considers both the color image and the depth map for finding the optimal segmentation. We extend the objective function by moment constraints, which allow to include prior knowledge on the 3D center, surface area or volume of the object in a principled way. As we show in this paper, the relaxed optimization problem is convex, and thus can be minimized in a globally optimal way leading to high-quality solutions independent of the initialization. We validated our approach experimentally on four different datasets, and show that using both color and depth substantially improves segmentation compared to color or depth only. Further, 3D moment constraints significantly robustify segmentation which proves in particular useful for object tracking. © 2013 Springer-Verlag.
Involved external institutions
Germany (DE)How to cite
APA:
Klodt, M., Sturm, J., & Cremers, D. (2013). Scale-aware object tracking with convex shape constraints on RGB-D images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 111-120). DEU.
MLA:
Klodt, Maria, Jürgen Sturm, and Daniel Cremers. "Scale-aware object tracking with convex shape constraints on RGB-D images." Proceedings of the 35th German Conference on Pattern Recognition, GCPR 2013, DEU 2013. 111-120.
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