Depth-adaptive supervoxels for RGB-D video segmentation

Weikersdorfer D, Schick A, Cremers D (2013)

Publication Type: Conference contribution

Publication year: 2013

Publisher: IEEE Computer Society

Pages Range: 2708-2712

Conference Proceedings Title: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Event location: AUS

ISBN: 9781479923410

DOI: 10.1109/ICIP.2013.6738558

Abstract

In this paper we present a method for automatic video segmentation of RGB-D video streams provided by combined colour and depth sensors like the Microsoft Kinect. To this end, we combine position and normal information from the depth sensor with colour information to compute temporally stable, depth-adaptive superpixels and combine them into a graph of strand-like spatiotemporal, depth-adaptive supervoxels. We use spectral graph clustering on the supervoxel graph to partition it into spatiotemporal segments. Experimental evaluation on several challenging scenarios demonstrates that our two-layer RGB-D video segmentation technique produces excellent video segmentation results. © 2013 IEEE.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Fraunhofer Institute for Optronics, System Technologies and Image Exploitation (IOSB) DE Germany (DE)

How to cite

APA:

Weikersdorfer, D., Schick, A., & Cremers, D. (2013). Depth-adaptive supervoxels for RGB-D video segmentation. In 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings (pp. 2708-2712). AUS: IEEE Computer Society.

MLA:

Weikersdorfer, David, Alexander Schick, and Daniel Cremers. "Depth-adaptive supervoxels for RGB-D video segmentation." Proceedings of the 2013 20th IEEE International Conference on Image Processing, ICIP 2013, AUS IEEE Computer Society, 2013. 2708-2712.

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