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Issue No.02 - Feb. (2013 vol.35)
pp: 504-511
A. Hosni , Inst. of Software Technol. & Interactive Syst., Vienna Univ. of Technol., Vienna, Austria
C. Rhemann , Inst. of Software Technol. & Interactive Syst., Vienna Univ. of Technol., Vienna, Austria
M. Bleyer , Inst. of Software Technol. & Interactive Syst., Vienna Univ. of Technol., Vienna, Austria
C. Rother , Microsoft Res. Cambridge, Cambridge, UK
M. Gelautz , Inst. of Software Technol. & Interactive Syst., Vienna Univ. of Technol., Vienna, Austria
ABSTRACT
Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such solutions can be efficiently achieved by smoothing the label costs with a very fast edge-preserving filter. In this paper, we propose a generic and simple framework comprising three steps: 1) constructing a cost volume, 2) fast cost volume filtering, and 3) Winner-Takes-All label selection. Our main contribution is to show that with such a simple framework state-of-the-art results can be achieved for several computer vision applications. In particular, we achieve 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain very fine structures as well as large displacements. To demonstrate robustness, the few parameters of our framework are set to nearly identical values for both applications. Also, competitive results for interactive image segmentation are presented. With this work, we hope to inspire other researchers to leverage this framework to other application areas.
INDEX TERMS
Optical imaging, Image color analysis, Vectors, Image edge detection, Image segmentation, Labeling, Stereo vision,interactive image segmentation, Stereo matching, optical flow
CITATION
A. Hosni, C. Rhemann, M. Bleyer, C. Rother, M. Gelautz, "Fast Cost-Volume Filtering for Visual Correspondence and Beyond", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 2, pp. 504-511, Feb. 2013, doi:10.1109/TPAMI.2012.156
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