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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-8
Vibhav Vineet , Centre for Visual Information Technology, International Institute of Information Technology, Hyderabad, 500032. India
P. J. Narayanan , Centre for Visual Information Technology, International Institute of Information Technology, Hyderabad, 500032. India
ABSTRACT
Graph cuts has become a powerful and popular optimization tool for energies defined over an MRF and have found applications in image segmentation, stereo vision, image restoration, etc. The maxflow/mincut algorithm to compute graph-cuts is computationally heavy. The best-reported implementation of graph cuts takes over 100 milliseconds even on images of size 640×480 and cannot be used for real-time applications or when iterated applications are needed. The commodity Graphics Processor Unit (GPU) has emerged as an economical and fast computation co-processor recently. In this paper, we present an implementation of the push-relabel algorithm for graph cuts on the GPU. We can perform over 60 graph cuts per second on 1024×1024 images and over 150 graph cuts per second on 640×480 images on an Nvidia 8800 GTX. The time for each complete graph-cut is about 1 millisecond when only a few weights change from the previous graph, as on dynamic graphs resulting from videos. The CUDA code with a well-defined interface can be downloaded for anyone’s use.
CITATION
Vibhav Vineet, P. J. Narayanan, "CUDA cuts: Fast graph cuts on the GPU", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-8, doi:10.1109/CVPRW.2008.4563095
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