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Computer Vision pp 51–108Cite as

Dynamic Graph Cuts and Their Applications in Computer Vision

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 285))

Abstract

Over the last few years energy minimization has emerged as an indispensable tool in computer vision. The primary reason for this rising popularity has been the successes of efficient graph cut based minimization algorithms in solving many low level vision problems such as image segmentation, object reconstruction, image restoration and disparity estimation. The scale and form of computer vision problems introduce many challenges in energy minimization. In this chapter we address the problem of efficient and exact minimization of groups of similar functions which are known to be solvable in polynomial time. We will present a novel dynamic algorithm for minimizing such functions. This algorithm reuses computation from previous problem instances to solve new instances resulting in a substantial improvement in the running time. We will present the results of using this approach on the problems of interactive image segmentation, image segmentation in video, human pose estimation and segmentation, and measuring uncertainty of solutions obtained by minimizing energy functions.

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Authors and Affiliations

  1. Microsoft Research, Cambridge, UK

    Pushmeet Kohli

  2. Oxford Brookes University, Oxford, UK

    Philip H. S. Torr

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  1. Pushmeet Kohli
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  2. Philip H. S. Torr
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  1. Department of Engineering, University of Cambridge, CB2 1PZ, Cambridge, UK

    Roberto Cipolla

  2. Dipartimento di Matematica ed Informatica, University of Catania, Viale A. Doria 6, I, 95125, Catania, Italy

    Sebastiano Battiato  & Giovanni Maria Farinella  & 

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Kohli, P., Torr, P.H.S. (2010). Dynamic Graph Cuts and Their Applications in Computer Vision. In: Cipolla, R., Battiato, S., Farinella, G.M. (eds) Computer Vision. Studies in Computational Intelligence, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12848-6_3

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  • DOI: https://doi.org/10.1007/978-3-642-12848-6_3

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