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FusionFlow: Discrete-Continuous Optimization for Optical Flow Estimation (2008)

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by Victor Lempitsky , Stefan Roth , Carsten Rother
Citations:57 - 7 self
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BibTeX

@MISC{Lempitsky08fusionflow:discrete-continuous,
    author = {Victor Lempitsky and Stefan Roth and Carsten Rother},
    title = {FusionFlow: Discrete-Continuous Optimization for Optical Flow Estimation},
    year = {2008}
}

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Abstract

Accurate estimation of optical flow is a challenging task, which often requires addressing difficult energy optimization problems. To solve them, most top-performing methods rely on continuous optimization algorithms. The modeling accuracy of the energy in this case is often traded for its tractability. This is in contrast to the related problem of narrow-baseline stereo matching, where the top-performing methods employ powerful discrete optimization algorithms such as graph cuts and message-passing to optimize highly non-convex energies. In this paper, we demonstrate how similar non-convex energies can be formulated and optimized discretely in the context of optical flow estimation. Starting with a set of candidate solutions that are produced by fast continuous flow estimation algorithms, the proposed method iteratively fuses these candidate solutions by the computation of minimum cuts on graphs. The obtained continuous-valued fusion result is then further improved using local gradient descent. Experimentally, we demonstrate that the proposed energy is an accurate model and that the proposed discretecontinuous optimization scheme not only finds lower energy solutions than traditional discrete or continuous optimization techniques, but also leads to flow estimates that outperform the current state-of-the-art.

Keyphrases

optical flow estimation    discrete-continuous optimization    candidate solution    top-performing method    similar non-convex energy    continuous optimization technique    current state-of-the-art    obtained continuous-valued fusion result    continuous optimization algorithm    accurate estimation    graph cut    traditional discrete    narrow-baseline stereo matching    local gradient descent    non-convex energy    difficult energy optimization problem    powerful discrete optimization algorithm    minimum cut    continuous flow estimation algorithm    discretecontinuous optimization scheme    related problem    challenging task    energy solution    optical flow    accurate model   

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