Lempitsky, Victor ; Roth, Stefan ; Rother, Carsten (2008)
FusionFlow: Discrete-Continuous Optimization for Optical Flow Estimation.
IEEE Conference on Computer Vision and Pattern Recognition.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2008 |
| Autor(en): | Lempitsky, Victor ; Roth, Stefan ; Rother, Carsten |
| Art des Eintrags: | Bibliographie |
| Titel: | FusionFlow: Discrete-Continuous Optimization for Optical Flow Estimation |
| Sprache: | Englisch |
| Publikationsjahr: | 2008 |
| Verlag: | IEEE, New York |
| Veranstaltungstitel: | IEEE Conference on Computer Vision and Pattern Recognition |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Forschungsgruppe Visual Inference (VINF), Flow fields, Discrete optimization, Computer vision, Graph cuts |
| Fachbereich(e)/-gebiet(e): | nicht bekannt 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 16 Apr 2018 09:03 |
| Letzte Änderung: | 16 Apr 2018 09:03 |
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