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Optical Flow Estimation with Channel Constancy

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons140744

Sevilla-Lara,  Laura
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons75293

Black,  Michael J.
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Sevilla-Lara, L., Sun, D., Learned-Miller, E. G., & Black, M. J. (2014). Optical Flow Estimation with Channel Constancy. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision - ECCV 2014. Proceedings, Part 1 (pp. 423-438). Cham et al.: Springer International Publishing. doi:10.1007/978-3-319-10590-1_28.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0024-E2F1-0
Zusammenfassung
Large motions remain a challenge for current optical flow algorithms. Traditionally, large motions are addressed using multi-resolution representations like Gaussian pyramids. To deal with large displacements, many pyramid levels are needed and, if an object is small, it may be invisible at the highest levels. To address this we decompose images using a channel representation (CR) and replace the standard brightness constancy assumption with a descriptor constancy assumption. CRs can be seen as an over-segmentation of the scene into layers based on some image feature. If the appearance of a foreground object differs from the background then its descriptor will be different and they will be represented in different layers.We create a pyramid by smoothing these layers, without mixing foreground and background or losing small objects. Our method estimates more accurate flow than the baseline on the MPI-Sintel benchmark, especially for fast motions and near motion boundaries.