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Long-Term Image Boundary Extrapolation

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons197297

Bhattacharyya,  Apratim
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44976

Malinowski,  Mateusz
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44451

Fritz,  Mario
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Volltexte (frei zugänglich)

arXiv:1611.08841.pdf
(Preprint), 10MB

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Zitation

Bhattacharyya, A., Malinowski, M., Schiele, B., & Fritz, M. (2016). Long-Term Image Boundary Extrapolation. Retrieved from http://arxiv.org/abs/1611.08841.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002C-26B1-A
Zusammenfassung
Boundary prediction in images and videos has been a very active topic of research and organizing visual information into boundaries and segments is believed to be a corner stone of visual perception. While prior work has focused on predicting boundaries for observed frames, our work aims at predicting boundaries of future unobserved frames. This requires our model to learn about the fate of boundaries and extrapolate motion patterns. We experiment on established real-world video segmentation dataset, which provides a testbed for this new task. We show for the first time spatio-temporal boundary extrapolation, that in contrast to prior work on RGB extrapolation maintains a crisp result. Furthermore, we show long-term prediction of boundaries in situations where the motion is governed by the laws of physics. We argue that our model has with minimalistic model assumptions derived a notion of "intuitive physics".