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Python bindings to the optical flow framework by C. Liu

Project description

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Python Bindings to Liu’s Optical Flow Framework for Bob

This package is a simple Python wrapper to the open-source Optical Flow estimator developed by C. Liu during his Ph.D. The code was originally conceived to operate over Matlab. This is a Python/Bob port. If you use this code, the author asks you to cite his thesis:

@phdthesis{Liu_PHD_2009,
  title = {{Beyond Pixels: Exploring New Representations and Applications for Motion Analysis}},
  author = {Liu, Ce},
  institution = {{Massachusetts Institute of Technology}},
  year = {2009},
  type = {{Ph.D. Thesis}},
}

If you decide to use this port on your publication, we kindly ask you to cite Bob as well, as the base software framework, on which this port has been developed:

@inproceedings{Anjos_ACMMM_2012,
  author = {Anjos, Andr\'e AND El Shafey, Laurent AND Wallace, Roy AND G\"unther, Manuel AND McCool, Christopher AND Marcel, S\'ebastien},
  title = {Bob: a free signal processing and machine learning toolbox for researchers},
  year = {2012},
  month = oct,
  booktitle = {20th ACM Conference on Multimedia Systems (ACMMM), Nara, Japan},
  publisher = {ACM Press},
  url = {http://publications.idiap.ch/downloads/papers/2012/Anjos_Bob_ACMMM12.pdf},
}

Please read Liu’s homepage for details on the code, also displaying the original Matlab port.

Installation

To install this package – alone or together with other Packages of Bob – please read the Installation Instructions. For Bob to be able to work properly, some dependent packages are required to be installed. Please make sure that you have read the Dependencies for your operating system.

Documentation

For further documentation on this package, please read the Stable Version or the Latest Version of the documentation. For a list of tutorials on this or the other packages ob Bob, or information on submitting issues, asking questions and starting discussions, please visit its website.

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Source Distribution

bob.ip.optflow.liu-2.0.1.zip (18.8 MB view hashes)

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