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Codes for "Predictive online optimisation with applications to optical flow"

Valkonen, Tuomo


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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Valkonen, Tuomo</dc:creator>
  <dc:date>2020-02-07</dc:date>
  <dc:description>These are the (Julia) codes for the optical flow experiments of the manuscript “Predictive online optimisation with applications to optical flow” by Tuomo Valkonen (arXiv:2002.03053).

Prerequisites

These codes were written for Julia 1.3. The Julia package prequisites are from November 2019 when our experiments were run, and have not been updated to maintain the same environment we used to do the experiments in the manuscript. You may get Julia from julialang.org.

Using

Navigate your unix shell to the directory containing this README.md and then run:

$ julia --project=PredictPDPS


The first time doing this, to ensure all the dependencies are installed, run

$ ]instantiate


Afterwards in the Julia shell, type:

&gt; using PredictPDPS


This may take a while as Julia precompiles the code. Then, to generate all the experiments in the manuscript, run:

&gt; batchrun_article()


This will save the results under img/. To see the experiments running visually, and not save the results, run

&gt; demo_known1()


or any of demo_XY(), where X=1,2,3 and Y=known,unknown. Further parameters and experiments are available via run_experiments. See the source code for details.

To run the data generation multi-threadeadly parallel to the algorithm, set the JULIA_NUM_THREADS environment variable to a number larger than one.</dc:description>
  <dc:identifier>https://zenodo.org/record/3659180</dc:identifier>
  <dc:identifier>10.5281/zenodo.3659180</dc:identifier>
  <dc:identifier>oai:zenodo.org:3659180</dc:identifier>
  <dc:relation>arxiv:arXiv:2002.03053</dc:relation>
  <dc:relation>doi:10.1007/s10851-020-01000-4</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3659179</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:source>Journal of Mathematical Imaging and Vision 2021</dc:source>
  <dc:subject>online optimisation</dc:subject>
  <dc:subject>optical flow</dc:subject>
  <dc:subject>primal-dual</dc:subject>
  <dc:subject>nonsmooth</dc:subject>
  <dc:title>Codes for "Predictive online optimisation with applications to optical flow"</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
</oai_dc:dc>
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