pulak09 / astar

ASTAR : GLOBALLY OPTIMAL CONSENSUS MAXIMISATION, CVPR 2015

ASTAR : GLOBALLY OPTIMAL CONSENSUS MAXIMISATION

This package contains the source code which implements optimal Consensus Maximisation proposed in T.J. Chin, P. Purkait, A. Eriksson and D. Suter Efficient Globally Optimal Consensus Maximisation with Tree Search, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015, Boston

Copyright (c) 2015 Pulak Purkait ([email protected].) School of Computer Science, The University of Adelaide, Australia The Australian Center for Visual Technologies http://www.cs.adelaide.edu.au/directory/pulak.purkait

Please acknowledge the authors by citing the above paper in any academic publications that have made use of this package or part of it.

The program is free for non-commercial academic use. Any commercial use is strictly prohibited without the authors' consent. Please acknowledge the authors by citing the above paper in any academic publications that have made use of this package or part of it.

If you encounter any problems or questions please email to [email protected].

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Run Source Code

Run linearASTAR/main_maxcon.m Run PseudoConvexASTAR/homography_maxcon_Keble.m (You Need Vlfeat to run this script.)

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Disclaimer

This is a demo code of our CVPR 2015 paper. This should work well under different choices of the parameters. Please report to the authors if you find any bug in the code.

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License

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.