Graph cuts in computer vision: Difference between revisions

Revision as of 08:41, 15 June 2017

Revision as of 09:24, 4 July 2017

Line 8:

Although the general <math>k</math>[[Graph coloring|-colour problem]] remains unsolved for <math>k > 2,</math> the approach of Greig, Porteous and Seheult<ref name="D.M. Greig, B.T 1989"/> has turned out<ref>Y. Boykov, O. Veksler and R. Zabih (1998), "[http://www.cs.cornell.edu/~rdz/Papers/BVZ-cvpr98.pdf Markov Random Fields with Efficient Approximations]", ''International Conference on Computer Vision and Pattern Recognition (CVPR)''.</ref><ref name="boykov2001fast">Y. Boykov, O. Veksler and R. Zabih (2001), "[http://www.cs.cornell.edu/~rdz/Papers/BVZ-pami01-final.pdf Fast approximate energy minimisation via graph cuts]", ''IEEE Transactions on Pattern Analysis and Machine Intelligence'', '''29''', 1222–1239.</ref> to have wide applicability in general computer vision problems. Greig, Porteous and Seheult approaches are often applied iteratively to a sequence of binary problems, usually yielding near optimal solutions.

==Binary Segmentationsegmentation of Imagesimages==

=== Notation ===

* Image: <math>x \in \{R,G,B\}^N</math>