Optimization of multi-label problems in computer vision

Details Optimization of multi-label problems in computer vision Optimization of multi-label problems in computer vision

2011-04-12

2011-04-12

Ge, Yuzhen (Department: 2624)

Image analysis

Image segmentation

Region labeling

Reexamination Certificate

active

07925089

ABSTRACT:
A method of labeling pixels in an image is described where the pixel label is selected from a set of three or more labels. The pixel labeling problem is reduced to a sequence of binary optimizations by representing the label value for each pixel as a binary word and then optimizing the value of each bit within the word, starting with the most significant bit. Data which has been learned from one or more training images is used in the optimization to provide information about the less significant bits within the word.

REFERENCES:
patent: 5293430 (1994-03-01), Shiau et al.
patent: 6507661 (2003-01-01), Roy
patent: 6744923 (2004-06-01), Zabih et al.
patent: 6973212 (2005-12-01), Boykov et al.
patent: 7587086 (2009-09-01), Shinkevich
patent: 7653261 (2010-01-01), Blake et al.
patent: 7660463 (2010-02-01), Blake et al.
patent: 2005/0271273 (2005-12-01), Blake et al.
patent: 2006/0104542 (2006-05-01), Blake et al.
patent: 2006/0214932 (2006-09-01), Grady et al.
patent: 2006/0291721 (2006-12-01), Torr et al.
patent: 2007/0022067 (2007-01-01), Cremers et al.
patent: 2007/0031037 (2007-02-01), Blake et al.
patent: 2007/0285722 (2007-12-01), Koyama
patent: 2008/0123945 (2008-05-01), Andrew et al.
Boykov, et al., “Fast Approximate Energy Minimization via Graph Cuts”, IEEE, vol. 23, No. 11, pp. 1222-1239.
Boykov, et al., “Markov Random Fields with Efficient Approximations”, available at least as early as Jun. 26, 2007, at <<http://ieeexplore.ieee.org/ieI4/5649/15135/00698673.pdf?isNumber=>>, pp. 8.
Freedman, et al., “Energy Minimization via Graph Cuts: Settling What is Possible”, pp. 8.
Greig, et al., “Exact Maximum A Posteriori Estimation for Binary Images”, J Royal Statistical Society, 1989, pp. 271-279.
Hammer, et al., “Roof Duality, Complementation and Persistency in Quadratic 0-1 Optimization”, Mathematical Programming, 1984, pp. 121-155.
Hochbaum, “An Efficient Algorithm for Image Segmentation, Markov Random Fields and Related Problems”, at <<http://delivery.acm.org/10.1145/510000/502093/p686-hochbaum.pdf?key1=502093&key2=3189382811&coll=GUIDE&dl=GUIDE&CFID=22335617&CFTOKEN=41199815 >>, ACM, vol. 48, No. 4, Jul. 2001, pp. 686-701.
Ishikawa, “Exact optimization for Markov random fields with convex priors”, at <<http://www.nsc.nagoya-cu.ac.jp/˜hi/MRF.pdf>>, IEEE, vol. 25, No. 10, Oct. 2003, pp. 1333-1336.
Kohli, et al., “Efficiently Solving Dynamic Markov Random Fields using Graph Cuts”, available at least as early as Jun. 26, 2007, at <<http://cms.brookes.ac.uk/staff/PushmeetKohli/papers/pushmeet-dynamic.pdf>>, pp. 8.
Kolmogorov, et al., “What Energy Functions Can be Minimized via Graph Cuts?”, IEEE, vol. 26, No. 2, Feb. 2004, pp. 147-159.
Nelder, et al., “Simplex Method for Function Minimization”, The British Library, pp. 308-313.
Rother, et al., “Digital Tapestry”, pp. 8.

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