Name
Abstract | ||
|---|---|---|
Markov Random Field is now ubiquitous in many formu- lations of various vision problems. Recently, optimization of higher-order potentials became practical using higher- order graph cuts: the combination of i) the fusion move algorithm, ii) the reduction of higher-order binary energy minimization to first-order, and iii) the QPBO algorithm. In the fusion move, it is crucial for the success and efficiency of the optimization to provide proposals that fits the ener- gies being optimized. For higher-order energies, it is even more so because they have richer class of null potentials. In this paper, we focus on the efficiency of the higher-order graph cuts and present a simple technique for generating proposal labelings that makes the algorithm much more ef- ficient, which we empirically show using examples in stereo and image denoising. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICCV.2009.5459187 | Kyoto |
Keywords | Field | DocType |
computer vision,gradient methods,image denoising,image fusion,optimisation,Markov random field,QPBO algorithm,computer vision,fusion-move graph cut,higher-order gradient descent,higher-order graph cuts,image denoising,optimization,stereo denoising | Cut,Computer vision,Graph cuts in computer vision,Gradient descent,Mathematical optimization,Image fusion,Markov random field,Computer science,Minification,Artificial intelligence,Binary number,Energy minimization | Conference |
Volume | Issue | ISSN |
2009 | 1 | 1550-5499 E-ISBN : 978-1-4244-4419-9 |
ISBN | Citations | PageRank |
978-1-4244-4419-9 | 12 | 0.70 |
References | Authors | |
25 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroshi Ishikawa | 1 | 12 | 0.70 |