Learning low-level vision

DMCA

Learning low-level vision (2000)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by William T. Freeman , Egon C. Pasztor

Venue:	International Journal of Computer Vision
Citations:	578 - 30 self

BibTeX

@ARTICLE{Freeman00learninglow-level,
    author = {William T. Freeman and Egon C. Pasztor},
    title = {Learning low-level vision},
    journal = {International Journal of Computer Vision},
    year = {2000},
    volume = {40},
    pages = {2000}
}

OpenURL

Abstract

We show a learning-based method for low-level vision problems. We set-up a Markov network of patches of the image and the underlying scene. A factorization approximation allows us to easily learn the parameters of the Markov network from synthetic examples of image/scene pairs, and to e ciently propagate image information. Monte Carlo simulations justify this approximation. We apply this to the \super-resolution " problem (estimating high frequency details from a low-resolution image), showing good results. For the motion estimation problem, we show resolution of the aperture problem and lling-in arising from application of the same probabilistic machinery.

Keyphrases

low-level vision markov network learning-based method factorization approximation synthetic example good result motion estimation problem monte carlo simulation super-resolution quot lling-in arising high frequency detail image scene pair low-level vision problem low-resolution image propagate image information aperture problem probabilistic machinery underlying scene

DMCA