Learning low-level vision

Learning low-level vision (2000)

by William T. Freeman , Egon C. Pasztor

Venue:	International Journal of Computer Vision
Citations:	382 - 25 self

Datum	Value	Source
TITLE	Learning low-level vision	SVM HeaderParse 0.2
AUTHOR NAME	William T. Freeman	SVM HeaderParse 0.2
AUTHOR AFFIL	1. MERL, a Mitsubishi Electric Res. Lab.	SVM HeaderParse 0.2
AUTHOR ADDR	201 Broadway; Cambridge, MA 02139	SVM HeaderParse 0.2
AUTHOR NAME	Egon C. Pasztor	SVM HeaderParse 0.2
AUTHOR AFFIL	1. MERL, a Mitsubishi Electric Res. Lab.	SVM HeaderParse 0.2
AUTHOR ADDR	201 Broadway; Cambridge, MA 02139	SVM HeaderParse 0.2
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.	SVM HeaderParse 0.2
YEAR	2000	INFERENCE
VENUE	International Journal of Computer Vision	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	2000	INFERENCE
VOLUME	40	INFERENCE
CITATIONS	34 found	ParsCit 1.0