Title :
A proposal for a hierarchical MRF model based on conditional probability
Author :
Igarashi, Harukazu ; Kawato, Mitsuo
Author_Institution :
ATR Auditory & Visual Perception Res. Lab., Kyoto, Japan
Abstract :
The standard regularization theory extended to problems where generic constraints or knowledge are expressed within the framework of a Markov random field (MRF) model. This extended theory is applied to image restoration in which a desired state in the line process is given as a constraint. The forward process in transformation between two kinds of visual information, from information of pixel intensity to information of edge configuration, is modeled with a renormalization group technique rather than with the usual optics. Perfect restorations were obtained for some simple pictures
Keywords :
Markov processes; picture processing; probability; MRF model; conditional probability; edge configuration; generic constraints; hierarchical Markov random field model; image restoration; picture processing; pixel intensity; renormalization group technique; Computer vision; Image edge detection; Image reconstruction; Image restoration; Markov random fields; Optical computing; Pixel; Probability distribution; Proposals; Shape;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170415
