Title :
Gauss-Markov measure field models for low-level vision
Author :
Marroquin, Jose L. ; Velasco, Fernando A. ; Rivera, Mariano ; Nakamura, Miguel
Author_Institution :
Centro de Investigacion en Matematicas, Guanajuato, mexico
fDate :
4/1/2001 12:00:00 AM
Abstract :
We present a class of models, derived from classical discrete Markov random fields, that may be used for the solution of ill-posed problems in image processing and in computational vision. They lead to reconstruction algorithms that are flexible, computationally efficient, and biologically plausible. To illustrate their use, we present their application to the reconstruction of the dominant orientation and direction fields, to the classification of multiband images, and to image quantization and filtering
Keywords :
Bayes methods; Gaussian distribution; Markov processes; filtering theory; image classification; image segmentation; Gauss-Markov measure field models; classical discrete Markov random fields; computational vision; direction fields; ill-posed problems; image filtering; image processing; image quantization; low-level vision; multiband images; orientation fields; reconstruction algorithms; Biological system modeling; Biology computing; Computer vision; Distributed computing; Estimation theory; Gaussian processes; Image reconstruction; Lattices; Markov random fields; State-space methods;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
