Title :
MAP region segmentation based on composite random field models
Author_Institution :
Dept. of Eng. Math. & Comput. Sci., Louisville Univ., KY, USA
Abstract :
A composite of two random field models is used to describe the observed image: a high-level process that describes the various regions in the images is modeled by a Gibbs-Markov model, and a low-level process that describes each particular region is modeled by a simultaneous autoregressive model. Using this composition, a recursive maximum a posterior (MAP) segmentation algorithm is formulated and various issues related to parameter estimation are discussed
Keywords :
Markov processes; image segmentation; parameter estimation; recursive functions; statistical analysis; Gibbs-Markov model; MAP region segmentation; autoregressive model; composite random field models; high-level process; images; low-level process; maximum a posteriori method; parameter estimation; recursive algorithm; Computer science; Electronic mail; Image segmentation; Labeling; Lattices; Layout; Mathematical model; Mathematics; Parameter estimation; Random processes;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0532-9
DOI :
10.1109/ICASSP.1992.226279
