TY - JOUR
T1 - SEGMENTATION OF NOISY TEXTURED IMAGES USING SIMULATED ANNEALING.
AU - Won, Chee Sun
AU - Derin, Haluk
PY - 1987
Y1 - 1987
N2 - The authors present a segmentation algorithm for noisy textured images. To represent noisy textured images, they propose a hierarchical stochastic model that consists of three levels of random fields: the region process, the texture process and the noise. The hierarchical model also includes local blurring and nonlinear image transformation as results of the image-corrupting effects. Having adopted a statistical model, the maximum a posteriori (MAP) estimation is used to find the segmented regions through the restored (noise-free) textured image data. Since the joint a posteriori distribution at hand is a Gibbs distribution, simulated annealing is used as a maximization technique. The simulated-annealing-based segmentation algorithm presented can also be viewed as a two-step iterative algorithm in the spirit of the EM algorithm (see Dempster, A. P. et. al. , J. Royal Stat. Society, vol. 39, pp. 1-38, 1977).
AB - The authors present a segmentation algorithm for noisy textured images. To represent noisy textured images, they propose a hierarchical stochastic model that consists of three levels of random fields: the region process, the texture process and the noise. The hierarchical model also includes local blurring and nonlinear image transformation as results of the image-corrupting effects. Having adopted a statistical model, the maximum a posteriori (MAP) estimation is used to find the segmented regions through the restored (noise-free) textured image data. Since the joint a posteriori distribution at hand is a Gibbs distribution, simulated annealing is used as a maximization technique. The simulated-annealing-based segmentation algorithm presented can also be viewed as a two-step iterative algorithm in the spirit of the EM algorithm (see Dempster, A. P. et. al. , J. Royal Stat. Society, vol. 39, pp. 1-38, 1977).
UR - http://www.scopus.com/inward/record.url?scp=0023263710&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:0023263710
SN - 0736-7791
SP - 563
EP - 566
JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
ER -