TEXTURE SEGMENTATION BASED ON A HIERARCHICAL MARKOV RANDOM FIELD MODEL

被引：21

作者：

HU, RM

FAHMY, MM

机构：

[1] Department of Electrical Engineering, Queen's University, Kingston

来源：

SIGNAL PROCESSING | 1992年 / 26卷 / 03期

关键词：

IMAGE ANALYSIS; IMAGE SEGMENTATION; TEXTURE SEGMENTATION; TEXTURE MODELING; MARKOV RANDOM FIELDS; PARAMETER ESTIMATION; SIMULATED ANNEALING;

D O I：

10.1016/0165-1684(92)90117-F

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a new texture segmentation technique for both supervised and unsupervised segmentation. The textured images under study are modeled by a proposed hierarchical Markov random field (MRF) model. This model is formed by combining the binomial model for textures and the multi-level logistic model for region distributions. The supervised segmentation is achieved by a new algorithm which can reach the global maxima of the posteriori distribution even if the textures are modeled by an MRF model. For unsupervised segmentation, a new parameter estimation scheme is proposed to estimate the model parameters directly from a given image. The new technique is verified by a variety of textured images, such as synthesized textures, natural textures and aerial images, in both supervised and unsupervised segmentation cases.

引用

页码：285 / 305

页数：21

共 14 条

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