基于模糊C均值与Markov随机场的图像分割  

Image Segmentation Based on FCM and Markov Random Fields

作　　者：蔡涛[1] 徐国华[1] 徐筱龙[1] 

机构地区：[1]华中科技大学交通学院水下作业实验室,武汉430073

出　　处：《计算机工程》2007年第20期34-36,39,共4页Computer Engineering

基　　金：国家"863"计划基金资助项目"7000米深海载人潜水器"子题"水下热液喷口目标识别技术"(2002AA401001-4B)

摘　　要：针对传统模糊C-均值(FCM)图像分割算法没有考虑图像空间连续性的缺点,提出一种改进的空间约束FCM分割算法。该算法引入了Markov随机场理论中类别标记的伪似然度近似策略,将像素特征域相似性同空间域相邻性有机地结合起来,给出了新的像素样本聚类目标函数。实验证明,该算法能大大提高分割性能并改善分割的视觉效果。To overcome the defects of image segmentation by classic fuzzy C-means （FCM） clustering that considers nothing about image continuity, this paper introduces a new spatially constrained FCM image segmentation algorithm. The pseudo-likelihood of labeling is adopted in this algorithm to combine spectral similarity and spatial neighboring of image pixels. A new objective function is proposed and minimized. The experiments are conducted on simulated gray images and real color images. Experimental results show that the proposed approach is more effective and has better performance.

关 键 词：模糊C均值 MARKOV随机场 伪似然度 图像分割 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]