基于决策粗糙集的图像分割  

Image segmentation algorithm based on the decision-theoretic rough set model

作　　者：李峰[1,2] 苗夺谦[1,2] 刘财辉[1,2] 杨伟[1,2] 

机构地区：[1]同济大学计算机科学与技术系,上海201804 [2]同济大学嵌入式系统与服务计算教育部重点实验室,上海201804

出　　处：《智能系统学报》2014年第2期143-147,共5页CAAI Transactions on Intelligent Systems

基　　金：国家自然科学基金资助项目(61103067;61273304)

摘　　要：图像分割是图像处理和图像分析中的重要研究内容之一。目前的研究大多集中在处理比较理想、不带噪声的图像,而现实中的图像往往是带有噪声的,并且图像中物体之间的边界灰度值常常是模糊的。针对带噪声的图像,在粒计算和决策粗糙集的框架下,提出了一种新的图像分割方法。该方法用决策粗糙集思想模拟目标和背景区域,在求取近似集时容忍部分噪声点的存在,通过优化目标和背景区域的粗糙度,获得分割的阈值。实验结果表明该方法能较好地处理带噪声的图像。Image segmentation is one of the important topics in image processing and analysis. The recent studies fo- cus mainly on handling the ideal images without noise, which often go against the reality. What＇ s more, the grey value of the boundaries between objects in the image is often fuzzy. For images with noise, within the framework of granular computing and decision-theoretic rough sets, a novel algorithm for image segmentation is proposed. The al- gorithm deals with image segmentation by simulating the target and background regions with the decision-theoretic rough set model, and tolerates some noise points when calculating the approximate sets. It determines the threshold of partitioning through minimization of roughness in both object and background regions. The experimental results show that the proposed method can solve the problem of noise and improve the effects of image segmentation.

关 键 词：决策粗糙集 图像分割 噪声图像 粒计算 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]