图像过渡区提取与分割的逆向云方法  

Backward Cloud-based Method for Image Transition Region Extraction and Thresholding

作　　者：吴涛[1] 陈一祥[2] 杨俊杰[1] 

机构地区：[1]湛江师范学院信息学院,湛江524048 [2]武汉大学遥感信息工程学院,武汉430079

出　　处：《计算机科学》2013年第5期287-290,306,共5页Computer Science

基　　金：国家973重点基础研究发展计划项目(2012CB719903);国家自然科学基金项目(60875007);广东高校优秀青年创新人才培养计划等资助

摘　　要：针对图像自动分割中的最优阈值选择问题,提出了一种基于云模型和过渡区思想的图像阈值化方法。该方法首先通过逆向云算法获得给定图像的云模型;其次设计自适应阈值搜索准则,在云模型的骨干元素区间内最小化该准则,生成图像过渡区的云模型;然后采用极大判定法则实现图像过渡区的不确定性提取;最终根据过渡区像素的灰度峰值完成图像阈值化。新方法利用云模型解决图像过渡区的提取与分割问题,具有不确定性。定性和定量的实验结果及分析表明,该方法分割效果较好、性能稳定,具有合理性和有效性。In order to select the optimal threshold for automatic image segmentation, a novel method for image threshol- ding, based on cloud model and transition region, was proposed. The method calculates the cloud model for a given image by backward cloud-based algorithm. Next, it defines a criterion to search threshold adaptively, minimizes this criterion in the skeleton interval of cloud model, and generates the cloud model for image transition region. Then, it extracts the transition region with uncertainty using the maximum determination rule. Finally, it achieves image thresholding accord- ing to the grayscale peak value of pixels in transition region. The proposed method solves the issue on image transition region extraction and thresholding using cloud model, thus it has uncertainty. The quantitative and qualitative experi- ments indicate that the proposed method yields accurate and robust result, and is reasonable and effective.

关 键 词：过渡区 云模型 图像阈值化 图像分割 不确定性 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]