图像分割的熵方法综述  

Review on Image Segmentation Based on Entropy

作　　者：曹建农[1] 

机构地区：[1]长安大学地球科学与资源学院,西安710054

出　　处：《模式识别与人工智能》2012年第6期958-971,共14页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金资助项目(No.40971217)

摘　　要：对图像分割的熵方法进行较全面地分析和综述,其中包括一维最大熵、最小交叉熵、最大交叉熵图像分割方法等.对Shannon熵、Tsallis熵及Renyi熵之间的关系等进行分析与评述.对二维(高维)熵及空间熵等进行分析与评述.最后指出一维熵与其它理论的有机结合、高维熵模型的计算效率等未来研究方向.The image segmentation based on entropy is analyzed and reviewed including one-dimensional maximum entropy, minimum cross entropy, maximum cross entropy and so on. The relations of Shannon entropy, Tsallis entropy and Renyi entropy are analyzed and commented, and the performance of two dimensional （high dimension） entropy and spatial entropy is also appraised. In conclusion, it points out the future research direction, such as the computational efficiency of the high-dimensional entropy model and one-dimensional entropy and other theories integrated.

关 键 词：图像分割 交叉熵 二维(高维)熵 空间熵 玻耳兹曼熵 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]