一种基于经验知识和信息熵的阈值选择策略  

A threshold selection strategy based on empirical knowledge and information entropy

作　　者：许永华[1] 吴敏[1] 何勇[1] 聂卓赟[1] 

机构地区：[1]中南大学信息科学与工程学院,湖南长沙410083

出　　处：《化工学报》2008年第7期1803-1807,共5页CIESC Journal

基　　金：国家高技术研究发展计划项目(2007AA04Z177);国家杰出青年基金项目(60425310)~~

摘　　要：针对高炉料面温度场红外图像的特征提取问题,提出一种基于经验知识和信息熵的图像分割阈值选择策略。该方法首先利用双峰法、最大方差法和一致性准则法分别对图像进行区域分割,再根据图像目标大小,运用知识规则制定判断图像质量的信息熵评价准则,选择合适的分割阈值,获得对判断料面温度分布最有利的图像区域特征。实际运行结果表明图像分割结果和期望的图像特征一致,符合实际工业应用要求,有利于操作人员判断炉况。Considering the fact that feature extraction of infra red image for burden surface temperature profile in blast furnace is a hard problem, an optimized strategy of threshold selection based on empirical knowledge and information entropy was proposed. Firstly, an image was segmented respectively by the two-peaked histogram method, the Otsu method and the consistency criteria method. Then a criterion to value the image quality based on information entropy was made by rules, which was related to the image sizes of objects. The appropriate threshold value, by which image segmentation was implemented to judge the burden temperature distribution most efficiently, was obtained. Actual application showed that the results of image segmentation were consistent with the expected image features, which satisfied the demands of industrial application and could help operators to judge the blast furnace conditions.

关 键 词：高炉 经验知识 信息熵 阈值选择 

分 类 号：TP229[自动化与计算机技术—检测技术与自动化装置]