皮尔逊相关性最大化导向的自动阈值分割方法  

Automatic thresholding segmentation guided by maximizing Pearson correlation

作　　者：邹耀斌[1,2] 齐慧康 孙水发[1,2] Zou Yaobin;Qi Huikang;Sun Shuifa(Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering(China Three Gorges University),Yichang 443002,China;College of Computer and Information Technology,China Three Gorges University,Yichang 443002,China)

机构地区：[1]湖北省水电工程智能视觉监测重点实验室(三峡大学),宜昌443002 [2]三峡大学计算机与信息学院,宜昌443002

出　　处：《电子测量技术》2023年第17期109-117,共9页Electronic Measurement Technology

基　　金：国家自然科学基金(61871258)项目资助。

摘　　要：现有阈值分割方法大多只适应于处理某种特定灰度分布模式的图像,为了在统一框架内处理不同灰度分布模式情形下的自动阈值选择问题,提出了一种皮尔逊相关性最大化导向的自动阈值分割方法。该方法首先对原始图像进行边缘检测以产生参考模板图像;然后对不同阈值下的二值图像进行轮廓提取以产生对应的轮廓图像;最后采用皮尔逊相关系数衡量不同轮廓图像与参考模板图像之间的相似性,并将相似性取最大值时所对应的阈值作为最终分割阈值。提出的方法和新近提出的3个阈值方法和4个非阈值方法进行了比较。在具有不同灰度分布模式的4幅合成图像和50幅真实世界图像上的实验结果表明:在合成图像集中,相比于分割精度第2的方法,平均误分类率降低了0.140 3;在真实世界图像集中,相比于分割精度第2的方法,平均误分类率降低了0.121 5。提出的方法虽然在计算效率方面不占有优势,但它对不同灰度分布模式的图像具有更灵活的分割适应性,能获得精度更高的分割结果图像。Most of the existing image thresholding methods are only suitable for processing the images with a specific gray level distribution.To deal with the issue of threshold selection in different gray level distribution within a unified framework,an automatic thresholding segmentation method guided by maximizing Pearson correlation is proposed.This method first performs edge detection on the original image to generate a reference template image;then it performs contour extraction on the binary images obtained by different thresholds to generate the corresponding contour images;it finally utilizes Pearson correlation coefficient to measure the similarities between different contour images and reference template images,and the threshold corresponding to the maximal similarity is selected as the final segmentation threshold.The proposed method is compared with 3 newly proposed thresholding methods and 4 non-thresholding methods.The experimental results on 4 synthetic images and 50 real-world images with different gray level distribution show that,compared with the second best method in segmentation accuracy,the proposed method is reduced by 0.1403 and 0.1215 in terms of the average misclassification error on the synthetic images and the real-world images,respectively.The proposed method has no advantage in computational efficiency,but it has more flexible segmentation adaptability to images with different gray level distribution patterns,and can obtain segmentation result images with higher accuracy.

关 键 词：阈值分割 图像间相似性 模板匹配 皮尔逊相关系数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]