图像分割算法研究与实现  

Research and Implementation of Image Segmentation Algorithm

作　　者：宋凯[1] 王舒卉 SONG Kai;WANG Shu-hui(College of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,Liaoning Province)

机构地区：[1]沈阳理工大学信息科学与工程学院,辽宁沈阳110159

出　　处：《沈阳工程学院学报（自然科学版）》2020年第4期67-70,共4页Journal of Shenyang Institute of Engineering:Natural Science

基　　金：辽宁省自然科学基金指导计划项目(2019-ZD-0260)。

摘　　要：图像分割技术是图像领域底层任务到高层任务的过渡,在图像处理到图像分析过程中起到关键作用。文章针对图像自身特征介绍了4种不同类型的分割方法,并选取其最有代表性的算法,即Otsu阈值化算法、Canny边缘检测算法、区域生长算法以及分水岭算法进行了实验分析。实验结果表明,Otsu阈值化算法对于目标和背景灰度级相差较大的图像效果较好,区域生长算法适用于复杂图像的分割,分水岭算法对微弱边缘有较好的响应,而Canny边缘检测算法普适性较高,适用于绝大多数图像。Image segmentation technology is the transition from low-level task to high-level task in image field,which plays a key role in the process of image processing and image analysis.This paper introduces four different types of segmentation methods according to the characteristics of the image itself,and selects the most representative algorithms,namely Otsu thresholding algorithm,Canny edge detection algorithm,region growing algorithm and watershed algorithm.The results show that the Otsu thresholding algorithm is better for the image with large gray level difference between target and background,the Region growing algorithm is suitable for complex image segmentation,the watershed algorithm has good response to weak edge,and the Canny edge detection algorithm has high universality and is suitable for most images.

关 键 词：图像分割技术 Canny边缘算法 Otsu阈值化算法 区域生长算法 分水岭算法 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]