关于图像分割算法的研究
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摘要
近些年来人工智能在计算机领域内,得到了越来越广泛的重视。图像处理是人工智能领域中一个重要的分支。图像分割是进行图像分析处理的第一步,也是机器视觉的重要步骤。作为特征提取和图像识别的基础,图像分割的处理结果的研究一直是数字图像处理技术研究中的热点和焦点。为了研究图像分割的方法,采用了基于边界的图像分割、基于区域的图像分割、基于阈值的图像分割和基于聚类的图像分割方法,并作出分析。
In recent years, artificial intelligence has been paid more and more attention in the field of computer. Image processing is an important branch of artificial intelligence. Image segmentation is not only the first step of image analysis and processing, but also an important step of machine vision. As the basis of feature extraction and image recognition, the research of image segmentation processing results has always been a hot spot and focus in the research of digital image processing technology. Image segmentation methods include boundary-based image segmentation, region-based image segmentation, threshold-based image segmentation and clustering-based image segmentation. This paper will introduce these methods in turn, and give an analysis of their advantages and disadvantages.
In recent years, artificial intelligence has been paid more and more attention in the field of computer. Image processing is an important branch of artificial intelligence. Image segmentation is not only the first step of image analysis and processing, but also an important step of machine vision. As the basis of feature extraction and image recognition, the research of image segmentation processing results has always been a hot spot and focus in the research of digital image processing technology. Image segmentation methods include boundary-based image segmentation, region-based image segmentation, threshold-based image segmentation and clustering-based image segmentation. This paper will introduce these methods in turn, and give an analysis of their advantages and disadvantages.
引文
[1] 林开颜,吴军辉,徐立鸿.彩色图像分割方法综述[J].中国图象图形学报,2005,10(1):1-10.
[2] 冈萨雷斯.数字图像处理[M].阮秋琦,译.北京:电子工业出版社,2005.
[3] Vincent L,Soille P.Watersheds in digital spaces:an efficientalgorithm based on immersion simulations[J].IEEE Transac-tions on Pattern Analysis and Machine Intelligence,1991,13(6):583-598.
[4] 李了了,邓善熙,丁兴号.基于大津法的图像分块二值化算法[J].2005,21(8):76-77.
[5] 林敏,陈建新,陈哲亮.基于边缘检测与分裂合并的图像分割算法[J].电子技术应用,2011,37(7):130-133.
[6] 石荣刚,李志远,江涛.图像分割的常用方法及其应用[J].现代电子技术,2007(12).
[7] 张建光,李永霞.基于区域的图像分割[J].信息技术,2011(26):13.
[8] 张建光.基于区域的图像分割[J].科技资讯,2011(26):12-13.
[9] 赵凌君,贾承丽.SAR图像边缘检测方法综述[J].中国图像图形学报,2007,12(12):2042-2049.
[10] 苏士美,吕雪扬.骨髓细胞图像的小波变换与K-means聚类分割算法[J].2015,36(4):15-18.
[2] 冈萨雷斯.数字图像处理[M].阮秋琦,译.北京:电子工业出版社,2005.
[3] Vincent L,Soille P.Watersheds in digital spaces:an efficientalgorithm based on immersion simulations[J].IEEE Transac-tions on Pattern Analysis and Machine Intelligence,1991,13(6):583-598.
[4] 李了了,邓善熙,丁兴号.基于大津法的图像分块二值化算法[J].2005,21(8):76-77.
[5] 林敏,陈建新,陈哲亮.基于边缘检测与分裂合并的图像分割算法[J].电子技术应用,2011,37(7):130-133.
[6] 石荣刚,李志远,江涛.图像分割的常用方法及其应用[J].现代电子技术,2007(12).
[7] 张建光,李永霞.基于区域的图像分割[J].信息技术,2011(26):13.
[8] 张建光.基于区域的图像分割[J].科技资讯,2011(26):12-13.
[9] 赵凌君,贾承丽.SAR图像边缘检测方法综述[J].中国图像图形学报,2007,12(12):2042-2049.
[10] 苏士美,吕雪扬.骨髓细胞图像的小波变换与K-means聚类分割算法[J].2015,36(4):15-18.