摘要
针对当前图像分割算法存在的分割误差大、分割时间长及无法进行在线图像分割的不足,提出一种基于聚类分析和活动轮廓模型相融合的图像分割算法.首先,对原始图像进行去噪处理,采用聚类分析算法对原始图像进行粗分割,将粗分割结果作为活动轮廓模型的初始轮廓线;其次,将活动轮廓模型根据初始轮廓线对图像不同区域轮廓进行拟合,实现图像的精细分割;最后与聚类分析算法、活动轮廓模型以及当前经典图像分割算法进行对比测试实验.实验结果表明,本文算法克服了当前图像分割算法存在的缺陷,提高了图像分割效率和精度,对噪声不敏感,并具有较强的鲁棒性,图像整体分割效果显著优于对比算法。
In order to overcome the shortcomings of current image segmentation algorithms,such as large segmentation error and long segmentation time and inability to segment online image,we proposed an image segmentation al gorithm based on combination of clustering analysis and active contou r model. Firstly,the original image was denoised and roughly segmented by clustering analysis algorithm. The roughly segmented result was tak en as the initial contour line of the active contour model. Secondly, the active contour model was used to fit the contour of different regions of th e image according to the initial contour line to realize the fine segmentation of the image. Finally,it was compared with clustering analysis algorithm,active contour model and the curr ent classical image segmentation algorithm. The experimental results show that the proposed algorithm overcomes the shortcomings of the cur rent image segmentation algorithm,improves the efficiency and accuracy of image segmentation,it is insensitive to noise and has strong robustness. The overal l image segmentation effect is significantly better than that of the contrast algorithm.
作者
李鸿雁
唐娴
LI Hongyan;TANG Xian(Department of Computer Engineering,Shangqiu University Applied Scienceand Technology College,Kaifeng 475000,Henan Province,China;College of Computer Science and Technology,Shangqiu University,Shangqiu 476000,Henan Province,China)
出处
《吉林大学学报（理学版）》
CAS
北大核心
2019年第4期896-902,共7页
Journal of Jilin University:Science Edition
基金
河南省科技攻关项目(批准号:182102410064)
关键词
图像分割效率
噪声干扰
初始轮廓线
活动轮廓模型
聚类分析
image segmentation efficiency
noise interference
init ial contour line
active contour model
clustering analysis
