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自适应最小误差阈值分割算法
引用本文:龙建武,申铉京,陈海鹏.自适应最小误差阈值分割算法[J].自动化学报,2012,38(7):1134-1144.
作者姓名:龙建武  申铉京  陈海鹏
作者单位:1. 吉林大学计算机科学与技术学院 长春 130012;
2. 吉林大学符号计算与知识工程教育部重点实验室 长春 130012
基金项目:国家自然科学基金(60973090);吉林省自然科学基金(201115025);教育部重点实验室开放基金(450060445325);吉林大学研究生创新基金(20111063,20121104)资助~~
摘    要:对二维最小误差法进行三维推广, 并结合三维直方图重建和降维思想提出了一种鲁 棒的最小误差阈值分割算法. 但该方法为全局算法, 仅适用于分割均匀光照图像. 为 提高其自适应性, 本文采用Water flow模型对非均匀光照图像进行背景估计, 以此获 得原始图像与背景图像的差值图像, 达到降低非均匀光照对图像分割造成干扰的目的. 为进 一步提高分割性能, 本文对差值图像采用γ 矫正进行增强, 然后采用鲁棒最小误差 法进行全局分割, 从而完成目标提取. 最后本文对均匀光照下以及非均匀光照下图像进行了 实验, 并与一维最小误差法、二维最小误差法、三维直方图重建和降维的Otsu阈值分割 算法、灰度波动变换自适应阈值方法以及一种改进的FCM方法在错误分割率和运行时间上进 行了对比. 实验结果表明, 相对于以上方法, 本算法的分割性能均有明显提升.

关 键 词:图像分割  自适应阈值分割  Water  flow模型  最小误差法
收稿时间:2011-9-22
修稿时间:2012-2-8

Adaptive Minimum Error Thresholding Algorithm
LONG Jian-Wu,SHEN Xuan-Jing,CHEN Hai-Peng.Adaptive Minimum Error Thresholding Algorithm[J].Acta Automatica Sinica,2012,38(7):1134-1144.
Authors:LONG Jian-Wu  SHEN Xuan-Jing  CHEN Hai-Peng
Institution:1. College of Computer Science and Technology, Jilin University, Changchun 130012;
2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012
Abstract:A robust minimum error thresholding method is proposed to combine the three-dimensional (3D) minimum error thresholding scheme based on 2D method with the principle of rebuilding and dimension reduction of the 3D histogram. Considering the global behavior of this approach and its ability to process even illumination images only, a water flow model is used to estimate the background of uneven illumination images for improving adaptability of the proposed method. Then, the difference image between the original image and background can be readily obtained to reduce the interference of uneven illumination during the binarization process. To improve execution performance of the segmentation procedure, gamma correction is employed to enhance image in addition to a global segmentation using robust minimum error thresholding algorithm. Subsequently, image segmentation tests are carried out with even and uneven illumination, and then comparison on misclassification error and time expenditure are performed between the proposed method and other approaches, i.e., 1D/2D minimum error thresholding, Otsu thresholding algorithm based on 3D histogram rebuilding and dimensionality reduction, adaptive gray wave transformation thresholding scheme, as well as a modified FCM method. The results show that the proposed approach yields better thresholding performance than those methods.
Keywords:Image segmentation  adaptive thresholding  water flow model  minimum error method
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