%0 Journal Article
%T Adaptive Minimum Error Thresholding Algorithm<br>自适应最小误差阈值分割算法
%A LONG Jian-Wu
%A SHEN Xuan-Jing
%A CHEN Hai-Peng
%A <br>龙建武
%A 申铉京
%A 陈海鹏
%J 自动化学报
%D 2012
%I 
%X 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.
%K Image segmentation
%K adaptive thresholding
%K water flow model
%K minimum error method<br>图像分割
%K 自适应阈值分割
%K Water
%K flow模型
%K 最小误差法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=BC10CD3F59E055E22D31F6B44DABA26A&yid=99E9153A83D4CB11&vid=16D8618C6164A3ED&iid=DF92D298D3FF1E6E&sid=9F83C44826B8A7D6&eid=EEBB803F60D7DC4B&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=14