红外图像统计阈值分割方法

Statistical Thresholding Method for Infrared Images

经典的统计阈值方法采用某种形式的类方差和作为阈值选择的准则,未考虑实际图像的特性,对目标和背景具有相似统计分布的图像的分割效果不甚理想。为此,利用阈值分割后两个类的标准偏差定义了一个新的阈值选择准则,并通过最小化此准则选择出最佳分割阈值。通过一系列实际图像上的实验结果表明,与现有的几种经典阈值分割方法相比,本方法分割图像的效果更好,尤其是对红外图像分割的效果更为明显。

Classic statistical thresholding methods take class variance sum of some form as criterions for threshold selection. They don＇t take special characteristic of practical images into account and fail to get ideal results when segmenting a kind of image having similar statistical distributions in the object and background. In order to eliminate the above limitation of classic statistical approaches,a novel statistical criterion was defined by utilizing standard deviations of two thresholded classes, and the optimal threshold was determined by minimizing it. Experiments on a variety of infrared images and general real world images show that our method outperforms the existing classic thresholding methods in segmentation quality, especially for infrared images.