An Effective Method of Threshold Selection for Small Object Image

The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.     

一种可有效分割小目标图像的阈值选取方法

目标检测与识别中常遇到目标与背景大小之比很小的小目标图像分割问题,此时现有的阈值分割方法几乎都失效.为此,提出了一种基于背景与目标的面积差和类内方差的小目标图像分割阈值选取方法.指出了目前图像阈值分割方法不能有效分割小目标图像这一缺陷,给出了基于背景与目标面积差和类内方差的一维直方图、二维直方图区域直分及更为有效的二维...     

基于混沌粒子群优化的倒数熵阈值选取方法

基于信息熵的方法是一类重要的阈值选取方法,但现有的最大熵方法存在无定义值问题。为此,提出了基于倒数熵的阈值选取方法。首先给出了倒数熵的定义及一维阈值选取方法,导出了基于二维直方图区域直分及区域斜分的倒数熵阈值选取算法公式;然后考虑到二维倒数熵分割运算量较大,提出利用混沌小生境粒子群算法来寻找最优阈值,避免了算法早熟,提高了搜索精度和算法效率。实验结果表明:二维倒数熵阈值选取的斜分方法在抗噪性和运算时间上优于直分方法;而与基于粒子群优化的二维最大熵方法相比,本文提出的基于混沌小生境粒子群优化的二维倒数熵斜分法在运行时间上降低了约40%,分割效果更佳。      

基于双树复小波变换和混沌粒子群优化的红外小目标检测

针对存在背景干扰和噪声情况下的红外图像弱小目标检测问题,提出了基于双树复小波变换和混沌粒子群优化的检测方法。该方法一方面先基于双树复小波变换对原始图像进行去噪,再利用Top-hat算子抑制背景;另一方面先利用Top-hat算子抑制原始图像的背景,经双树复小波去噪后,再进一步使用Top-hat算子。将上述两方面得到的图像求和即为预处理图像。然后基于混沌粒子群优化的类内绝对差及背景与目标面积差的阈值选取方法分割预处理图像。大量实验结果表明,与基于小波和形态学的红外目标检测方法相比,该方法抗噪性强,具有更为优越的检测性能。