基于二维直方图和模糊熵准则的阈值化方法

Thresholding Using Two-Dimensional Histogram and Fuzzy Entropy Principle

摘要:

图像分割是图像分析、识别和理解的基础。图像分割主要是指将图像分成各具特性的区域并提取出感兴趣目标的技术,其研究多年来一直受到人们的高度重视；阈值化法是图像分割的一种重要方法,在图像处理与识别中广为应用；针对图像分割中细节往往被忽略导致后续处理困难的问题,基于模糊关系和最大模糊熵原理提出了一种阈值化方法,对二维直方图进行模糊分割；为了获得图像分割中的细节,提出的方法根据最大熵原则自动确定模糊区域和门限,进而获得二维模糊熵和遗传算法最优解,最后获得图像细节；通过对不同灰度水平和颜色类型图像进行实验比较,实验结果表明提出的方法优于二维非模糊方法和一维模糊熵分割法,得到该方法在图像分割中获得细节的结论。

Abstract:

Image segmentation is the basis of image analysis, recognition and understanding. Image segmentation, partitioning an image into different regions with some specific properties, has always been an important and challenging problem for many years. Thresholding is an important form of image segmentation and is used in the processing of image for many applications.This paper presents a thresholding approach by performing fuzzy partition on a two-dimensional (2-D) histogram based on fuzzy relation and maximum fuzzy entropy principle. The experiments with various gray level and color images have demonstrated that the proposed approach outperforms the 2-D nonfuzzy approach and the one-dimensional(1-D) fuzzy partition approach.