Texture Classification and Segmentation Based on Iterative Morphological Decomposition
Texture Classification and Segmentation Based on Iterative Morphological Decomposition
复制标题
基于迭代形态学分解的纹理分类和分割
DOI：
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发表时间：
1993
影响因子：
2.6
通讯作者：
J. Ronsin
中科院分区：
文献类型：
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作者：
Demin Wang;V. Haese;A. Bruno;J. Ronsin
摘要提出了一种基于灰度数学形态学的纹理分类和纹理图像分割的新方法。它定义了一种递归形态分解算法，该算法利用一组不同大小的结构元素，将纹理图像按照纹理基元的大小和灰度级分解为一系列分量图像。每个分量图像只包含一定大小的纹理基元，原始纹理图像可以由其所有分量图像的和精确地重建。从这些分量图像中可以提取出许多纹理特征，用于纹理分类和纹理图像分割。然后，本文提出了一种自适应纹理图像分割技术。提取纹理特征的窗口大小根据待分割纹理的误分类期望来选择。每个像素的窗口位置由其邻域确定。这种技术可以改善分割结果，特别是沿着纹理边界。实验结果表明，该方法计算速度快，对结构纹理和随机纹理的分类和分割都是有效的。尽管使用的特征很少，但已经获得了相当好的实验结果。
Abstract This paper presents a new method for texture classification and textured image segmentation based on grayscale mathematical morphology. It defines a recursive morphological decomposition algorithm by using a group of structuring elements with different sizes which decomposes a texture image into a series of component images according to texture primitive sizes and gray levels. Each component image contains only the texture primitives of a certain size, and the original texture image can be exactly reconstructed by the sum of all of its component images. Many texture features can be extracted from these component images for texture classification and textured image segmentation. This paper, then, proposes an adaptive textured image segmentation technique. The size of the window from which texture features are extracted is selected according to expectation of misclassification from the textures to be segmented. The window position for each pixel is determined by its neighborhood. This technique can improve segmentation results, especially along texture boundaries. The experimental results show that the method presented is fast in computation and efficient for classification and segmentation of both structural and random textures. Fairly good experimental results have been obtained, even though few features have been used.