Texture discrimination with multidimensional distributions of signed gray-level differences
Texture discrimination with multidimensional distributions of signed gray-level differences
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DOI：
10.1016/s0031-3203(00)00010-8
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发表时间：
2001-03-01
影响因子：
8
通讯作者：
Pietikäinen, M
中科院分区：
文献类型：
--
作者：
Ojala, T;Valkealahti, K;Pietikäinen, M
灰度差统计已成功应用于许多纹理分析研究中。在本文中，我们提议使用有符号的灰度差及其多维分布来描述纹理。与早期基于灰度共生矩阵或绝对灰度差直方图的相关方法相比，本方法具有重要优势。针对困难的纹理分类和有监督的纹理分割问题所进行的实验表明，与诸如共生矩阵、高斯马尔可夫随机场或加博尔滤波等主流方法相比，我们的方法具有非常良好且稳健的性能。（C）2001模式识别学会。由爱思唯尔科学有限公司出版。保留所有权利。
The statistics of gray-level differences have been successfully used in a number of texture analysis studies. In this paper we propose to use signed gray-level differences and their multidimensional distributions for texture description. The present approach has important advantages compared to earlier related approaches based on gray level cooccurrence matrices or histograms of absolute gray-level differences. Experiments with difficult texture classification and supervised texture segmentation problems show that our approach provides a very good and robust performance in comparison with the mainstream paradigms such as cooccurrence matrices, Gaussian Markov random fields, or Gabor filtering. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.