| 基于多层小波和共生矩阵的纹理表面缺损检测 |
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| 引用本文： | 韩彦芳,施鹏飞.基于多层小波和共生矩阵的纹理表面缺损检测[J].上海交通大学学报,2006,40(3):425-430. |
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| 作者姓名： | 韩彦芳 施鹏飞 |
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| 作者单位： | 上海交通大学,图像处理与模式识别研究所,上海,200240 |
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| 摘 要： | 提出一种利用多层小波和共生矩阵进行纹理表面缺损检测的有效方法.该方法首先将缺损图像在不同水平上进行小波分解,得到一系列低频子图像和高频细节子图像;然后计算和分析各水平上高频细节子图像的共生矩阵特征;最后选择低频子图像进行小波合成得到无纹理图像进行检测.实验证明,该方法能够快速准确地进行纹理缺损检测.
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| 关 键 词： | 图像识别 小波变换 共生矩阵 纹理分类 缺损检测 |
| 文章编号： | 1006-2467（2006）03-0425-06 |
| 收稿时间： | 2005-04-07 |
| 修稿时间： | 2005年4月7日 |
The Texture Defect Detection Based on Multi-level Wavelet Transform and Co-occurrence Matrix |
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| HAN Yan-fang,SHI Peng-fei.The Texture Defect Detection Based on Multi-level Wavelet Transform and Co-occurrence Matrix[J].Journal of Shanghai Jiaotong University,2006,40(3):425-430. |
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| Authors: | HAN Yan-fang SHI Peng-fei |
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| Abstract: | An efficient approach of using multi-level wavelet transform and co-occurrence matrix for texture defect detection was proposed.The defective image is firstly decomposed into approximation and detail sub-images at various levels by wavelet transform.Then,the co-occurrence matrix features of the detail subimages are computed and analyzed to decide the appropriate level at which the approximation sub-images are reconstructed into non-texture images for defect detection.The experimental results show that this approach is efficient both in speed and performance. |
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| Keywords: | image recognition wavelet transform co-occurrence matrix texture classification defect detection |
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