| 纹理图像多尺度灰度共生矩阵步长参数的研究 |
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| 引用本文： | 李丽宏,谢东阳,王琳,潘飞扬,王鹏涛.纹理图像多尺度灰度共生矩阵步长参数的研究[J].河北工程大学学报,2021,38(3):108-112. |
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| 作者姓名： | 李丽宏 谢东阳 王琳 潘飞扬 王鹏涛 |
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| 作者单位： | 河北工程大学 信息与电气工程学院, 河北 邯郸 056038,河北工程大学 信息与电气工程学院, 河北 邯郸 056038,河北工程大学 信息与电气工程学院, 河北 邯郸 056038,河北工程大学 信息与电气工程学院, 河北 邯郸 056038,河北工程大学 信息与电气工程学院, 河北 邯郸 056038 |
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| 基金项目： | 河北省科技计划项目（20475702D）；河北省高等学校科学技术研究项目（ZD2014081） |
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| 摘 要： | 将小波变换多尺度理论用于分析确定共生矩阵最佳步长参数值，利用小波变换对原始图像进行分解，根据具体纹理图像，选择合适小波子图像（近似图像或其细节子图像）进行纹理分析，通过计算分解图像的纹理特征参数（对比度）确定最佳步长参数。当步长参数为最优值时，计算所得纹理特征参数值将处于周期极值位置，其利于纹理分析。相对于原始图像，分解图像数据量小，在寻优共生矩阵最佳步长参数时，计算复杂度及时间消耗都有所降低。实验验证，基于小波分解图像所得最佳步长参数值是精确的。
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| 关 键 词： | 纹理图像 灰度共生矩阵 小波变换 多尺度理论 步长参数 |
| 收稿时间： | 2021/4/19 0:00:00 |
Research on Step Parameter of Multi-scale Gray Level Co-occurrence Matrix for Texture Image |
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| Authors: | LI Lihong XIE Dongyang WANG Lin PAN Feiyang and WANG Pengtao |
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| Affiliation: | School of Information and Electronic Engineering, Hebei University of Engineering, Handan, Hebei 056038, China,School of Information and Electronic Engineering, Hebei University of Engineering, Handan, Hebei 056038, China,School of Information and Electronic Engineering, Hebei University of Engineering, Handan, Hebei 056038, China,School of Information and Electronic Engineering, Hebei University of Engineering, Handan, Hebei 056038, China and School of Information and Electronic Engineering, Hebei University of Engineering, Handan, Hebei 056038, China |
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| Abstract: | We apply the multi-scale theory of wavelet transform to determine the optimal step parameter. More specifically, we decompose the original image using wavelet transform and according to the specific texture image, select the appropriate wavelet sub-image (approximate image or its detailed sub-image) for texture analysis. The texture feature parameter (contrast) of the decomposed image is utilized to determine the optimal step parameter. When the step parameter is optimal, the texture feature parameter reaches the extreme value of the period which is beneficial to texture analysis. As the amount of data in the decomposed image is less than that in the original image, both the computation complexity and the time consumed in finding the optimal step parameter are reduced. Furthermore, experimental results show that the optimal step parameter of the wavelet decomposed image is accurate. |
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| Keywords: | texture image gray level co-occurrence matrix wavelet transform multi-scale theory step parameter |
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