Compared with the traditional method, it can describe the feature of residential texture more accurately by using vector information of residential texture.
与传统的居民地提取方法比，此算法用到了居民地纹理的矢量信息，从而更准确的刻画了居民地的纹理特性。
A new feature algorithm is proposed based on wavelet transform in HSI space to obtain a feature vector with combining information of color, texture and scale.
提出了基于小波变换的HSI空间的彩色纹理墙地砖图像的特征提取新算法，得到具有颜色、纹理和尺度融合信息的特征矢量。
Accordingly, we propose an improved feature extraction scheme, adopting the tone of filtered image combined with the texture features based on the GLCM of unfiltered image to form the feature vector.
针对这种情况，提出了一种改进的特征提取方法，将基于原图像的灰度级共生矩阵提取的纹理特征与滤波后图像的灰度特征进行组合用于分类。
The feature vector is composed of wavelet texture energy features, texture features based on the gray-level co-occurrence matrix and the tone of filtered SAR image by using tree wavelet.
该方法将树型小波中颇纹理能量特征、灰度共生矩阵特征、树型小波滤波后的灰度组成的特征矢量对SAR图像进行分类。
Compared with the traditional method, it can describe the feature of residential texture more accurately by using vector information of residential texture. The results show th...
与传统的居民地提取方法比，此算法用到了居民地纹理的矢量信息，从而更准确的刻画了居民地的纹理特性。
Compared with the traditional method, it can describe the feature of residential texture more accurately by using vector information of residential texture. The results show th...
与传统的居民地提取方法比，此算法用到了居民地纹理的矢量信息，从而更准确的刻画了居民地的纹理特性。
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