The original texture coordinate and height values are already represented in tangent space, so the eye vector must be as well.
原始的材质贴图坐标和高度数据已经在切线空间中了，因此视向量最好也能如此。
According to the features of color texture image of cucumber disease in sunlight greenhouse, recognition of cucumber disease using Support Vector Machine (SVM) and chromaticity moments is introduced.
根据日光温室黄瓜病害的彩色纹理图像的特点，将支持向量机和色度矩方法用于识别黄瓜病害。
According to the features of color texture image of maize disease, a method of recognizing disease by using support vector machine (SVM) and chromaticity moments is introduced.
针对玉米病害叶片彩色纹理图像的特点，提出一种将支持向量机和色度矩分析应用于玉米病害识别的方法。
According to the features of color texture image of plant disease, recognition of plant disease using support vector machine (SVM) and chromaticity moments was introduced.
针对植物病害彩色纹理图像的特点，提出将支持向量机和色度矩分析方法相结合应用于植物病害识别中。
According to the features of color texture image of grape disease, a method of recognizing of grape dis-ease using support vector machine (SVM) and chromaticity moments is introduced.
针对葡萄病害彩色纹理图像的特点，提出一种将支持向量机和色度矩分析应用于葡萄病害识别的方法。
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。
Efficient extraction of image texture features are used on the following support vector machine classifier learning and training have a very important role.
图像纹理特征的有效提取对下面所用到的支持向量机分类器来进行学习和训练有非常重要的作用。
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。
Then the vector textures are performed image blending with texture of frame-buffer to generate rendering images, which are treated as the background images of IBFV algorithm instead of noise textures.
然后将这些矢量纹理作为IBFV算法中的背景图像，代替原来的噪声纹理与帧缓存中的纹理进行图像混合生成新图。
Then the vector textures are performed image blending with texture of frame-buffer to generate rendering images, which are treated as the background images of IBFV algorithm instead of noise textures.
然后将这些矢量纹理作为IBFV算法中的背景图像，代替原来的噪声纹理与帧缓存中的纹理进行图像混合生成新图。
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