提取有效的特征用于纹理描述和分类一直是纹理分析的难点。
Extracting effective features for texture description and classification is always a difficult problem in texture analysis.
通过分析研究虹膜上丰富的纹理特征，结合不同的特征提取方法和分类器可以实现虹膜的身份认证。
Through analyzing the iris feature and combining with different feature extraction methods, we can realize iris recognition.
在基于小波的纹理分类算法的基础上，提出了逐点特征加权和活动窗口算法，使小波纹理分析能够用于高分辨率遥感影像的分类。
This paper discusses the shortage of conventional algorithms of texture classification based on wavelet transform, presents two improved approaches of point feature weighting and smart windows.
本文采用聚类分析法，由单谱图象的灰度和纹理参数构成特征向量，进行初始分类。
In this paper, cluster analysis was used for the initial classification in the feature space consisting of intensity of a MR image and its textures.
通过对结果的分析得到一些结论，这些结论对于选用和搭配影像纹理分类方法有一定的指导作用。
This paper used three kinds of texture classification methods to classify aerial image. Through experiment and analysis, we get some Suggestions for choosing texture classification method.
现在对于纹理图像的分析和分类广泛用于瑕疵定位、景物识别、图像检索、遥感图像分析等多个领域。
Nowadays, texture image analysis and classification is widely used in blemish locate, object recognize, image search, remote sensing image analysis and other fields.
然后在充分分析影像不同窗口纹理特征的基础上，提出应用组合纹理特征进行土地覆盖分类和居民地信息提取方法。
Then, land cover classification and residential areas extraction with combined texture feature was proposed by the sufficient analysis of the texture feature with different image window.
然后在充分分析影像不同窗口纹理特征的基础上，提出应用组合纹理特征进行土地覆盖分类和居民地信息提取方法。
Then, land cover classification and residential areas extraction with combined texture feature was proposed by the sufficient analysis of the texture feature with different image window.
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