在皮肤检测阶段，在总结前人工作的基础上，采用了一种有效的肤色检测模型，并在此基础上利用简单统计纹理特征进行皮肤检测。
In period of skin detection, we adopted an effective skin color model by studying the former work and based on this we carried out skin detection through simple and statistical texture character.
本文在现有的理论和文献基础上，研究了一种基于图像纹理统计特征分析的面部皮肤状态检测系统。
In this paper, depending on available theory and literature, it studied a kind of measuring system of facial skin condition based on statistical feature analysis of image texture.
为了更有效地利用图像的局部特征恢复被噪声感染的图像，基于图像局部纹理方向概率统计模型，提出一种针对混合噪声的非线性滤波算法。
In order to more effectively make use of local features to restore the noise-infected image, a nonlinear filtering algorithm based on local texture direction probability statistic model was proposed.
该方法在DCT压缩域，通过直接对DCT系数计算，获得图像纹理的统计特征，并作为检索的依据。
The statistical features of the texture images is computed directly from DCT coefficients, and used for image retrieval.
然后通过对基元特征进行加权投影统计，得到图像的方向性、对比度等纹理特征，这些特征可以更好的适应人类视觉特性。
The texture features such as direction and contrast of the image can be obtained from weighted projection statistics of primitive feature, which are more matched with human vision.
在使用模板匹配方法检测织物瑕疵的过程中，通过实时采集、分析织物的灰度图像，获得织物纹理的统计信息，并从中提取出正常纹理的特征。
In the detection of fabric flaw based on template matching, statistical information of natural and normal texture of the fabric can be extracted via collecting and analyzing the gray image.
介绍了一种利用局部沃尔什变换(LWT)提取图像纹理特征的新方法，给出lwt的定义，并分析了LWT系数的统计特性及其各阶矩的纹理鉴别性能。
A new texture feature extraction method using Local Walsh Transform (LWT) is presented. The definition of LWT is given. The statistical properties of LWT coefficients are analyzed.
文中根据SAR图像的纹理特征和相干斑噪声的统计特征，提出了一种基于区域分集的相干斑抑制算法。
According to the characteristics of SAN image details and speckle distribution, this peper puts forward a speckle-reduction algorithm based on region division.
在对图像纹理特征进行统计分析的基础上，本文提出了一种基于纹理分析的图像小波变换清晰度评价方法。
An image definition criterion using wavelet transform based on the texture analysis was proposed in this paper through the statistical analysis to the image texture characteristic.
该系统先对粮虫图像进行小波边缘提取，根据灰度共生矩阵和局部统计方法提取小波分割后的图像纹理特征。
Edge detction based on wavelet multi-scale identity is made. The statistics features based on regional gray and the co-occurrence matrix of gray level are taken as performing image segmentation.
在纹理特征提取方面，针对不同纹理特点分别采用了基于共生矩阵的统计纹理分析和基于小波变换的频谱纹理分析两种方法予以实现。
Similarly, the work of texture feature extraction is obtained by using co-occurrence matrix or frequency analysis based on wavelet transform depending on different characteristics of images.
可疑区域的静态纹理和动态纹理特征都累计为直方图形式，从而加强了特征的统计意义。
The static texture features and dynamic texture features of suspicious regions are represented with histograms, so as to improve the statistic sense of the features.
可疑区域的静态纹理和动态纹理特征都累计为直方图形式，从而加强了特征的统计意义。
The static texture features and dynamic texture features of suspicious regions are represented with histograms, so as to improve the statistic sense of the features.
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