The contents in the paper include acoustic image preprocessing, feature extraction of texture and shape, and classifier design.
内容涉及声图像的预处理、纹理和形状特征的提取，以及分类器的设计等。
The description and extraction of SAR image texture feature is important to texture segmentation.
SAR图像纹理特征的描述和提取是纹理分割的关键。
The techniques for realization of OWT and applications in image feature extraction and texture segmentation are explored thoroughly.
深入研究了光学子波变换的实现技术及其在图象特征提取和纹理图象分割等方面的应用。
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 extraction of forest texture feature from color forest aerial remote sensing images is an important part of the automatic interpretation of forest types achieved by computer.
彩色森林航空遥感图像中森林纹理特征提取是实现计算机自动判读的重要环节。
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.
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。
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.
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。
Feature extraction based on the histogram, texture, projection and shape of the defect images was also investigated.
研究了基于缺陷图像直方图、纹理、投影和形状的特征提取。
We do some researches on the algorithm of CBIR, and pay more attention on the global feature (including color, edge and texture feature) extraction and matching algorithms.
对基于内容的图象信息检索算法作了研究。重点阐述了对颜色、边缘、纹理等全局特征的提取与匹配算法。
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。
The excellent characteristics of rotation-invariance, good orientation selection and finite redundancy are fully utilized, and applied in texture feature extraction.
充分利用双树复小波变换的旋转不变性、良好的方向选择性以及有限的冗余等优点，将其有效地应用于纹理特征提取过程中。
This paper discusses the optimization of back propagaton neural networks for the grain texture feature, extraction in grain classification.
主要讨论了在谷物纹理识别中对神经网络的优化。
This paper discusses the optimization of back propagaton neural networks for the grain texture feature, extraction in grain classification.
主要讨论了在谷物纹理识别中对神经网络的优化。
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