Conclusion Ultrasonic image texture features were different between normal and infarct myocardium.
结论正常与梗死心肌超声图像纹理特征不同。
Base on generalization and analysis of co-occurrence matrix algorithms, a method is proposed that could make fast acquisition of target image texture features and thus implement image retrieval.
在总结和分析共生矩阵算法的基础上，提出了一个快速获取目标图像纹理特征，进而实现图像检索的方法。
The proposed transform could represent the geometrical features such as edges and texture more sparsely, which is of great benefit to image compression.
多方向多尺度变换能以更稀疏的方式表示图像的边缘和纹理等几何特征，有利于图像压缩。
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。
At first, extracting features of chromaticity moments of texture image of grape disease is done, then classification method of SVM for recognition of grape disease is discussed.
首先利用色度矩提取葡萄病害叶片纹理图像的特征向量，然后将支持向量机分类方法应用于病害的识别。
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.
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。
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。
At first, the extracting features of chromaticity moments of texture image of maize disease is done, then classification method of SVM for recognition of maize disease is discussed.
首先利用色度矩提取玉米病害叶片纹理图像的特征向量，然后将支持向量机分类方法应用于病害的识别。
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。
Texture is one of the important visual features in image analysis.
纹理作为一种重要的视觉特征，广泛应用于图像分析。
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。
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.
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。
A new image retrieval method by using Max correlation min distance to combine together texture features, Gaussian density characteristics and face detection of images for image retrieval is presented.
提出利用最大相关最小距离将图像的纹理特征、高斯密度特征与人脸检测相结合的算法进行图像检索。
An effective method for image segmentation based on color and texture features is proposed.
文章提出了一种有效的基于颜色和纹理综合特征的图像分割方法。
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。
A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed.
提出了一种结合颜色、纹理和形状特征的细胞病理图像检索方法。
To avoid the influence of background, a method of image background remove is proposed, in this paper applying color histogram and LBP algorithm to extract color and texture features.
提出基于分割算法的图像背景去除技术，用于减少背景对提取特征的干扰；用颜色直方图、LBP算法来提取图像的颜色与纹理特征。
Finally, based on the work of above, proposed a method of description and extraction image color and texture features in the dual tree complex wavelet transformation domain.
最后，在上面工作的基础上，提出了基于对偶数复小波域的图像的颜色和纹理特征的描述和提取方法。
Appropriate texture features are selected with this method to serve image classification and simulative results are discussed.
使用这一方法挑选合适的纹理特征用于图像分类，并对结果进行分析。
In this paper, out methods for image retrieval using center and texture features are firstly discussed. Furthermore, a new method for image retrieval using combined center and texture is proposed.
本文介绍了基于纹理特征的特征提取方法和中心特征的提取方法，并进而提出了一种综合利用上述两个特征共同进行检索的方法。
Texture is an important item of image information, texture-based image retrieval has been an active research area, and the similarity comparison of texture features is a key to image retrieval.
纹理是图像的重要属性，基于纹理特征检索图像是当前的研究热点，对图像的纹理进行相似性比较是进行图像检索的关键。
The statistical features of the texture images is computed directly from DCT coefficients, and used for image retrieval.
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。
Fractal dimension of wood texture image can represent a lot of texture features, and it is an important characteristic parameter of timber species.
木材纹理图像的分形维数可以代表木材很多的纹理特征，它是木材树种的一项重要数字特征参数。
On the basis of which, an improved co-occurrence matrix and histogram are developed to extract the texture and shape features for the image retrieval.
在此基础上，采用一种改进的基于纹理基元的共生矩阵来获取纹理特征，并结合纹理基元的形状直方图来进行图像检索。
Likewise, granular computing theory is trying to apply to texture features extraction and accurate segmentation of chest HRCT image.
同样，粒计算理论也在被尝试应用于HRCT图像纹理特征值提取和肺部组织分割。
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。
The main features used for image retrieval are color, texture and shape. This thesis looks into image retrieval techniques based on color and texture.
基于内容的图像检索技术主要利用图像的颜色、纹理和形状特征对图像进行检索，论文重点研究基于颜色和纹理特征的图像检索技术。
We employ this method to extract the features of such texture image and use weighted Euclidean distance classifier to fulfill the matching and identification task.
用该方法提取纹理图像的特征，并使用加权欧式距离分类器来完成匹配工作。
Major features appearing on the image include contrails light blue, deep clouds white, high clouds light blue and fog mist low clouds red - smooth in texture.
图中可见凝结尾迹浅蓝色，厚云白色，高层云浅蓝色及雾低层云红色，质感较柔和。
Firstly, texture and shape features are extracted at the pixel level, and spectral features are extracted at the object level based on multi-scale image segmentation maps.
首先在画素级上提取影像的纹理和形状结构特征，在构建的多尺度分割集影像上提取物件的区域光谱特征。
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