织物图像的纹理特征提取是一个关键的步骤。
Extracting of textural features of fabric images is a key process.
局部傅里叶变换(LFT)是一种重要的纹理特征提取方法。
Local Fourier transform (LFT) is an important texture feature extracting method.
提出了基于人类视觉系统的自然场景图像颜色和纹理特征提取方法。
A nature scene image color-texture feature extraction method based on human visual system (HVS) was proposed.
利用图像处理技术完成织物疵点检测是一个纹理特征提取和模式识别问题。
Fabric defect inspection using image processing technology is a problem of extracting of textural features and pattern recognition.
通过将对数极坐标和傅立叶变换结合，完成旋转不变的傅立叶纹理特征提取。
In this paper, we incorporate Fourier transform with Log-Polar transform to achieve rotation invariant texture analysis.
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。
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.
该文提出一种基于最小二乘及区域分割的多光谱图像纹理特征提取与比较的方法。
A new method based on least squares and region segmentation is proposed to retrieve and compare the texture features of multi-spectral images in this paper.
该文提出一种基于最小二乘及区域分割的多光谱图像纹理特征提取与比较的方法。
This paper proposes a novel method of fusing panchromatic and multispectral images based on contourlet and IHS.
提出了一种基于纹理特征提取的图像处理技术和神经网络结合进行钢丝绳检测的新方法。
A new method of picking up the texture characteristics and using neural network to inspect the condition of steel wire rope is provided.
该方法在对SPOT5遥感影像进行纹理特征提取的基础上，构建了结合多窗口纹理的SVM模型。
In this method, the SVM classification model combined with texture analysis is established on the basis of texture extraction from SPOT5 remote sensing image.
边缘提取在图像分割、纹理特征提取、形状特征提取、图像识别、计算机视觉等方面的重要性十分突出。
Edge detection is very significant in the domain of image fragmentation, texture detection, computer view and so on.
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。
The excellent characteristics of rotation-invariance, good orientation selection and finite redundancy are fully utilized, and applied in texture feature extraction.
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.
结果表明，这种特征提取方法能有效地提取灰度图像目标纹理特征，并且对噪音和形状的变化具有强鲁棒性。
The results indicate this method can effectively extract texture feature of gray image target, and has robust to noise and change of target shape.
研究了基于缺陷图像直方图、纹理、投影和形状的特征提取。
Feature extraction based on the histogram, texture, projection and shape of the defect images was also investigated.
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。
The techniques for realization of OWT and applications in image feature extraction and texture segmentation are explored thoroughly.
首先叙述了基于内容的图像检索的系统模型和特点，接着针对颜色、纹理和形状进行了概率特征提取、相似度量等的进一步具体分析讨论。
It presents the model and feature of content-based image retrieval system, and then discusses some methods of feature abstraction and similarity measurement based on color, texture and shape.
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。
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.
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。
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.
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。
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.
传统的图像特征提取方法，基本上是围绕图像的颜色、纹理、形状和空间关系来展开的。
Current CBIR systems generally make use of lower-level features like color, texture, shape and space relationship.
实验结果表明，该算法特征提取和识别速度快，尤其对于清晰度不高、现场拍摄的纹理图像具有较好的效果。
Result shows that this algorithm is fast in feature extraction and identification, with especially good performance at low quality images.
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.
在图像特征提取上改进并提出了三种特征的提取：纹理特征，灰度直方图均值化特征，图像的主成分特征。
The features concerned are such as texture feature, gray histogram feature and features derive form the principle component analysis.
该算法以具有多分辨率特性的小波包为基础，结合结构法的纹理元分析方式，采用区域质心完成局部特征提取，最后使用投影法进行整体的特征提取。
The texture feature was obtained by statistically projecting the local centroid which was referred from the structural texture analysis methods based on the wavelet package transform.
在特征提取中有色彩，纹理，形状和空间关系等特征，而形状特征能给人们带来非常直观的信息。
There are color, texture, shape and spatial relationship. Among these features, the shape can bring people attractive visual information.
基于多方向纹理边缘检测的特征提取方法利用了纹理的位置、灰度、大小、方向、相关性等多种结构特征，因此提取出来的可区分性特征使虹膜识别的准确性得到大幅度的提高。
The method based on the multidirectional edge detection uses the position, gray, size, direction and relativity of texture, so the dividing features ensure to increase the precision much more.
基于多方向纹理边缘检测的特征提取方法利用了纹理的位置、灰度、大小、方向、相关性等多种结构特征，因此提取出来的可区分性特征使虹膜识别的准确性得到大幅度的提高。
The method based on the multidirectional edge detection uses the position, gray, size, direction and relativity of texture, so the dividing features ensure to increase the precision much more.
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