形状特征提取和表示是基于内容图像检索的重要研究内容之一。
Shape feature extraction and description are one of important research topics in content-based image retrieval.
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.
彩色形态变换作为一种数学形态学方法在彩色空间的延拓，可有效地应用于图象处理、图象编码和目标形状特征提取等。
As an extension of mathematical morphology to color space, the proposed transformation can be efficiently used in color image processing, image encoding and shape features extraction of objects, etc.
其次，本文研究了区域形状特征提取及基于PCA的特征选择方法，通过对区域特征进行优化选择，构造准确描述目标特性且维数较低的特征。
Secondly, the method of region feature extraction and optimal feature selection are studied based on PCA, and the effective features are constructed for the target by the analysis of the PCA.
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.
以颅骨边界形状为识别对象，运用不同的算法进行特征提取，并结合实验结果对不同算法进行了分析比较。
The border shape of skull is taken as identification target, extract characteristic depend on different algorithms, analyze and compare the result of different experiments.
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.
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.
特征的选取取决于特征提取的算法，本文采用的是14点主动形状模型。
The selection of features depends on the algorithm of feature extraction , and we use the 14-point ASM(Active Shape Model) in this paper.
在特征提取中，综合考虑了目标区域的对比度、面积、位置、轮廓、形状和亮度均匀性等特征量，建立了一种新的斑痕缺陷模型。
In feature extraction, a new "blemish" model is proposed, which takes several features into account, such as the contrast, area, location, outline, shape and the luminance uniformity of the object.
特征提取时，基于图像本身蕴含的信息复杂且庞大，主要研究了如何充分、有效描述图像的颜色、形状特征。
When extracting the images 'feature, we major research on how to fully and effectively describe the image color, shape features, as the image itself is complex and contains mass information.
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.
基于形状分布算法提出了一种基于点对分布的三维模型特征提取算法。
A new method of feature extraction in 3d model was proposed based on point-pairs distribution.
利用三维部件与其外包立方盒体积比作为特征提取的依据，提出了一种实例检索算法，能够处理形状不规则的产品部件实例。
A case retrieval algorithm is proposed which uses the volume ratio of 3-dimension component and its bounding box to extract feature values.
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.
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.
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