Description: Feature extraction is a key issue in contentbased
image retrieval (CBIR). In the past, a number of
texture features have been proposed in literature,
including statistic methods and spectral methods.
However, most of them are not able to accurately capture
the edge information which is the most important texture
feature in an image. Recent researches on multi-scale
analysis, especially the curvelet research, provide good
opportunity to extract more accurate texture feature for
image retrieval. Curvelet was originally proposed for
image denoising and has shown promising performance.
In this paper, a new image feature based on curvelet
transform has been proposed. We apply discrete curvelet
transform on texture images and compute the low order
statistics from the transformed images. Images are then
represented using the extracted texture features. Retrieval
results show, it significantly outperforms the widely used
Gabor texture feature. Platform: |
Size: 1426432 |
Author:Swati |
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Description: 表面缺损检测对保证产品的使用性能、完整性和安全性具有重要意义 本文将表面
缺损类型总结为结构缺损、几何缺损、颜色缺损和纹理缺损等几类，并进行特征分析。在此基础上，从基于灰度特征、形态特征、色度特征和纹理特征等几方面对表面缺损的图像检测方法进行综-Surface defect detection products to ensure the use of performance, integrity and security will be of great significance to this article summed up the type of surface defect structure of defects, defect geometry, color and texture defects, such as several types of defects, and analysis. On this basis, from the gray-scale-based features, shape features, color features and texture features, such as several images of the surface defect detection method for fully mechanized Platform: |
Size: 391168 |
Author:wangzhongmei |
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Search - texture analysis color matlab