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CBIR-matlab-GUI

于 2013-01-05 发布 文件大小:1576KB
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代码说明：

  实现了一个基于颜色、纹理、形状综合特征的MatlabGUI图像检索系统。(Bulit an image retrieval system based on which combines these three features and realizes user-defined weights.)

文件列表：

井涛-20120802040-程序和论文
...........................\井涛-20120802040-基于颜色形状纹理综合特征的CBIR.doc,1834496,2013-01-03
...........................\基于综合特征的图像检索系统程序
...........................\..............................\area_max.asv,227,2013-01-03
...........................\..............................\area_max.m,222,2013-01-03
...........................\..............................\Contour_Track.m,105,2013-01-03
...........................\..............................\Fourier_texture2.m,557,2013-01-03





...........................\..............................\hsvfeature.m,849,2013-01-02
...........................\..............................\Morphology_modify.m,191,2013-01-03
...........................\..............................\Pic





...........................\..............................\...\14.JPG,8308,2008-11-07
...........................\..............................\...\15.jpg,8908,2008-11-07
...........................\..............................\...\16.jpg,7549,2008-11-07
...........................\..............................\...\17.JPG,7474,2008-11-07
...........................\..............................\...\18.JPG,7786,2008-11-07
...........................\..............................\...\19.JPG,4296,2008-11-07
...........................\..............................\...\2.jpg,6953,2008-11-07
...........................\..............................\...\20.JPG,3902,2008-11-07
...........................\..............................\...\21.jpg,5617,2008-11-07
...........................\..............................\...\22.jpg,6462,2008-11-07
...........................\..............................\...\23.JPG,26042,2012-11-19
...........................\..............................\...\24.JPG,2997,2008-11-07
...........................\..............................\...\3.jpg,7031,2008-11-07
...........................\..............................\...\4.jpg,13422,2008-11-07
...........................\..............................\...\5.JPG,8889,2008-11-07
...........................\..............................\...\6.jpg,5288,2008-11-07
...........................\..............................\...\7.jpg,6308,2008-11-07
...........................\..............................\...\8.JPG,9086,2008-11-07
...........................\..............................\...\9.JPG,9078,2008-11-07
...........................\..............................\Run.asv,19902,2013-01-03
...........................\..............................\Run.fig,29277,2013-01-03
...........................\..............................\Run.m,20001,2013-01-03
...........................\..............................\See_the_result.m,367,2013-01-03
...........................\..............................\Shape_7.asv,1943,2013-01-02
...........................\..............................\Shape_7_moment.m,1944,2013-01-02
...........................\..............................\Watershed.asv,2075,2013-01-02
...........................\..............................\Watershed.m,1443,2013-01-02
...........................\..............................\说明.txt,186,2013-01-04

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    视网膜血管检测的Gabor变换和机器学习，教程 本教程将演示如何Gabor变换和广义 的线性模型（GLM）可用于视网膜血管检测 图像。 ，我们将尝试检测视网膜血管从 的训练图像，首先，Gabor滤波器与图像卷积。 GLM将使用Gabor变换的图像特征确定 （独立变量）和容器的位置 为结果（因变量）。( Retinal Vessel Detection by Gabor Transform and Machine Learning, a Tutorial This tutorial will demonstrate how Gabor transforms and generalized linear model (GLM) can be used for detection of retinal vessels in images. Specifically, we will attempt to detect the retinal vessels a training image , by first, convoluting multiple Gabor filters with the image. A GLM will be determined using the Gabor transformed images as features (the independent variables), and the locations of the vessels as the outcome (the dependent variable).)
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    提出了一种基于图像边缘连接的线段检测方法。数字图像中的线段是由很多线段图元组成,在同一 条线段中,它们的方向是相同的并且受到真实线段的方向约束。因此可以通过提取小的线段图元, 然后分析它们 的连接特性, 进行线段的检测。首先用ADM 算法对图像进行边缘检测, 得到单像素宽的边缘 然后在4 个方向上 进行线段的检测 对初步得到的结果进行合并和验证, 去掉短的线段, 连接临近的线段。经过实验证明, 该方法能 够快速地提取图像中的线段,并且对噪声具有一定的鲁棒性(A line segment connecting a detection method based on image edges. The segment of the digital image is composed by many segments elements, their orientation is the same and by the real line segment direction constraint in the same segment. By extracting a small segment Element can then analyze their connection characteristics for the detection of the line segment. First with ADM algorithm for edge detection on the image to obtain a single-pixel-wide edge then in the four directions on the detection of the line segments merger and verification of the preliminary results obtained, removing the short line segments, the line segments connected near. The experiments show that this method can quickly extract the line segments in the image, and certain robustness to noise)
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  • CBIR-matlab-GUI
    实现了一个基于颜色、纹理、形状综合特征的MatlabGUI图像检索系统。(Bulit an image retrieval system based on which combines these three features and realizes user-defined weights.)
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