Description: matching shape can be subdivided between two approaches: feature-based and template-based matching. The feature-based approach uses the features of the search and template image, such as edges or corners, as the primary match-measuring metrics to find the best matching location of the template in the source image. The template-based, or global, approach, uses the entire template, with generally a sum-comparing metric (using SAD, SSD, cross-correlation, etc.) that determines the best location by testing all or a sample of the viable test locations within the search image that the template image may match up to. Platform: |
Size: 10481664 |
Author:gislam |
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Description: 毕业设计，基于内容的图像检索，支持的检索特征包括 sift,颜色直方图，灰度矩阵，HU不变矩，边缘方向直方图，检索方法使用K-means和K-D树两种，需要OPENCV支持，运行时请先选定一个文件夹来生成特征库，特征库用access数据库保存，只支持JPG文件-Graduate design, content-based image retrieval, search features, including support sift, color histogram, gray matrix, HU moment invariants, edge direction histogram, retrieval method using the K-means and KD trees are two kinds of needs OPENCV support Please select a runtime folder to generate the feature library, feature library with access database save, only supports JPG files Platform: |
Size: 359424 |
Author:平天羽 |
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Description: 本算法在Matlab2008b 环境下实现。包括main,san 和ssd 三个函数。
这次实现的算法并不是比较两个已经知道的点是否匹配，而是已知一个图形中的特征点，在另外一个图像中找到与其最匹配的点，匹配度用SAD 或者SSD 来度量。
main.m 是程序的入口，包括生成和读入实验数据，调用sad 函数和ssd 函数求匹配点，最后绘
制出最后的结果。sad.m 是用sad 度量方法在另一图中求解匹配点的函数实现，有3 个参数
y=sad(x,image1,image2)。其中x 是代匹配的数据，image1 是图像1 数据，image2 是图像2 数据。
从理论上分析，ssd 比sad 算法要复杂一点，经过测试，在一幅640*480 的图像中寻找10 个匹配点数据SAD 用时25.062519 秒，SSD 用时25.291432 秒。-The algorithm Matlab2008b environment to achieve. Including the main, san, and ssd three functions.
The implementation of the algorithm is not the point of comparing two matches already know, but the known feature points in a graphic, an image found in the other match with the most points, matching measured with the SAD or SSD.
main.m is the entry, including the generation and reading test data, call the sad ssd function evaluation functions and matching points, and finally draw the final result. sad.m measure is sad figure in another match point in the function implementation to solve, there are three parameters
y = sad (x, image1, image2). Where x is the generation of matching data, image1 is the data image 1, image2 is the image 2 data.
From the theoretical analysis, ssd little more complicated than the sad algorithm, tested in a 640* 480 images of 10 match points in the search for data using time 25.062519 seconds SAD, SSD with time 25.291432 seconds.
Platform: |
Size: 3024896 |
Author:qqqqqq |
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Description: Template matching is concerned with measuring the similarity
between patterns of two objects. This paper proposes a massively
parallel approach to pattern recognition with a large
template set. A class of image recognition problems inherently
needs large template sets, such as the recognition of Chinese
characters which needs thousands of templates. The proposed
algorithm is based on the SIMD-SM-R machine or the SIMD
machine with broadcasting abilities, which is the most popular
parallel machine to date, using a multiresolution method to
search for the matching template. The approach uses the
pyramid data structure for the multiresolution representation of
templates and the input image pattern. For a given image it
scans the template pyramid searching for the match. Implementation
of the proposed scheme is described. Platform: |
Size: 591872 |
Author:silkan_h |
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Search - two image match search