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GMS-Feature-Matcher-master

于 2021-05-06 发布

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下载积分: 1 下载次数: 2

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代码说明：

说明：利用GMS对特征点匹配进行约束，降低误匹配率，代码包含python版本，C++版本，matlab版本。(GMS is used to constrain the matching of feature points to reduce the error matching rate. The code includes Python version, C + + version and Matlab version.)

文件列表：

GMS-Feature-Matcher-master, 0 , 2020-06-10
GMS-Feature-Matcher-master\CMakeLists.txt, 270 , 2020-06-10
GMS-Feature-Matcher-master\LICENSE, 1512 , 2020-06-10
GMS-Feature-Matcher-master\README.md, 3306 , 2020-06-10
GMS-Feature-Matcher-master\data, 0 , 2020-06-10
GMS-Feature-Matcher-master\data\01.jpg, 63193 , 2020-06-10
GMS-Feature-Matcher-master\data\02.jpg, 37640 , 2020-06-10
GMS-Feature-Matcher-master\data\result_dogs_colorcode.png, 428540 , 2020-06-10
GMS-Feature-Matcher-master\data\result_dogs_lines.png, 387141 , 2020-06-10
GMS-Feature-Matcher-master\include, 0 , 2020-06-10
GMS-Feature-Matcher-master\include\gms_matcher.h, 8899 , 2020-06-10
GMS-Feature-Matcher-master\matlab, 0 , 2020-06-10
GMS-Feature-Matcher-master\matlab\Compile.m, 501 , 2020-06-10
GMS-Feature-Matcher-master\matlab\MexGMS.cpp, 2260 , 2020-06-10
GMS-Feature-Matcher-master\matlab\demo.m, 297 , 2020-06-10
GMS-Feature-Matcher-master\matlab\gms_match.m, 462 , 2020-06-10
GMS-Feature-Matcher-master\python, 0 , 2020-06-10
GMS-Feature-Matcher-master\python\gms_matcher.py, 13958 , 2020-06-10
GMS-Feature-Matcher-master\python\opencv_demo.py, 3562 , 2020-06-10
GMS-Feature-Matcher-master\src, 0 , 2020-06-10
GMS-Feature-Matcher-master\src\demo.cpp, 2834 , 2020-06-10

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2013-11-24 20:39:58下载

积分：1
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2008-03-03 22:26:01下载

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我自己做的一个bmp文件进行24位真彩转256灰度和二进制的程序，并能用sobel算法，识别物体的边界。 (I myself make a bmp file for 24-bit true color to 256 gray-scale and binary procedures and sobel algorithm can be used to identify object boundaries.)

2008-02-18 03:14:39下载

积分：1
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这个是图像处理中的hough变换的C++源代码，工需要的同学参考学习。(This is the image processing hough transform C++ source code, engineering students need to learn.)

2013-09-07 00:28:11下载

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CGAL，Computational Geometry Algorithms Library，计算几何算法库，设计目标是，以C++库的形式，提供方便，高效，可靠的几何算法。CGAL可用于各种需要几何计算的领域，如计算机图形学，科学可视化，计算机辅助设计和建模，地理信息系统，分子生物学，医学成像，机器人运动规划，网格生成，数值方法等等。　　计算几何算法库（CGAL），提供计算几何相关的数据结构和算法，诸如三角剖分（2D约束三角剖分及二维和三维Delaunay三角剖分），Voronoi图（二维和三维的点，2D加权Voronoi图，分割Voronoi图等），多边形（布尔操作，偏置），多面体（布尔运算），曲线整理及其应用，网格生成（二维Delaunay网格生成和三维表面和体积网格生成等），几何处理（表面网格简化，细分和参数化等），凸壳算法（2D，3D和dD），搜索结构（近邻搜索，kd树等），插值，形状分析，拟合，距离等。(The goal of the CGAL Open Source Project is to provide easy access to efficient and reliable geometric algorithms in the form of a C++ library. CGAL is used in various areas needing geometric computation, such as: computer graphics, scientific visualization, computer aided design and modeling, geographic information systems, molecular biology, medical imaging, robotics and motion planning, mesh generation, numerical methods... More on the projects using CGAL web page. The Computational Geometry Algorithms Library (CGAL), offers data structures and algorithms like triangulations (2D constrained triangulations, and Delaunay triangulations and periodic triangulations in 2D and 3D), Voronoi diagrams (for 2D and 3D points, 2D additively weighted Voronoi diagrams, and segment Voronoi diagrams), polygons (Boolean operations, offsets, straight skeleton), polyhedra (Boolean operations), arrangements of curves and their applications (2D and 3D envelopes, Minkowski sums), mesh generation (2D Del)

2014-12-11 14:45:21下载

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使用Visual C++编写的生成三次Hermite曲线的源代码。(Bicubic Hermite code in Visual C++.)

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sift角点检测及匹配，在不同尺度空间下的角点检测方法，很方便实用。(sift corner detection and matching, the corner detection methods under different scales of space, it is convenient and practical.)

2013-12-08 17:25:35下载

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：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量机分类器进行训练(plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi)

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