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一些图像处理常用的函数，包括图像之间的点匹配、鲁棒性估计、图像旋转、基本矩阵的求解、单应矩阵求解等，可用于摄像机标定-Commonly used in a number of image processing functions, including point match between the image and robustness of the estimates, image rotation, solve the fundamental matrix, homography solv
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计算基本矩阵 很有用也很齐全，是源代码m文件，希望对大家有用-Toolbox of computation Fundamental matrix
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用于立体图像矫正:Harries角点、NCC匹配、RANSAC计算基本矩阵完成立体图像对的极线校正，自己书写的opencv函数-For three-dimensional image correction: Harries corner NCC matching, RANSAC calculation of the completion of the fundamental matrix the epipolar rectification of the stereo image pairs o
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立体视觉方向 透视投影矩阵 基础矩阵等地求解函数-The stereovision direction perspective projection matrix fundamental matrix to solve the function
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用于双目立体图像匹配：用surf提取特征点、Flann匹配、RANSAC计算基本矩阵完成立体图像对的极线校正，用opencv实现-For binocular stereo image matching feature extraction point: surf, Flann matching, RANSAC calculation of the completion of the fundamental matrix the epipolar rectification of the ster
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雙camera，取出左右camera的image，做flann ransac matching（基予sift）同時計算出fundamental matrix和深度關係。另外做了光流法h檔 加載後 可以計算左右camera 運動位移。
如果不能解壓縮，請將檔案後綴改成7z。-3D geometry
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RANSAC为RANdom SAmple Consensus的缩写，它是根据一组包含异常数据的样本数据集，计算出数据的数学模型参数，得到有效样本数据的算法。它于1981年由Fischler和Bolles最先提出[1]。
RANSAC算法经常用于计算机视觉中。例如，在立体视觉领域中同时解决一对相机的匹配点问题及基本矩阵的计算。
RANSAC算法的基本假设是样本中包含正确数据(inliers，可以被模型描述的数据)，也包含异常数据(Outliers，偏离正常范围很远、无法适应数学模型的数据)
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对两幅图像提取特征点，并计算基本矩阵，对两幅图像进行极线矫正，并画出校正后图像特征点的极线，效果非常好-The two images of the image feature points in the feature points are extracted, and the calculated fundamental matrix, correction of lines of the two images, and after the draw correction lines, ver
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该程序利用MATLAB语言计算本质矩阵和基本矩阵，其中有用到ransac八点算法-The program utilizes the MATLAB language, the nature of the matrix and the fundamental matrix, which is useful to ransac eight algorithms
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该程序应用八点算法计算视觉几何中的基本矩阵，并在MATLAB的环境中运行-The program application eight algorithm to calculate the fundamental matrix of the visual geometry, and run in the MATLAB environment
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立体视觉相关的程序，这部分需要先了解图像和摄像机之间的对立关系，并对极线几何比较了解，先把原理熟悉了以后，具体到编程上就会轻松一点，整体还是比较耗时的。具体来说，程序使用SSD得到匹配点，然后根据匹配点计算出基本矩阵，最后计算出匹配点相关的极线-Stereo vision procedure, this part of the need to understand the antagonistic relationship between the image and the camera, an
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一种求解基础矩阵的方法，matlab实现-Solving the fundamental matrix method, matlab achieve
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LIBVISO2: C++ Library for Visual Odometry 2LIBVISO2 (Library for Visual Odometry 2) is a very fast cross-platfrom (Linux, Windows) C++ library with MATLAB wrappers for computing the 6 DOF motion of a moving mono/stereo camera. The stereo version is b
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对两幅图像进行配准，分别提取两幅图像的surf特征点以及描述子，得到粗匹配结果，然后根据粗匹配结果，采用ransac方法计算基础矩阵，并去除误匹配点，得到较准确匹配结果-Two image registration, surf was extracted from the feature points in two images to get the coarse matching and descr iptor, then according to the results, the coars
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本部分代码只是提供了基础矩阵及摄像机焦距部分的代码。其中基础矩阵的求解用到的数据，是图像匹配得到的点对-This section provides the basis matrix code only part of the code and the focal length of the camera. Which is used to solve the fundamental matrix of the data is obtained by image matching points
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Fundamental Matrix Fundamental Matrix-Fundamental Matrix Fundamental Matrix Fundamental Matrix
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利用opencv进行图像匹配，采用SURF算法。另求出基本矩阵 并验证准确性-using SURF method to match the image
with the help of opencv and get the fundamental matrix also
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用OPENCV编写的8点算法，求基础矩阵，计算机视觉处理，写的非常不错，可以-OPENCV with 8:00 algorithm written, seeking fundamental matrix, computer vision processing, write very good, you can take a look at
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基于投影M估计量的稳健回归方法，用于估计计算机视觉立体像对间的Fundamental Matrix。Fundamental Matrix，参考文献：H. Chen, P. Meer, Robust regression with projection based M-estimators. 9th International Conference on Computer Vision (ICCV), Nice, France, October 2003, 878-885.PS：这是作者11年前本
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这是一段改进的八点算法，用来求基本矩阵的，并且用matlab进行了实现-This is a modified eight algorithms used to find the fundamental matrix, and was realized with matlab
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