Description: 计算机视觉中得八点算法，采用SVD分解最小二乘解来求基本矩阵，这是模拟仿真实验程序。-Was 8:00 in computer vision algorithms, using SVD decomposition least-squares solution to seek the fundamental matrix, which is simulation, experimental procedure. Platform: |
Size: 2048 |
Author:亮月新 |
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Description: 矩阵的特征值与特征向量
（1）理解矩阵的特征值、特征向量的基本概念；了解矩阵特征多项式概念；
（2）熟练掌握求矩阵的特征值、特征向量的方法；
（3）掌握矩阵的特征值和特征向量的基本性质；
（4）了解求矩阵特征值和特征向量的MATLAB 和MAPLE 命令-Matrix Eigenvalues and eigenvectors (1) understand the matrix eigenvalues, eigenvectors of the basic concepts understanding of the concept of matrix characteristic polynomial (2) proficiency in order to matrix eigenvalue, eigenvector method (3) control Matrix eigenvalues and eigenvectors of the fundamental nature (4) Understanding the demand matrix, eigenvalues and eigenvectors of MATLAB and MAPLE commands Platform: |
Size: 148480 |
Author:alice |
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Description: 利用RANSAC算法鲁棒性的计算两幅视图间的基本矩阵，本代码含有7点算法，8点算法-RANSAC algorithm using the calculation of robust fundamental matrix between two views, this code contains the algorithm 7, 8-point algorithm Platform: |
Size: 933888 |
Author:苇子根 |
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Description: 利用Matlab和SPSS软件实现聚类分析.运用Matlab中的一些基本矩阵计算方法，通过自己编程实现聚类算法-Matlab and SPSS software using cluster analysis. The use of Matlab, some of the fundamental matrix method, clustering algorithms, through their own programming Platform: |
Size: 70656 |
Author:WANGFANG |
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Description: 计算机视觉作业，实现两幅图片中的极线、极点计算和基础矩阵的求取。-Computer vision homework.It is to calculate the polar, pole and the fundamental matrix calculation between two image. Platform: |
Size: 24307712 |
Author:董亚锋 |
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Description: 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 Platform: |
Size: 1024 |
Author:艾俊生 |
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Description: 运用Matlab中的一些基本矩阵计算方法，通过自己编程实现聚类算法，在此只讨论根据最短距离规则聚类的方法。-The use of Matlab, some of the fundamental matrix method, clustering algorithms, through their own programming, in which only discuss the rules under the shortest distance clustering method. Platform: |
Size: 83968 |
Author: |
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Description: sift特征点的提取匹配后使用Ransac进行基本矩阵的估计-sift the extraction of feature points were matched using the fundamental matrix estimation Ransac Platform: |
Size: 577536 |
Author:尚超 |
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Description: 立体匹配，包括计算基本矩阵，极线校正，和SSD计算匹配点-Matching, including the calculation of fundamental matrix, epipolar correction, and SSD calculation of matching points Platform: |
Size: 1272832 |
Author:gsd |
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Description: 基于RANSAC算法的基础矩阵求解相机的参数-The fundamental matrix based on RANSAC algorithm to solve the camera parameters Platform: |
Size: 529408 |
Author:王雨谒 |
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Description: Projection Based M-Estimator ,一个基于M
-Estimator估计器的投影程序，能够很好的估计，计算机图像领域的线性、异方差（椭圆和 基础矩阵）和子空间等。- using the base class for linear, heteroscedastic (ellipse and fundamental matrix) and subspace estimation are included in the program. Platform: |
Size: 4879360 |
Author:top |
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Description: 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 solving Platform: |
Size: 6125568 |
Author:vivian |
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Description: 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 Platform: |
Size: 19456 |
Author:股霏霏 |
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Description: 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 stereo image pairs with opencv Platform: |
Size: 3072 |
Author:guonan |
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Description: 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 own writing opencv function Platform: |
Size: 12288 |
Author:guonan |
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Description: 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)，即数据集中含有噪声。这些异常数据可能是由于错误的测量、错误的假设、错误的计算等产生的。同时RANSAC也假设，给定一组正确的数据，存在可以计算出符合这些数据的模型参数的方法。-RANSAC for RANdom SAmple Consensus, it is based on a set of sample data sets contain abnormal data, mathematical model parameters calculated data, the effective sample data algorithm. First proposed by Fischler and Bolles in 1981 [1]. The RANSAC algorithm often used in computer vision. For example, while addressing the three-dimensional visual field on the camera match point problem and the fundamental matrix calculation. The RANSAC algorithm' s basic assumption is that sample contains the correct data (inliers, can be described by the model data) also contain abnormal data (Outliers, deviated from the normal range very far, unable to adapt to the mathematical model of the data), that the data set contains noise. These abnormal data may be generated due to wrong measurements, wrong assumptions, wrong calculation. Simultaneously RANSAC also assume, given a set of correct data, there can be calculated out of these data to the model parameters. Platform: |
Size: 6607872 |
Author:周炜 |
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Search - Fundamental matrix