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1·A new linear iterative algorithm for estimating the fundamental matrix is presented.

提出了一种新的估计基本矩阵的线性迭代算法。

2·By using the characteristic vector method, the fundamental matrix solution is obtained.

采用特征向量法求其基本矩阵解。

3·It consist of computation of fundamental matrix in two views and of trifocal tensor in three views.

主要包括两视点结构中基本矩阵及三视点结构中三焦张量的计算。

4·On the basis of the error model, the paper also analyzes the application of data normalization algorithm to estimating fundamental matrix.

针对所建立的误差模型，分析数据规范化算法在基础矩阵估计中的应用。

5·To avoid sitting reference object on scene, the model of self-calibration based on fundamental matrix estimation and its implementation are proposed.

为避免测量过程中在现场设置标定参考点，研究了以基础矩阵估计为基础的交通事故摄影图像自标定模型，并分析了其实现过程。

6·The invariants of double algebra's form has simple and explicit expression which can be computed directly by coordinates of image points and fundamental matrix.

用双代数形式表示的不变量具有简洁、具体的表达式，可以由图像点坐标和基础矩阵直接求出。

7·According to the planar constraints, we proved that the product of the fundamental matrix and the homography matrix of an image pair is an antisymmetric matrix.

根据两幅图像中的平面约束，证明了图像对的基础矩阵和同形矩阵的乘积具有反对称的性质。

8·The corner detection algorithm, avoidance of the degeneration problem and the fundamental matrix iterative optimization algorithm based on rank-2 constraint were introduced.

给出了角点检测算法、退化问题的避免措施以及基于秩2约束的基础矩阵迭代优化求解的方法。

9·This paper analyzes the statistical characteristics of camera imaging error based on studying total least squares, and clarifies the error effect on estimating fundamental matrix.

在研究整体最小二乘法的基础上，分析摄像机成像误差的统计特征，明确误差对基础矩阵估计算法的影响。

10·Experiments on simulated and real image data are conducted. The results show that this algorithm is very robust to noises and outliers, and the fundamental matrix with high accuracy can be found.

大量的模拟数据和真实图像的实验结果表明，此算法不仅具有良好的鲁棒性，而且可提高基础矩阵的估计精度。