Robust regression and outlier detection
Robust regression and outlier detection
It can be done without camera calibration
Pattern Recognition Letters
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Artificial Intelligence - Special volume on computer vision
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
On accurate and robust estimation of fundamental matrix
Computer Vision and Image Understanding
Estimating the fundamental matrix by transforming image points in projective space
Computer Vision and Image Understanding
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera Self-Calibration: Theory and Experiments
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Fast Computation of the Fundamental Matrix for an Active Stereo Vision System
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Direct Methods for Self-Calibration of a Moving Stereo Head
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Euclidean Reconstruction from Uncalibrated Views
Proceedings of the Second Joint European - US Workshop on Applications of Invariance in Computer Vision
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Robust detection of degenerate configurations for the fundamental matrix
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
On the probabilistic epipolar geometry
Image and Vision Computing
A homography transform based higher-order MRF model for stereo matching
Pattern Recognition Letters
Hi-index | 0.01 |
The epipolar geometry is the intrinsic projective geometry between two views, and the algebraic representation of it is the fundamental matrix. Estimating the fundamental matrix requires solving an over-determined equation. Many classical approaches assume that the error values of the over-determined equation obey a Gaussian distribution. However, the performances of these approaches may decrease significantly when the noise is large and heterogeneous. This paper proposes a novel technique for estimating the fundamental matrix based on least absolute deviation (LAD), which is also known as the L"1 method. Then a linear iterative algorithm is presented. The experimental results on some indoor and outdoor scenes show that the proposed algorithm yields the accurate and robust estimates of the fundamental matrix when the noise is non-Gaussian.