IEEE Transactions on Pattern Analysis and Machine Intelligence
A direct method for locating the focus of expansion
Computer Vision, Graphics, and Image Processing
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Two-dimensional imaging
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Model-Based Brightness Constraints: On Direct Estimation of Structure and Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Principles of computerized tomographic imaging
Principles of computerized tomographic imaging
Scalable Extrinsic Calibration of Omni-Directional Image Networks
International Journal of Computer Vision
3D Model Acquisition from Extended Image Sequences
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
The confounding of translation and rotation in reconstruction from multiple views
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Global rigidity constraints in image displacement fields
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Complete scene structure from four point correspondences
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
A Semi-direct Approach to Structure from Motion
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Robustness to Noise of Stereo Matching
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Fast Registration of Tabular Document Images Using the Fourier-Mellin Transform
DIAL '04 Proceedings of the First International Workshop on Document Image Analysis for Libraries (DIAL'04)
Curvature Scale Space Corner Detector with Adaptive Threshold and Dynamic Region of Support
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Rotation Estimation from Spherical Images
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Radon-Based Structure from Motion without Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Structure from motion for scenes without features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Is Dense Optic Flow Useful to Compute the Fundamental Matrix?
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Epipolar geometry of catadioptric stereo systems with planar mirrors
Image and Vision Computing
The quasi-perspective model: Geometric properties and 3D reconstruction
Pattern Recognition
Simultaneous Camera Pose and Correspondence Estimation with Motion Coherence
International Journal of Computer Vision
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Fundamental matrix estimation is a central problem in computer vision and forms the basis of tasks such as stereo imaging and structure from motion. Existing algorithms typically analyze the relative geometries of matched feature points identified in both projected views. Automated feature matching is itself a challenging problem. Results typically have a large number of false matches. Traditional fundamental matrix estimation methods are very sensitive to matching errors, which led naturally to the application of robust statistical estimation techniques to the problem. In this work, an entirely novel approach is proposed to the fundamental matrix estimation problem. Instead of analyzing the geometry of matched feature points, the problem is recast in the frequency domain through the use of Integral Projection, showing how this is a reasonable model for orthographic cameras. The problem now reduces to one of identifying matching lines in the frequency domain which, most importantly, requires no feature matching or correspondence information. Experimental results on both real and synthetic data are presented that demonstrate the algorithm is a practical technique for fundamental matrix estimation. The behavior of the proposed algorithm is additionally characterized with respect to input noise, feature counts, and other parameters of interest.