Fast outlier rejection by using parallax-based rigidity constraint for epipolar geometry estimation

Authors:
Engin Tola;A. Aydın Alatan
Affiliations:
Computer Vision Laboratory, Ecóle Polytechnique Fédéral de Lausanne (EPFL), Lausanne, Switzerland;Dept. of Electrical & Electronics Eng, Middle East Technical University (METU), Ankara, Turkey
Venue:
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Year:
2006

Citing 7
Cited 0

Digital video processing

Digital video processing
In Defense of the Eight-Point Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Approach to Moving Object Detection in 2D and 3D Scenes

IEEE Transactions on Pattern Analysis and Machine Intelligence
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
ROR: Rejection of Outliers by Rotations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision
Guided Sampling via Weak Motion Models and Outlier Sample Generation for Epipolar Geometry Estimation

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel approach is presented in order to reject correspondence outliers between frames using the parallax-based rigidity constraint for epipolar geometry estimation. In this approach, the invariance of 3-D relative projective structure of a stationary scene over different views is exploited to eliminate outliers, mostly due to independently moving objects of a typical scene. The proposed approach is compared against a well-known RANSAC-based algorithm by the help of a test-bed. The results showed that the speed-up, gained by utilization of the proposed technique as a preprocessing step before RANSAC-based approach, decreases the execution time of the overall outlier rejection, significantly.