Randomized RANSAC with Sequential Probability Ratio Test

Authors:
Jiri Matas;Ondrej Chum
Affiliations:
CTU Prague;CTU Prague
Venue:
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Year:
2005

Citing 0
Cited 12

A consensus sampling technique for fast and robust model fitting

Pattern Recognition
A generative framework for fast urban labeling using spatial and temporal context

Autonomous Robots
Adaptive Sample Consensus for Efficient Random Optimization

ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
An adaptive-scale robust estimator for motion estimation

ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Real-time large scale near-duplicate web video retrieval

Proceedings of the international conference on Multimedia
A minimal case solution to the calibrated relative pose problem for the case of two known orientation angles

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Accelerated hypothesis generation for multi-structure robust fitting

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Accelerating FAB-MAP with concentration inequalities

IEEE Transactions on Robotics
An M-estimator for high breakdown robust estimation in computer vision

Computer Vision and Image Understanding
Self-calibration of wireless cameras with restricted degrees of freedom

Computer Vision and Image Understanding
Image matching by fast random sample consensus

Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
HEASK: Robust homography estimation based on appearance similarity and keypoint correspondences

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-controllable probability n. A provably optimal model verification strategy is designed for the situation when the contamination of data by outliers is known, i.e. the algorithm is the fastest possible (on average) of all randomized RANSAC algorithms guaranteeing 1 - n confidence in the solution. The derivation of the optimality property is based on Wald驴s theory of sequential decision making. The R-RANSAC with SPRT, which does not require the a priori knowledge of the fraction of outliers and has results close to the optimal strategy, is introduced. We show experimentally that on standard test data the method is 2 to 10 times faster than the standard RANSAC and up to 4 times faster than previously published methods.