[IPOL announce] new article: Automatic RANSAC by Likelihood Maximization

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2022/357/

Clément Riu, Vincent Nozick, and Pascal Monasse,
Automatic RANSAC by Likelihood Maximization,
Image Processing On Line, 12 (2022), pp. 27–49.
https://doi.org/10.5201/ipol.2022.357

Abstract
In computer vision, and particularly in 3D reconstruction from images, 
it is customary to be faced with regression problems contaminated by 
outlying data. The standard and efficient method to deal with them is 
the Random Sample Consensus (RANSAC) algorithm. The procedure is simple 
and versatile, drawing random minimal samples from the data to estimate 
parameterized candidate models and ranking them based on the amount of 
compatible data. Such evaluation involves some threshold that separates 
inliers from outliers. In presence of unknown level of noise, as is 
usual in practice, it is desirable to remove the dependency on this 
fixed threshold and to estimate it as an additional unknown. Among the 
numerous variants of RANSAC, few, that we call 'automatic', propose this 
approach, which involves changing the maximization criterion of 
consensus, as it is naturally increasing with the varying threshold. An 
algorithm of Zach and Cohen (ICCV 2015) uses the likelihood statistics. 
We present the details and the implementation of their method along with 
quantitative and qualitative tests on standard stereovision tasks: 
estimation of homography, fundamental and essential matrix.