Abstract
This work presents a novel approach for detecting inliers in a given set of correspondences (matches). It does so without explicitly identifying any consensus set, based on a method for inlier rate estimation (IRE). Given such an estimator for the inlier rate, we also present an algorithm that detects a globally optimal transformation. We provide a theoretical analysis of the IRE method using a stochastic generative model on the continuous spaces of matches and transformations. This model allows rigorous investigation of the limits of our IRE method for the case of 2D-translation, further giving bounds and insights for the more general case. Our theoretical analysis is validated empirically and is shown to hold in practice for the more general case of 2D-affinities. In addition, we show that the combined framework works on challenging cases of 2D-homography estimation, with very few and possibly noisy inliers, where RANSAC generally fails.
Original language | English |
---|---|
Title of host publication | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 |
Publisher | IEEE Computer Society |
Pages | 5243-5251 |
Number of pages | 9 |
ISBN (Electronic) | 9781467369640 |
DOIs | |
State | Published - 14 Oct 2015 |
Externally published | Yes |
Event | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States Duration: 7 Jun 2015 → 12 Jun 2015 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
---|---|
Volume | 07-12-June-2015 |
ISSN (Print) | 1063-6919 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 |
---|---|
Country/Territory | United States |
City | Boston |
Period | 7/06/15 → 12/06/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition