Conditional variance of differences: a robust similarity measure for matching and registration

Authors:
Atsuto Maki;Riccardo Gherardi
Affiliations:
Cambridge Research Laboratory, Toshiba Research Europe, Cambridge, United Kingdom;Cambridge Research Laboratory, Toshiba Research Europe, Cambridge, United Kingdom
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

Citing 5
Cited 0

Alignment by Maximization of Mutual Information

International Journal of Computer Vision
The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Robust Multi-Sensor Image Alignment

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Lucas-Kanade 20 Years On: A Unifying Framework

International Journal of Computer Vision
Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new similarity measure, the sum of conditional variance of differences (SCVD), designed to be insensitive to highly non-linear intensity transformations such as the ones occurring in multi-modal image registration and tracking. It improves on another recently introduced statistical measure, the sum of conditional variances (SCV), which has been reported to outperform comparable information theoretic similarity measures such as mutual information (MI) and cross-cumulative residual entropy (CCRE). We also propose two additional extensions that further increase the robustness of SCV(D) by relaxing the quantisation process and making it symmetric. We demonstrate the benefits of SCVD and improvements on image matching and registration through experiments.