Registering a multisensor ensemble of images

Authors:
Jeff Orchard;Richard Mann
Affiliations:
School of Computer Science, University of Waterloo, Waterloo, ON, Canada;School of Computer Science, University of Waterloo, Waterloo, ON, Canada
Venue:
IEEE Transactions on Image Processing
Year:
2010

Citing 7
Cited 1

Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-modal Volume Registration Using Joint Intensity Distributions

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
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
Image matching using alpha-entropy measures and entropic graphs

Signal Processing - Special section on content-based image and video retrieval
An anatomical equivalence class based joint transformation-residual descriptor for morphological analysis

IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Efficient population registration of 3d data

CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications

Information-theoretic multi-modal image registration based on the improved fast Gauss transform: application to brain images

MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis

Quantified Score

Hi-index	0.02

Visualization

Abstract

Many registration scenarios involve aligning more than just two images. These image sets--called ensembles-are conventionally registered by choosing one image as a template, and every other image is registered to it. This pairwise approach is problematic because results depend on which image is chosen as the template. The issue is particularly acute for multisensor ensembles because different sensors create images with different features. Also, pairwise methods use only a fraction of the available data at a time. In this paper, we propose a maximum-likelihood clustering method that registers all the images in a multisensor ensemble simultaneously. Experiments involving rigid-body and affine transformations show that the clustering method is more robust and accurate than competing pairwise registration methods. Moreover, the clustering results can be used to form a rudimentary segmentation of the image ensemble.