A survey of image registration techniques
ACM Computing Surveys (CSUR)
Interpolation artefacts in mutual information-based image registration
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Medical Image Registration Using Geometric Hashing
IEEE Computational Science & Engineering
Regularized Laplacian Zero Crossings as Optimal Edge Integrators
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
Towards a Better Comprehension of Similarity Measures Used in Medical Image Registration
MICCAI '99 Proceedings of the Second 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
A generalized divergence measure for robust image registration
IEEE Transactions on Signal Processing
Fast parametric elastic image registration
IEEE Transactions on Image Processing
A contour-based approach to multisensor image registration
IEEE Transactions on Image Processing
Fast and accurate global motion estimation algorithm using pixel subsampling
Information Sciences: an International Journal
A multi-resolution area-based technique for automatic multi-modal image registration
Image and Vision Computing
Automated approaches for analysis of multimodal MRI acquisitions in a study of cognitive aging
Computer Methods and Programs in Biomedicine
Optimized hierarchical block matching for fast and accurate image registration
Image Communication
Rapid multimodality registration based on MM-SURF
Neurocomputing
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This paper presents an approach to the registration of significantly dissimilar images, acquired by sensors of different modalities. A robust matching criterion is derived by aligning the locations of gradient maxima. The alignment is achieved by iteratively maximizing the magnitudes of the intensity gradients of a set of pixels in one of the images, where the set is initialized by the gradient maxima locations of the second image. No explicit similarity measure that uses the intensities of both images is used. The computation utilizes the full spatial information of the first image and the accuracy and robustness of the registration depend only on it. False matchings are detected and adaptively weighted using a directional similarity measure. By embedding the scheme in a "coarse to fine” formulation, we were able to estimate affine and projective global motions, even when the images were characterized by complex space varying intensity transformations. The scheme is especially suitable when one of the images is of considerably better quality than the other (noise, blur, etc.). We demonstrate these properties via experiments on real multisensor image sets.