Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Registration of Translated and Rotated Images Using Finite Fourier Transforms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Integrating Faces and Fingerprints for Personal Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alignment Using Distributions of Local Geometric Properties
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Parameter Estimation in Computer Vision
SIAM Review
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
Efficient global weighted least-squares translation registration in the frequency domain
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
An FFT-based technique for translation, rotation, and scale-invariant image registration
IEEE Transactions on Image Processing
Fast parametric elastic image registration
IEEE Transactions on Image Processing
A maximum likelihood approach for image registration using control point and intensity
IEEE Transactions on Image Processing
Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A contour-based approach to multisensor image registration
IEEE Transactions on Image Processing
An adaptive search length algorithm for block matching motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Adjustable partial distortion search algorithm for fast block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Rigid registration of renal perfusion images using a neurobiology-based visual saliency model
Journal on Image and Video Processing
Using the local phase of the magnitude of the local structure tensor for image registration
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
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An interesting problem in pattern recognition is that of image registration, which plays an important role in many vision-based recognition and motion analysis applications. Of particular interest among registration problems are multimodal registration problems, where the images exist in different feature spaces. State-of-the-art phased-based approaches to multimodal image registration methods have provided good accuracy but have high computational cost. This paper presents a fast phase-based approach to registering multimodal images for the purpose of initial coarse-grained registration. This is accomplished by simultaneously performing both globally exhaustive dynamic phase sub-cloud matching and polynomial feature space transformation estimation in the frequency domain using the fast Fourier transform (FFT). A multiscale phase-based feature extraction method is proposed that determines both the location and size of the dynamic sub-clouds being extracted. A simple outlier pruning based on resampling is used to remove false keypoint matches. The proposed phase-based approach to registration can be performed very efficiently without the need for initial estimates or equivalent keypoints from both images. Experimental results show that the proposed method can provide accuracies comparable to the state-of-the-art phase-based image registration methods for the purpose of initial coarse-grained registration while being much faster to compute.