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
Image Registration by Maximization of Combined Mututal Information and Gradient Information
MICCAI '00 Proceedings of the Third 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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multisensor Image Registration via Implicit Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Description of interest regions with local binary patterns
Pattern Recognition
Infrared-visual image registration based on corners and hausdorff distance
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Visible and infrared image registration using trajectories and composite foreground images
Image and Vision Computing
Multi-sensor registration for objects motion detection
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Efficient Least Squares Multimodal Registration With a Globally Exhaustive Alignment Search
IEEE Transactions on Image Processing
A contour-based approach to multisensor image registration
IEEE Transactions on Image Processing
Visible and infrared image registration in man-made environments employing hybrid visual features
Pattern Recognition Letters
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With a large number of registration algorithms proposed, image registration techniques have achieved rapid development. However, there still exist many deficiencies in multimodality registration where high speed and accuracy are difficult to simultaneously achieve for real-time processing. In order to solve these problems we propose a novel method named MM-SURF (Multimodal-SURF). Inheriting the advantages of the SURF, the method is able to generate a large number of robust keypoints. For each keypoint, the neighborhood gradient magnitude is utilized to compute its dominant orientation. Relying on the dominant orientation, a MM-SURF descriptor is constructed as the local features description of the keypoint. The geometric transformation matrix for multimodal image registration is obtained by matching the keypoints. The method makes full use of gray information of multimodal images and simultaneously inherits the good performance of the SURF. Experimental results indicate that the proposed method achieves higher accuracy and consumes less runtime than the other similar algorithms for multimodal image registrations, and also demonstrate its robustness and stability in the presence of image blurring, rotation, noise and luminance variations.