A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Incorporating Connected Region Labelling into Automatic Image Registration Using Mutual Information
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Robust Multi-Sensor Image Alignment
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Multisensor Image Registration via Implicit Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-modality Image Registration Using Mutual Information Based on Gradient Vector Flow
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
A robust hybrid method for nonrigid image registration
Pattern Recognition
Images registration based on mutual information and nonsubsampled contourlet transform
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Multimodal monitoring of cultural heritage sites and the FIRESENSE project
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Visible and infrared image registration in man-made environments employing hybrid visual features
Pattern Recognition Letters
Visible and infrared image registration employing line-based geometric analysis
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Rapid multimodality registration based on MM-SURF
Neurocomputing
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This paper presents a method for registering multi-sensor images such as visible and infrared images. Registration is achieved by iteratively minimizing an objective function through updating transformation parameters of deformation. A new entropy-based objective function is proposed on the basis of a 3-D joint histogram incorporating intensity information and edge orientation information. To complement the entropy-based objective function, a weighting function is also introduced through the use of a coincidence measure of edge orientations. Experimental results show that the proposed method provides more robust registration results than the existing approaches.