A survey of image registration techniques
ACM Computing Surveys (CSUR)
Feature-Based Sequence-to-Sequence Matching
International Journal of Computer Vision
Fusion of color and infrared video for moving human detection
Pattern Recognition
Infrared-visual image registration based on corners and hausdorff distance
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Broadcast Court-Net Sports Video Analysis Using Fast 3-D Camera Modeling
IEEE Transactions on Circuits and Systems for Video Technology
Visible and infrared image registration employing line-based geometric analysis
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Fast saliency-aware multi-modality image fusion
Neurocomputing
Rapid multimodality registration based on MM-SURF
Neurocomputing
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We present a new method to register a pair of images captured in different image modalities. Unlike most of existing systems that register images by aligning single type of visual features, e.g., interest point or contour, we try to align hybrid visual features, including straight lines and interest points. The entire algorithm is carried out in two stages: line-based global transform approximation and point-based local transform adaptation. In the first stage, straight lines derived from edge pixels are employed to find correspondences between two images in order to estimate a global perspective transformation. In the second stage, we divide the entire image into non-overlapping cells with fixed size. The point having the strongest corner response within each cell is selected as the interest point. These points are transformed to other image based on the global transform, and then used to bootstrap a local correspondence search. Experimental evidence shows this method achieves better accuracy for registering visible and long wavelength infrared images/videos as compared to state-of-the-art approaches.