A survey of image registration techniques
ACM Computing Surveys (CSUR)
Automatic Extraction of Man-Made Objects from Aerial and Space Images (II)
Automatic Extraction of Man-Made Objects from Aerial and Space Images (II)
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Model-Based Target Recognition in Pulsed Ladar Imagery
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Automatic EO/IR sensor image registration
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Registration of Cad-Models to Images by Iterative Inverse Perspective Matching
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Precise matching of 3-D target models to multisensor data
IEEE Transactions on Image Processing
Automatic target recognition organized via jump-diffusion algorithms
IEEE Transactions on Image Processing
Model-based target recognition in pulsed ladar imagery
IEEE Transactions on Image Processing
A contour-based approach to multisensor image registration
IEEE Transactions on Image Processing
Visible and infrared image registration using trajectories and composite foreground images
Image and Vision Computing
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Given two images of roughly the same scene, image registration is the process of determining the transformation that nearly maps one image to another. While some efficient procedures have been developed lately, the registration of images acquired from sensors operating in different modalities is still a challenging problem. In general, such images have different gray level characteristics and the features in the two images to be registered are often not well preserved, rendering registration techniques such as those based on feature extraction and area correlation generally not efficient, and hence not feasible in all cases. In this paper, we propose a new algorithm for multi-sensor image registration based on using the local frequency representation of the images together with image representation of computer-aided design (CAD) models that permit region-of-interest-to-region-of-interest, ROI-to-ROI, space to solve image registration problem (instead of relying only on the captured images to solve image registration problem using image-to-image space). The key point underlying the proposed approach is the employment of local frequency representations of CAD models images to efficiently determine sets of matching points from the images to be registered, which in turn enables obtaining correspondence between these sets for estimating the transformation parameters. Performance evaluation results reported here indicate that the proposed technique is robust and yields promising results for multi-modal image registration.