Piecewise cubic mapping functions for image registration
Pattern Recognition
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Description of Local Singularities for Image Registration
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery
IEEE Transactions on Image Processing
A comparative study of transformation functions for nonrigid image registration
IEEE Transactions on Image Processing
Local Image Registration by Adaptive Filtering
IEEE Transactions on Image Processing
A highly repeatable feature detector: improved Harris---Laplace
Multimedia Tools and Applications
A hybrid approach based on MEP and CSP for contour registration
Applied Soft Computing
Computers and Electrical Engineering
Automated HRSI georegistration using orthoimage and SRTM: Focusing KOMPSAT-2 imagery
Computers & Geosciences
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Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.