Global parametric image alignment via high-order approximation
Computer Vision and Image Understanding
Incorporating symmetry into the Lucas-Kanade framework
Pattern Recognition Letters
Generalized least squares-based parametric motion estimation
Computer Vision and Image Understanding
Generalizing Inverse Compositional and ESM Image Alignment
International Journal of Computer Vision
Panoramic video coding using affine motion compensated prediction
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Bidirectional composition on Lie groups for gradient-based image alignment
IEEE Transactions on Image Processing
Automatic estimation of asymmetry for gradient-based alignment of noisy images on Lie group
Pattern Recognition Letters
| Hi-index | 0.01 |
Gradient-based motion estimation methods (GMs) are considered to be in the heart of state-of-the-art registration algorithms, being able to account for both pixel and subpixel registration and to handle various motion models (translation, rotation, affine, and projective). These methods estimate the motion between two images based on the local changes in the image intensities while assuming image smoothness. This paper offers two main contributions. The first is enhancement of the GM technique by introducing two new bidirectional formulations of the GM. These improve the convergence properties for large motions. The second is that we present an analytical convergence analysis of the GM and its properties. Experimental results demonstrate the applicability of these algorithms to real images.