Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Iconic feature based nonrigid registration: the PASHA algorithm
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Lie algebra approach for tracking and 3D motion estimation using monocular vision
Image and Vision Computing
The Asymmetry of Image Registration and Its Application to Face Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Homography-based 2D Visual Tracking and Servoing
International Journal of Robotics Research
Groupwise Geometric and Photometric Direct Image Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Bi-directional Framework for Unifying Parametric Image Alignment Approaches
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Symmetric Non-rigid Registration: A Geometric Theory and Some Numerical Techniques
Journal of Mathematical Imaging and Vision
Asymmetric gradient-based image alignment
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A dataset and evaluation methodology for template-based tracking algorithms
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
Generalizing Inverse Compositional and ESM Image Alignment
International Journal of Computer Vision
An intensity similarity measure in low-light conditions
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Fast motion estimation using bidirectional gradient methods
IEEE Transactions on Image Processing
Advances in matrix manifolds for computer vision
Image and Vision Computing
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In this paper, a new formulation based on bidirectional composition on Lie groups (BCL) for parametric gradient-based image alignment is presented. Contrary to the conventional approaches, the BCL method takes advantage of the gradients of both template and current image without combining them a priori. Based on this bidirectional formulation, two methods are proposed and their relationship with state-of-the-art gradient based approaches is fully discussed. The first one, i.e., the BCL method, relies on the compositional framework to provide the minimization of the compensated error with respect to an augmented parameter vector. The second one, the projected BCL (PBCL), corresponds to a close approximation of the BCL approach. A comparative study is carried out dealing with computational complexity, convergence rate and frequence of convergence. Numerical experiments using a conventional benchmark show the performance improvement especially for asymmetric levels of noise, which is also discussed from a theoretical point of view.