International Journal of Computer Vision
Group Actions, Homeomorphisms, and Matching: A General Framework
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
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
Homography-based 2D Visual Tracking and Servoing
International Journal of Robotics Research
Optimization on Lie Manifolds and Projective Tracking
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
The Bi-directional Framework for Unifying Parametric Image Alignment Approaches
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
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
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
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Many parametric image alignment approaches assume equality of the images to register up to motion compensation. In presence of noise this assumption does not hold. In particular, for gradient-based approaches, which rely on the optimization of an error functional with gradient descent methods, the performances depend on the amount of noise in each image. We propose in this paper to use the Asymmetric Composition on Lie groups (ACL) formulation of the alignment problem to improve the robustness in presence of asymmetric levels of noise. The ACL formulation, generalizing state-of-the-art gradient-based image alignment, introduces a parameter to weight the influence of the images during the optimization. Three new methods are presented to estimate this asymmetry parameter: one supervised (MVACL) and two fully automatic (AACL and GACL). Theoretical results and experimental validation show how the new algorithms improve robustness in presence of noise. Finally, we illustrate the interest of the new approaches for object tracking under low-light conditions.