A Lie group formulation of robot dynamics
International Journal of Robotics Research
Fixation simplifies 3D motion estimation
Computer Vision and Image Understanding
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
International Journal of Computer Vision
Application of Lie Algebras to Visual Servoing
International Journal of Computer Vision - Special issue on image-based servoing
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure from Motion Causally Integrated Over Time
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hyperplane Approximation for Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making Good Features Track Better
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Image-based robot task planning and control using a compact visual representation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Video orbits of the projective group a simple approach to featureless estimation of parameters
IEEE Transactions on Image Processing
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This paper addresses the parameters' estimation of 2D and 3D transformations. For the estimation we present a method based on system identification theory, we named it the "A-method". The transformations are considered as elements of the Lie group GL(n) or one of its subgroups. We represent the transformations in terms of their Lie Algebra elements. The Lie algebra approach assures to follow the shortest path or geodesic in the involved Lie group. To prove the potencial of our method, two experiments are presented. The first one is a monocular estimation of 3D rigid motion of an object in the visual space. With this aim, the six parameters of the rigid motion are estimated based on measurements of the six parameters of the affine transformation in the image. Secondly, we present the estimation of the affine or projective transformations involved in monocular region tracking.