A note on the orthonormal discriminant vector method for feature extraction
Pattern Recognition
Visual Servoing Using Eigenspace Method and Dynamic Calculation of Interaction Matrices
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Photometry-based visual servoing using light reflexion models
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Colorimetry-based visual servoing
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Dynamic 6DOF metrology for evaluating a visual servoing system
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Mathematical metrology for evaluating a 6DOF visual servoing system
PerMIS '09 Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems
3D motion segmentation using intensity trajectory
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Photometric visual servoing for omnidirectional cameras
Autonomous Robots
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A general scheme to represent the relation between dynamic images and camera and/or object motions is proposed for applications to visual control of robots. We consider the case where a moving camera observes moving objects in a static scene. The camera obtains images of the objects moving within the scene. Then, the possible combinations of the camera and the objects' poses and the obtained images are not arbitrary but constrained to each other. Here we represent this constraint as a lower dimensional hypersurface in the product space of the whole combination of their motion control parameters and image data. The visual control is interpreted as to find a path on this surface leading to their poses where a given goal image will be obtained. In this paper, we propose a visual control method to utilize tangential properties of this surface. First, we represent images with a composition of a small number of “eigen images” by using K-L (Karhunen-Loève) expansion. Then, we consider to reconstruct the eigen space (the eigen image space) to achieve efficient and straightforward controls. Such reconstruction of the space results in the constraint surface being mostly flat within the eigen space. By this method, visual control of robots in a complex configuration is achieved without image processing to extract and correspond image features in dynamic images. The method also does not need camera or hand-eye calibrations. Experimental results of visual servoing with the proposed method show the feasibility and applicability of our newly proposed approach to a simultaneous control of camera self-motion and object motions.