| ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
M. Vinzce and M. Ayromlou and M. Ponweiser and M. Zillich
ABSTRACT
A real-world limitation of visual servoing approaches is the sensitivity of visual tracking to varying ambient conditions and background clutter. This work presents a model-based vision framework to improve the robustness of edge-based feature tracking. Lines and ellipses are tracked using Edge Projected Integration of Cues (EPIC). EPIC uses cues in regions delineated by edges which are de ned by observed edgels and a priori knowledge from a wire-frame model of the object. The edgels are then used for a robust t of the feature geometry, but this sometimes will result in multiple feature candidates. A nal validation step uses the model topology to vote for the most likely feature candidates. EPIC is suited for real-time operation. Experiments demonstrate operation at frame rate. Navigating a walking robot through an industrial environment shows the robustness to varying lighting conditions. Tracking objects over varying background indicates robustness to clutter. |
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| Site generated on Friday, 06 January 2006
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