DocumentCode :
3050445
Title :
Combining information using hard constraints
Author :
DeCarlo, Douglas ; Metaxas, Dimitris
Author_Institution :
Dept. of Comput. Sci., Rutgers Univ., Piscataway, NJ, USA
Volume :
2
fYear :
1999
fDate :
1999
Abstract :
In this paper we show how the use of hard constraints in solving estimation problems, by allowing multiple sources of information to be taken into account during optimization, increases robustness and improves efficiency over alternative methods such as the statistical combination of separate optimization results. Our argument is based on an empirical evaluation of the technique which uses a model-based optical flow constraint in a deformable model framework for tracking a face. The flow constraint makes the model-to-edge alignment optimization problem easier by projecting away the portion of the search space that optical flow makes unlikely, while a Kalman filter is used to reconcile hard constraints with the uncertainty in the optical flow data. Using these hard constraints, the system converges more quickly at each iteration and avoids local minima in solutions that cause other methods to lose track. We conjecture that this use of constraints will be effective in any integration application where there are disparities in the difficulty of computational problems associated with the we of different information sources
Keywords :
Kalman filters; image sequences; optimisation; Kalman filter; estimation problems; hard constraints; local minima; model-based optical flow constraint; model-to-edge alignment optimization problem; multiple sources; optimization; Computer science; Constraint optimization; Covariance matrix; Deformable models; Equations; Information resources; Optical sensors; Optimization methods; Robustness; Symmetric matrices;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Conference_Location :
Fort Collins, CO
ISSN :
1063-6919
Print_ISBN :
0-7695-0149-4
Type :
conf
DOI :
10.1109/CVPR.1999.784620
Filename :
784620
Link To Document :
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