Combining information using hard constraints

Douglas DeCarlo, Dimitri Metaxas

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations

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 use of different information sources.

Original languageEnglish (US)
Pages (from-to)132-138
Number of pages7
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2
StatePublished - Jan 1 1999
EventProceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Fort Collins, CO, USA
Duration: Jun 23 1999Jun 25 1999

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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