Digital video processing
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
2D-Object Tracking Based on Projection-Histograms
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Performance Evaluation of Graphics Recognition Algorithms: Principles and Applications
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Conditional-mean estimation via jump-diffusion processes inmultiple target tracking/recognition
IEEE Transactions on Signal Processing
Iterative maximum likelihood displacement field estimation in quantum-limited image sequences
IEEE Transactions on Image Processing
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Feature tracking methods based on Bayesian estimation are widely studied in computer vision systems. The performance of Bayesian decision, however, remains an open problem because an implementation of Bayesian estimation is significantly affected by many parameters in modeling the prior and observation probabilities. In this paper, we evaluate the performance of our MAP based feature tracking algorithm with various parameter settings for many features. For most 2D feature points in our experiments, we found that the uniform distribution model (or Gaussian model with a very large variance) with linear prediction yields the best feature tracking performance.