A Markov Chain Monte Carlo Approach to Stereovision
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Finding the Largest Unambiguous Component of Stereo Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
ICDT '03 Proceedings of the 9th International Conference on Database Theory
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Statistical Cue Integration in DAG Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Aggregate operators in probabilistic databases
Journal of the ACM (JACM)
ACM Transactions on Computational Logic (TOCL)
| Hi-index | 0.00 |
We describe an estimation technique which, given a measurement of the depth of a target from a wide-field-of-view (WFOV) stereo camera pair, produces a minimax risk fixed-size confidence interval estimate for the target depth. This work constitutes the first application to the computer vision domain of optimal fixed-size confidence-interval decision theory. The approach is evaluated in terms of theoretical capture probability and empirical capture frequency during actual experiments with a target on an optical bench. The method is compared to several other procedures including the Kalman Filter. The minimax approach is found to dominate all the other methods in performance. In particular, for the minimax approach, a very close agreement is achieved between theoretical capture probability and empirical capture frequency. This allows performance to be accurately predicted, greatly facilitating the system design, and delineating the tasks that may be performed with a given system.