Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier Independent Viewpoint Selection for 3-D Object Recognition
Mustererkennung 2000, 22. DAGM-Symposium
Learning Temporal Context in Active Object Recognition Using Bayesian Analysis
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Transinformation for Active Object Recognition
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
On Optimal Camera Parameter Selection in Kalman Filter Based Object Tracking
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Online control of active camera networks for computer vision tasks
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 0.00 |
In this paper we introduce a formalism for optimal camera parameter selection for iterative state estimation. We consider a framework based on Shannon's information theory and select the camera parameters that maximize the mutual information, i.e. the information that the captured image conveys about the true state of the system. The technique explicitly takes into account the a priori probability governing the computation of the mutual information. Thus, a sequential decision process can be formed by treating the a posteriori probability at the current time step in the decision process as the a priori probability for the next time step. The convergence of the decision process can be proven. We demonstrate the benefits of our approach using an active object recognition scenario. The results show that the sequential decision process outperforms a random strategy, both in the sense of recognition rate and number of views necessary to return a decision.