Elements of information theory
Elements of information theory
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Viewpoint Selection-A Classifier Independent Learning Approach
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Learning Temporal Context in Active Object Recognition Using Bayesian Analysis
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Alignment by Maximization of Mutual Information
Alignment by Maximization of Mutual Information
Sensor planning for object search
Sensor planning for object search
Transinformation for Active Object Recognition
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Active learning with statistical models
Journal of Artificial Intelligence Research
A Comparison of Decision Making Criteria and Optimization Methods for Active Robotic Sensing
NMA '02 Revised Papers from the 5th International Conference on Numerical Methods and Applications
On Optimal Camera Parameter Selection in Kalman Filter Based Object Tracking
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Optimal Camera Parameter Selection for State Estimation with Applications in Object Recognition
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
On Fusion of Multiple Views for Active Object Recognition
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Real-Time Gesture Recognition by Learning and Selective Control of Visual Interest Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sensor management using an active sensing approach
Signal Processing
Task oriented facial behavior recognition with selective sensing
Computer Vision and Image Understanding
Efficient Discriminant Viewpoint Selection for Active Bayesian Recognition
International Journal of Computer Vision
A probabilistic model of eye movements in concept formation
Neurocomputing
Active Scheduling of Organ Detection and Segmentation in Whole-Body Medical Images
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Comparing active vision models
Image and Vision Computing
Robust sequential view planning for object recognition using multiple cameras
Image and Vision Computing
Sensor selection for active information fusion
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Gaussian process models of spatial aggregation algorithms
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Task oriented facial behavior recognition with selective sensing
Computer Vision and Image Understanding
Approximate nonmyopic sensor selection via submodularity and partitioning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Information driven search for point sources of gamma radiation
Signal Processing
Multi-step multi-camera view planning for real-time visual object tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Sub-sampling: Real-time vision for micro air vehicles
Robotics and Autonomous Systems
Aspects of optimal viewpoint selection and viewpoint fusion
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Sensor management: a new paradigm for automatic video surveillance
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Design methodology for context-aware wearable sensor systems
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Robotic object detection: learning to improve the classifiers using sparse graphs for path planning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Contextually guided semantic labeling and search for three-dimensional point clouds
International Journal of Robotics Research
Active planning for underwater inspection and the benefit of adaptivity
International Journal of Robotics Research
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
Online control of active camera networks for computer vision tasks
ACM Transactions on Sensor Networks (TOSN)
Towards active event recognition
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We introduce a formalism for optimal sensor parameter selection for iterative state estimation in static systems. Our optimality criterion is the reduction of uncertainty in the state estimation process, rather than an estimator-specific metric (e.g., minimum mean squared estimate error). The claim is that state estimation becomes more reliable if the uncertainty and ambiguity in the estimation process can be reduced. We use Shannon's information theory to select information-gathering actions that maximize mutual information, thus optimizing the information that the data conveys about the true state of the system. The technique explicitly takes into account the a priori probabilities governing the computation of the mutual information. Thus, a sequential decision process can be formed by treating the a priori probability at a certain time step in the decision process as the a posteriori probability of the previous time step. We demonstrate the benefits of our approach in an object recognition application using an active camera for sequential gaze control and viewpoint selection. We describe experiments with discrete and continuous density representations that suggest the effectiveness of the approach.