Elements of information theory
Elements of information theory
Adaptive 3-D Object Recognition from Multiple Views
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Neural Computation
Information-based objective functions for active data selection
Neural Computation
Planning multiple observations for object recognition
International Journal of Computer Vision - Special issue on active vision II
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Active object recognition integrating attention and viewpoint control
Computer Vision and Image Understanding
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Object Recognition: Looking for Differences
International Journal of Computer Vision - Special issue: Research at McGill University
On the Sequential Accumulation of Evidence
International Journal of Computer Vision - Special issue: Research at McGill University
Tabu Search
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Feature Space Trajectory Methods for Active Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing 3D Objects by Generating Random Actions
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
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
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Conditional Feature Sensitivity: A Unifying View on Active Recognition and Feature Selection
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Fast Discriminant Approach to Active Object Recognition and Pose Estimation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Model-based classification trees
IEEE Transactions on Information Theory
Robust sequential view planning for object recognition using multiple cameras
Image and Vision Computing
Multiple viewpoint recognition and localization
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
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
Active exploration for robust object detection
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A Computational Learning Theory of Active Object Recognition Under Uncertainty
International Journal of Computer Vision
Contextually guided semantic labeling and search for three-dimensional point clouds
International Journal of Robotics Research
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This paper presents a novel viewpoint selection criterion for active object recognition and pose estimation whose key advantage resides in its low computational cost with respect to current popular approaches in the literature. The proposed observation selection criterion associates high utility with observations that predictably facilitate distinction between pairs of competing hypotheses by a Bayesian classifier. Rigorous experimentation of the proposed approach was conducted on two case studies, involving synthetic and real data, respectively. The results show the proposed algorithm to perform better than a random navigation strategy in terms of the amount of data required for recognition while being much faster than a strategy based on mutual information, without compromising accuracy.