Artificial Intelligence
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Embedded neural networks: exploiting constraints
Neural Networks - Special issue on neural control and robotics: biology and technology
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Transinformation for Active Object Recognition
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Information Theoretic Focal Length Selection for Real-Time Active 3-D Object Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Coevolution of active vision and feature selection
Biological Cybernetics
The Amsterdam Library of Object Images
International Journal of Computer Vision
A situated model for sensory-motor coordination in gaze control
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
A hierarchical Bayesian framework for multimodal active perception
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Active vision models can simplify visual tasks, provided that they can select sensible actions given incoming sensory inputs. Many active vision models have been proposed, but a comparative evaluation of these models is lacking. We present a comparison of active vision models from two different approaches. The ''probabilistic approach'' is an approach in which state estimation is the central goal. The ''behavioural approach'' is an approach that does not divide the vision process in a state estimation and an acting phase. We identify different types of models of the probabilistic approach, and introduce a model inspired on the behavioural approach. We describe these types of models in a common framework and evaluate their performances on a task of viewpoint selection for the classification of three-dimensional objects. The experimental results reveal how the performances of the active vision models relate to each other. For example, the behavioural model performs as good as the best model from the probabilistic approach. Overall, the experimental results reveal relations between the usefulness of active vision, the number of objects involved in the classification task, and the richness of the visual observations of the models. We conclude that research on active vision should aim at reaching a deeper understanding of these relations by applying active vision models to more complex and real-world tasks.