Artificial Intelligence
Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Active Gesture Recognition Using Partially Observable markov Decision Processes
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Spatiotemporal Abstraction of Stochastic Sequential Processes
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Q-learning of sequential attention for visual object recognition from informative local descriptors
ICML '05 Proceedings of the 22nd international conference on Machine learning
Attention-Based Dynamic Visual Search Using Inner-Scene Similarity: Algorithms and Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reinforcement Learning for Decision Making in Sequential Visual Attention
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An Approach for Preparing Groundtruth Data and Evaluating Visual Saliency Models
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Learning sequential visual attention control through dynamic state space discretization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Online learning of task-driven object-based visual attention control
Image and Vision Computing
Attention to multiple local critics in decision making and control
Expert Systems with Applications: An International Journal
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Learning of position-invariant object representation across attention shifts
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
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This paper proposes a model of selective attention for visual search tasks, based on a framework for sequential decision-making. The model is implemented using a fixed pan-tilt-zoom camera in a visually cluttered lab environment, which samples the environment at discrete time steps. The agent has to decide where to fixate next based purely on visual information, in order to reach the region where a target object is most likely to be found. The model consists of two interacting modules. A reinforcement learning module learns a policy on a set of regions in the room for reaching the target object, using as objective function the expected value of the sum of discounted rewards. By selecting an appropriate gaze direction at each step, this module provides top-down control in the selection of the next fixation point. The second module performs “within fixation” processing, based exclusively on visual information. Its purpose is twofold: to provide the agent with a set of locations of interest in the current image, and to perform the detection and identification of the target object. Detailed experimental results show that the number of saccades to a target object significantly decreases with the number of training epochs. The results also show the learned policy to find the target object is invariant to small physical displacements as well as object inversion.