Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attending to Motion: Localizing and Classifying Motion Patterns in Image Sequences
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Modeling the Dynamics of Feature Binding During Object-Selective Attention
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
The JAMF Attention Modelling Framework
Attention in Cognitive Systems
Second-Order (non-fourier) attention-based face detection
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Towards a biologically plausible active visual search model
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
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A number of computational models of visual attention exist, but making comparisons is difficult due to the incompatible implementations and levels at which the simulations are conducted. To address this issue, we have developed a general-purpose neural network simulator that allows all of these models to be implemented in a unified framework. The simulator allows for the distributed execution of models, in a heterogeneous environment. Graphical tools are provided for the development of models by non-programmers and a common model description format facilitates the exchange of models. In this paper we will present the design of the simulator and results that demonstrate its generality.