Abstract
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.
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Rothenstein, A.L., Zaharescu, A., Tsotsos, J.K. (2005). TarzaNN : A General Purpose Neural Network Simulator for Visual Attention Modeling. In: Paletta, L., Tsotsos, J.K., Rome, E., Humphreys, G. (eds) Attention and Performance in Computational Vision. WAPCV 2004. Lecture Notes in Computer Science, vol 3368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30572-9_12
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DOI: https://doi.org/10.1007/978-3-540-30572-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24421-9
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