Abstract
This paper proposes a bottom-up attention model based on pulsed Hebbian-based neural networks that simulate the lateral surround inhibition of neurons with similar visual features. The visual saliency can be represented in binary codes that simulate neuronal pulses in the human brain. Moreover, the model can be extended to the pulsed cosine transform that is very simple in computation. Finally, a dynamic Markov model is proposed to produce the human-like stochastic attention selection. Due to its good performance in eye fixation prediction and low computational complexity, our model can be used in real-time systems such as robot navigation and virtual human system.
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Yu, Y., Wang, B., Zhang, L. (2009). Hebbian-Based Neural Networks for Bottom-Up Visual Attention Systems. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_1
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DOI: https://doi.org/10.1007/978-3-642-10677-4_1
Publisher Name: Springer, Berlin, Heidelberg
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