Abstract
A capability of depth perception in biological visual systems evolved throughout thousands of years to help animals and us, humans, to survive in a real life. This ability has helped us to navigate and avoid threatening obstacles. However, we still know very little about the biological processes that lead to such a perfection which is by far not achievable for artificial vision systems. Thus, proper models of these mechanisms would help in their better comprehension, as well as they could guide construction of better computer stereovision systems. In this paper we try to propose a new topology of an artificial neural network for the stereovision system. We try to construct a very simple model of a binocular system that is biologically inspired in a behavioral aspect and which, at the same time, is computationally efficient. It is a hybrid that consists of the convolutional, binocular receptive, and the Hamming neural networks. The input signal is non-parametrically transformed for better statistical preconditioning. The paper ends with the experimental results.
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Cyganek, B. (2005). Artificial Neural Receptive Field for Stereovision. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_69
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DOI: https://doi.org/10.1007/11550822_69
Publisher Name: Springer, Berlin, Heidelberg
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