Abstract
We present a methodology and a neural network architecture for the modeling of low- and mid-level visual processing. The network architecture uses local filter operators as basic processing units which can be combined into a network via flexible connections. Using this methodology we design a neuronal network that models the joint processing of oriented contrast changes, their motion and depth. The network reflects the structure and the functionality of visual pathways. We present network responses to a stereo video sequence, highlight the correspondence to biological counterparts, outline the limitations of the methodology, and discuss specific aspects of the processing and the extent of visual tasks that can be successfully carried out by the suggested neuronal architecture.
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Oberhoff, D., Stynen, A., Kolesnik, M. (2006). Neural Network Architecture for Modeling the Joint Visual Perception of Orientation, Motion, and Depth. In: André, E., Dybkjær, L., Minker, W., Neumann, H., Weber, M. (eds) Perception and Interactive Technologies. PIT 2006. Lecture Notes in Computer Science(), vol 4021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11768029_4
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DOI: https://doi.org/10.1007/11768029_4
Publisher Name: Springer, Berlin, Heidelberg
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