S. Furman, Y. Y. Zeevi


Processing and analysis of images are implemented in the multidimensional space of visual information representation. This space includes the well investigated dimensions of intensity, color and spatio-temporal frequency. There are, however, additional less investigated dimensions such as curvature, size and depth (for example - from binocular disparity). Along these dimensions, the human visual system (HVS) enhances and emphasizes important image attributes by adaptation and nonlinear filtering. It is interesting and possible to emulate the visual system processing of images along these dimensions, in order to achieve intelligent image processing and computer vision. Sparsely connected, recurrent adaptive sensory neural network (NN), incorporating non-linear interactions in the feedback loops, are presented. Such generic NN exhibit Automatic Gain Control (AGC) model of processing along the visual dimensions. The results are compared with those of psychophysical experiments exhibiting good reproduction of visual illusions.


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Paper Citation

in Harvard Style

Furman S. and Zeevi Y. (2010). AUTOMATIC GAIN CONTROL NETWORKS FOR MULTIDIMENTIONAL VISUAL ADAPTATION . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 163-175. DOI: 10.5220/0003061901630175

in Bibtex Style

author={S. Furman and Y. Y. Zeevi},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},

in EndNote Style

JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
SN - 978-989-8425-32-4
AU - Furman S.
AU - Zeevi Y.
PY - 2010
SP - 163
EP - 175
DO - 10.5220/0003061901630175