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A Retina-Inspired Neurocomputing Circuit for Image Representation


Fudan University
Shanghai, China


Biological vision systems have become highly optimized over millions of years of evolution, developing complex neural structures to represent and process stimuli. Moreover, biological systems of vision are typically far more efficient than current human-made machine vision systems. The present report describes a non-task-dependent image representation schema that simulates the early phase of a biological neural vision mechanism. We designed a neural model involving multiple types of computational units to simulate ganglion cells and their non-classical receptive fields, local feedback control circuits and receptive field dynamic self-adjustment mechanisms in the retina. We found that, beyond the pixel level, our model was able to represent images self-adaptively and rapidly. In addition, the improved representation was found to substantially facilitate contour detection. We propose that this improvement arose because ganglion cells can resize their receptive fields, enabling multi-scale analysis functionality, a neighborhood referring function and a localized synthesis function. The ganglion cell layer is the starting point of subsequent diverse visual processing. The universality of this cell type and its functional mechanisms suggests that it will be useful for designing image processing algorithms in future.

Short Bio

Hui Wei received the Ph.D. degree at the Department of ComputerSscience at Beijing University of Aeronautics and Astronautics in 1998. From 1998 to 2000, he was a postdoctoral fellow at the Department of Computer Science and the Institute of Artificial Intelligence at Zhejiang University. Since November 2000, he has joined the Department of Computer Science and Engineering at Fudan University. His research interests include artificial intelligence and cognitive science.

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Last updated: December 14, 2013