Abstract
The need for autonomous systems to work under unanticipated conditions requires the use of smart sensors. High resolution systems develop tremendous computational loads. Inspiration from animal vision systems can guide us in developing preprocessing approaches implementable in real time with high resolution and deduced computational load. Given a high quality optical path and a 2D array of photodetectors, the resolution of a digital image is determined by the density of photodetectors sampling the image. In order to reconstruct an image, resolution is limited by the distance between adjacent detectors. However, animal eyes resolve images 10-100 times better than either the acceptance angle of a single photodetector or the center-to-center distance between neighboring photodetectors. A new model of the fly's visual system emulates this improved performance, offering a different approach to subpixel resolution. That an animal without a cortex is capable of this performance suggests that high level computation is not involved. The model takes advantage of a photoreceptor cell's internal structure for capturing light. This organelle is a waveguide. Neurocircuitry exploits the waveguide's optical nonlinearities, namely in the shoulder region of its gaussian sensitivity-profile, to extract high resolution information from the visual scene. The receptive fields of optically disparate inputs overlap in space. Photoreceptor input is continuous rather than discretely sampled. The output of the integrating module is a signal proportional to the position of the target within the detector array. For tracking a point source, resolution is 10 times better than the detector spacing. For locating absolute position and orientation of an edge, the model performs similarly. Analog processing is used throughout. Each element is an independent processor of local luminance. Information processing is in real time with continuous update. This processing principle will be reproduced in an analog integrated circuit using photodiodes and fiber optic waveguides as the nonlinear light sensing devices, current mirrors and opamp circuits for the processing. The outputs of this circuit will go to other artificial neural networks for further processing.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael J. Wilcox, Donald C. Thelen, "Fiber optics that fly", Proc. SPIE 2824, Adaptive Computing: Mathematical and Physical Methods for Complex Environments, (11 November 1996); doi: 10.1117/12.258127; https://doi.org/10.1117/12.258127
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