Lie group model neuromorphic geometric engine for real-time terrain reconstruction from stereoscopic aerial photos

Thomas R. Tsao; Doris Tsao

doi:10.1117/12.271514

4 April 1997 Lie group model neuromorphic geometric engine for real-time terrain reconstruction from stereoscopic aerial photos

Thomas R. Tsao, Doris Tsao

Author Affiliations +

Proceedings Volume 3077, Applications and Science of Artificial Neural Networks III; (1997) https://doi.org/10.1117/12.271514
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

In the 1980's, neurobiologist suggested a simple mechanism in primate visual cortex for maintaining a stable and invariant representation of a moving object. The receptive field of visual neurons has real-time transforms in response to motion, to maintain a stable representation. When the visual stimulus is changed due to motion, the geometric transform of the stimulus triggers a dual transform of the receptive field. This dual transform in the receptive fields compensates geometric variation in the stimulus. This process can be modelled using a Lie group method. The massive array of affine parameter sensing circuits will function as a smart sensor tightly coupled to the passive imaging sensor (retina). Neural geometric engine is a neuromorphic computing device simulating our Lie group model of spatial perception of primate's primal visual cortex. We have developed the computer simulation and experimented on realistic and synthetic image data, and performed a preliminary research of using analog VLSI technology for implementation of the neural geometric engine. We have benchmark tested on DMA's terrain data with their result and have built an analog integrated circuit to verify the computational structure of the engine. When fully implemented on ANALOG VLSI chip, we will be able to accurately reconstruct a 3D terrain surface in real-time from stereoscopic imagery.

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Thomas R. Tsao and Doris Tsao "Lie group model neuromorphic geometric engine for real-time terrain reconstruction from stereoscopic aerial photos", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); https://doi.org/10.1117/12.271514

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