A Self-Aiming Camera Based on Neurophysical Principles

Samarth Swarup, Tuna Oezer, Sylvian R. Ray, Thomas J Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The deep layers of the superior colliculus (SC) integrate information from multiple senses to initiate orienting movements in vertebrate animals. A probabilistic model of the SC based on an interpretation of the neuroscientific data has been proposed by Anastasio et. al. [1]. By incorporating this SC model, in the form of an artificial neural network, as the decision mechanism for a system with two senses, hearing and vision, we have constructed and tested a Self-Aiming Camera (SAC). SAC senses and directs its lens toward the best "target" currently in the environment at any moment. Experiments were performed with SAC using several algorithms for combining the multisensory data as a comparison against the SC model. Generally, the SC model is superior in dealing with low amplitude signals and at least equal to any ad hoc model for the full range of unimodal and bimodal targets.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages3201-3206
Number of pages6
Volume4
StatePublished - 2003
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: Jul 20 2003Jul 24 2003

Other

OtherInternational Joint Conference on Neural Networks 2003
Country/TerritoryUnited States
CityPortland, OR
Period7/20/037/24/03

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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