Authors:
Tyler W. Garaas
;
Frank Marino
and
Marc Pomplun
Affiliation:
University of Massachusetts Boston, United States
Keyword(s):
Robotic Vision, Neural Modeling, Camera Control, Auto White Balance, Auto Exposure.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
Recently there has been growing interest in creating large-scale simulations of certain areas in the brain. The areas that are receiving the overwhelming focus are visual in nature, which may provide a means to compute some of the complex visual functions that have plagued AI researchers for many decades; robust object recognition, for example. Additionally, with the recent introduction of cheap computational hardware capable of computing at several teraflops, real-time robotic vision systems will likely be implemented using simplified neural models based on their slower, more realistic counterparts. This paper presents a series of small neural networks that can be integrated into a neural model of the human retina to automatically control the white-balance and exposure parameters of a standard video camera to optimize the computational processing performed by the neural model. Results of a sample implementation including a comparison with proprietary methods are presented. One
strong advantage that these integrated sub-networks possess over proprietary mechanisms is that ‘attention’ signals could be used to selectively optimize areas of the image that are most relevant to the task at hand.
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