Optical Methods To Provide Feature Inputs For Adaptive Learning Networks

Larry R. Weiner

doi:10.1117/12.959159

23 December 1980 Optical Methods To Provide Feature Inputs For Adaptive Learning Networks

Larry R. Weiner

Proceedings Volume 0238, Image Processing for Missile Guidance; (1980) https://doi.org/10.1117/12.959159
Event: 24th Annual Technical Symposium, 1980, San Diego, United States

Abstract

Up to this point, optical information processing system designers have taken an end-to-end approach to pattern recognition and feature extraction from imagery. The majority of algorithms that have been proposed for this category of problems have used a completely optical solution in which only the signal conditioning and detection have been implemented by other means. It is the purpose of this paper to present an alternate approach in which optical methods are used to provide image features for an adaptive learning network [1] (ALN). The result of the ALN design process is to specify a functional mapping between input feature space and a set of response variables that can be interpreted as indicators of particular processes associated with the input data. This mathematical mapping can then be reduced to hardware and made to perform on real time inputs. In the case of recognizing patterns in the field of view of airborne sensors, certain useful features of the input image such as the spatial frequency content or optical moment information have been discarded as inputs for the ALN due to computational complexity and the packaging constraints of an aerial platform. It is here that the speed, resolution and potential compactness of coherent optical methods can be used to extract features from input images that otherwise would not be feasible. This paper will describe those optical operations which are applicable for conditioning data for the ALN process, and present an example of how this hybrid approach can be used.

Citation Download Citation

Larry R. Weiner "Optical Methods To Provide Feature Inputs For Adaptive Learning Networks", Proc. SPIE 0238, Image Processing for Missile Guidance, (23 December 1980); https://doi.org/10.1117/12.959159

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KEYWORDS

Sensors

Aluminum nitride

Image processing

Image enhancement

Image filtering

Data modeling

Adaptive optics

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