Characterization of clutter in IR images using maximum likelihood adaptive neural system

The use of neural networks to quantify IR image clutter is described. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse images. The neural network presented is the maximum likelihood ada...

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Bibliographic Details
Published in[1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers pp. 1076 - 1080 vol.2
Main Authors Perlovsky, L.I., Jaskolski, J.J., Chernick, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE Comput. Soc. Press 1992
Subjects
Adaptive systems
Character recognition
Gaussian distribution
Image analysis
Image recognition
Intelligent networks
Maximum likelihood detection
Neural networks
Object detection
Sensor phenomena and characterization
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Summary:The use of neural networks to quantify IR image clutter is described. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse images. The neural network presented is the maximum likelihood adaptive neural system (MLANS). MLANS is a parametric neural network that combines optimal statistical techniques with a model-based approach. It is shown that MLANS is better at image clutter characterization than the traditional quadratic classifier because MLANS is not limited to the usual Gaussian distribution assumption of statistical pattern recognition approaches and can adapt to the image clutter distribution.< >
ISBN:0818631600
9780818631603
ISSN:1058-6393
2576-2303
DOI:10.1109/ACSSC.1992.269133