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Proceedings Paper

Neural novelty filter for time-sequential imagery
Author(s): Jaroslaw Szostakowski; Slawomir Skoneczny
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Paper Abstract

Image sequences are very difficult to analyze because of their high dimensionality. The large amount of visual data is generated even in a typical situation. That is why the number of data should be limited for information processing. Often, most information in a frame is relatively slowly changing background and only small pieces of a frame are new or novel. Our purpose is to process a time sequence of images and to model objects and/or background from an image sequence in a compact form suitable for recognition and processing. This problem is similar to compression problems and it can be solved optimally by using a truncated Karhunen-Loeve (KL) expansion of the process. This paper describes a new efficient method for novelty filtering of time-sequential images. This method uses a neural approach for calculating a truncate Karhunen-Loeve expansion of the process. The algorithm employs the multilayer neural networks and it exploits the error back-propagation learning algorithm. A neural network implementation seems to be a very promising and effective tool for novelty filtering on image sequence. The validity and performance of the proposed neural network architecture and associated learning algorithm have been tested by extensive computer simulation.

Paper Details

Date Published: 28 March 1995
PDF: 8 pages
Proc. SPIE 2424, Nonlinear Image Processing VI, (28 March 1995); doi: 10.1117/12.205247
Show Author Affiliations
Jaroslaw Szostakowski, Warsaw Univ. of Technology (Poland)
Slawomir Skoneczny, Warsaw Univ. of Technology (Poland)

Published in SPIE Proceedings Vol. 2424:
Nonlinear Image Processing VI
Edward R. Dougherty; Jaakko T. Astola; Harold G. Longbotham; Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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