Abstract
We propose a learning system for object categorization which utilizes information from multiple sensors. The system learns not only prior to its deployment in a supervised mode but also in a self-learning mode. A competition based neural network learning algorithm is used to distinguish between representations of different categories. We illustrate the system application on an example of image categorization. A radar guides a selection of candidate images provided by the camera for subsequent analysis by our learning method. Radar information gets coupled with navigational information for improved localization of objects during self-learning.
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Prokhorov, D.V. (2009). A Self-learning System for Object Categorization. In: Filipe, J., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2009. Lecture Notes in Business Information Processing, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01347-8_22
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DOI: https://doi.org/10.1007/978-3-642-01347-8_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01346-1
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