Abstract
This study investigates the processing of sonar signals with neural networks for robust recognition of indoor robot environment composed of simple objects (plane, corner, edge and cylinder). The neural networks can differentiate more targets with higher accuracy. It achieves this by exploiting the identifying features extracted from sonar signals. In this paper we compare two different architectures of neural networks (global and specialized structure) in term of classification rates, the best classifier obtained is used to recognize a robot environment. The results strengthen our claims that sonar can be used as a viable system for object recognition in robotics and other application domains.
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© 2004 Springer-Verlag Berlin Heidelberg
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Oufroukh, N.A., Colle, E. (2004). Neural Networks for Improved Target Differentiation with Sonar. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_39
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DOI: https://doi.org/10.1007/978-3-540-27868-9_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22570-6
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