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Computer Vision, Graphics and Image Processing pp 597–609Cite as

Object Recognition Using Reflex Fuzzy Min-Max Neural Network with Floating Neurons

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4338))

Abstract

This paper proposes an object recognition system that is invariant to rotation, translation and scale and can be trained under partial supervision. The system is divided into two sections namely, feature extraction and recognition sections. Feature extraction section uses proposed rotation, translation and scale invariant features. Recognition section consists of a novel Reflex Fuzzy Min-Max Neural Network (RFMN) architecture with “Floating Neurons”. RFMN is capable to learn mixture of labeled and unlabeled data which enables training under partial supervision. Learning under partial supervision is of high importance for the practical implementation of pattern recognition systems, as it may not be always feasible to get a fully labeled dataset for training or cost to label all samples is not affordable. The proposed system is tested on shape data-base available online, Marathi and Bengali digits. Results are compared with “General Fuzzy Min-Max Neural Network” proposed by Gabrys and Bargiela.

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Author information

Authors and Affiliations

  1. Electronics & Elec. Communication Engg. Department, Indian Institute of Technology, Kharagpur, 721302, India

    A. V. Nandedkar & P. K. Biswas

Authors
  1. A. V. Nandedkar
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  2. P. K. Biswas
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, IIT Delhi, New Delhi, India

    Prem K. Kalra

  2. School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel

    Shmuel Peleg

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© 2006 Springer-Verlag Berlin Heidelberg

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Nandedkar, A.V., Biswas, P.K. (2006). Object Recognition Using Reflex Fuzzy Min-Max Neural Network with Floating Neurons. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_53

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  • DOI: https://doi.org/10.1007/11949619_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68301-8

  • Online ISBN: 978-3-540-68302-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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