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Machine Vision for Coin Recognition with ANNs: Effect of Training and Testing Parameters

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Engineering Applications of Neural Networks (EANN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 744))

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Abstract

Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data for object recognition, classification and computer vision segmentation. Features are extracted from input data and used for object classification purposes. Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) are popular tools for pattern recognition applications. The performance of the networks is usually defined in terms of the classification accuracy. However, there are no real design guidelines for training and testing protocols. This research set out to evaluate the effect on accuracy of the design parameters, including: size of the database, number of classes, quality of images, type of network, nature of training and testing strategy. A coin recognition task was used for the evaluation. A set of guidelines for part recognition tasks is presented based on experience with this task.

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Acknowledgement

The research was partially funded by Mitacs and partially funded by Queen’s University.

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Authors and Affiliations

  1. Queen’s University, Kingston, ON, K7L 3N6, Canada

    Vedang Chauhan, Keyur D. Joshi & Brian Surgenor

Authors
  1. Vedang Chauhan
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  2. Keyur D. Joshi
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  3. Brian Surgenor
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Corresponding author

Correspondence to Vedang Chauhan .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Giacomo Boracchi

  2. Democritus University of Thrace, University Campus, Xanthi, Greece

    Lazaros Iliadis

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Univesity of Ioannina, Ioannina, Greece

    Aristidis Likas

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Chauhan, V., Joshi, K.D., Surgenor, B. (2017). Machine Vision for Coin Recognition with ANNs: Effect of Training and Testing Parameters. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_44

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  • DOI: https://doi.org/10.1007/978-3-319-65172-9_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65171-2

  • Online ISBN: 978-3-319-65172-9

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