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2017 | OriginalPaper | Chapter

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

Authors : Vedang Chauhan, Keyur D. Joshi, Brian Surgenor

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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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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Metadata
Title
Machine Vision for Coin Recognition with ANNs: Effect of Training and Testing Parameters
Authors
Vedang Chauhan
Keyur D. Joshi
Brian Surgenor
Copyright Year
2017
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-319-65171-2

Electronic ISBN: 978-3-319-65172-9

Copyright Year: 2017

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

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