Localization of Visual Codes in the DCT Domain Using Deep Rectifier Neural Networks

Péter Bodnár, Tamás Grósz, László Tóth, László G. Nyúl

Abstract

The reading process of visual codes consists of two steps, localization and data decoding. This paper presents a novel method for QR code localization using deep rectifier neural networks, trained directly in the JPEG DCT domain, thus making image decompression unnecessary. This approach is efficient with respect to both storage and computation cost, being convenient, since camera hardware can provide JPEG stream as their output in many cases. The structure of the neural networks, regularization, and training data parameters, like input vector length and compression level, are evaluated and discussed. The proposed approach is not exclusively for QR codes, but can be adapted to Data Matrix codes or other two-dimensional code types as well.

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Paper Citation


in Harvard Style

Bodnár P., Grósz T., Tóth L. and G. Nyúl L. (2014). Localization of Visual Codes in the DCT Domain Using Deep Rectifier Neural Networks . In Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2014) ISBN 978-989-758-041-3, pages 37-44. DOI: 10.5220/0005125700370044


in Bibtex Style

@conference{anniip14,
author={Péter Bodnár and Tamás Grósz and László Tóth and László G. Nyúl},
title={Localization of Visual Codes in the DCT Domain Using Deep Rectifier Neural Networks},
booktitle={Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2014)},
year={2014},
pages={37-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005125700370044},
isbn={978-989-758-041-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2014)
TI - Localization of Visual Codes in the DCT Domain Using Deep Rectifier Neural Networks
SN - 978-989-758-041-3
AU - Bodnár P.
AU - Grósz T.
AU - Tóth L.
AU - G. Nyúl L.
PY - 2014
SP - 37
EP - 44
DO - 10.5220/0005125700370044