relation: http://publicatio.bibl.u-szeged.hu/20911/
title: Efficient visual code localization with neural networks
creator:  Bodnár Péter
creator:  Grósz Tamás
creator:  Tóth László
creator:  Nyúl László Gábor
description: The use of computer-readable visual codes became common in our  everyday life both in industrial environments and for private  use. The reading process of visual codes consists of two  steps, namely, localization and data decoding. In this paper  we examine the localization step of visual codes using  conventional and deep rectifier neural networks. They are also  evaluated in the discrete cosine transform domain and shown to  be efficient, which makes full decompression unnecessary for  setups involving JPEG images. This approach is also efficient  from a storage viewpoint and computation cost viewpoint, since  camera hardware can provide a JPEG stream as output in many  cases. The use of neural networks implemented on graphics  processing unit allows real-time automatic code object  localization. In our earlier studies, the proposed approach  was evaluated on the most popular code type, quick response  code, and some other 2D codes as well. Here, we also prove  that deep rectifier networks are also suitable for 1D barcode  localization and present extensive evaluation and comparison  to state-of-the-art approaches.
date: 2018
type: Folyóiratcikk
type: PeerReviewed
format: text
identifier: http://publicatio.bibl.u-szeged.hu/20911/1/Bodnar2018_Article_EfficientVisualCodeLocalizatio.pdf
identifier:     Bodnár Péter;  Grósz Tamás;  Tóth László;  Nyúl László Gábor: Efficient visual code localization with neural networks.   PATTERN ANALYSIS AND APPLICATIONS, 21 (1).  pp. 249-260.  ISSN 1433-7541 (2018)     
identifier: doi:10.1007/s10044-017-0619-6
relation: http://doi.org/10.1007/s10044-017-0619-6
relation: 3210082
language: eng
relation: info:eu-repo/semantics/altIdentifier/doi/10.1007/s10044-017-0619-6
rights: info:eu-repo/semantics/restrictedAccess