Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/124220
Title: An Encoder-Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation
Authors: Gómez-Cárdenes, Óscar
Marichal-Hernández, José Gil
Son, Jung Young
Pérez Jiménez, Rafael 
Rodríguez-Ramos, José Manuel
UNESCO Clasification: 3325 Tecnología de las telecomunicaciones
Keywords: Barcodes
Classical Signal Processing
Encoder–Decoder
Multiscale Drt
Pixelwise Segmentation, et al
Issue Date: 2023
Journal: Sensors (Basel, Switzerland)
Abstract: In this work, two methods are proposed for solving the problem of one-dimensional barcode segmentation in images, with an emphasis on augmented reality (AR) applications. These methods take the partial discrete Radon transform as a building block. The first proposed method uses overlapping tiles for obtaining good angle precision while maintaining good spatial precision. The second one uses an encoder-decoder structure inspired by state-of-the-art convolutional neural networks for segmentation while maintaining a classical processing framework, thus not requiring training. It is shown that the second method's processing time is lower than the video acquisition time with a 1024 × 1024 input on a CPU, which had not been previously achieved. The accuracy it obtained on datasets widely used by the scientific community was almost on par with that obtained using the most-recent state-of-the-art methods using deep learning. Beyond the challenges of those datasets, the method proposed is particularly well suited to image sequences taken with short exposure and exhibiting motion blur and lens blur, which are expected in a real-world AR scenario. Two implementations of the proposed methods are made available to the scientific community: one for easy prototyping and one optimised for parallel implementation, which can be run on desktop and mobile phone CPUs.
URI: http://hdl.handle.net/10553/124220
DOI: 10.3390/s23136109
Source: Sensors (Basel, Switzerland)[EISSN 1424-8220],v. 23 (13), (Julio 2023)
Appears in Collections:Artículos
Adobe PDF (7,65 MB)
Show full item record

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.