An Encoder–Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation

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...

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Vydané v:Sensors (Basel, Switzerland) Ročník 23; číslo 13; s. 6109
Hlavní autori: Gómez-Cárdenes, Óscar, Marichal-Hernández, José Gil, Son, Jung-Young, Pérez Jiménez, Rafael, Rodríguez-Ramos, José Manuel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 03.07.2023
MDPI
Predmet:
Accuracy
Algorithms
Augmented Reality
Bar codes
barcodes
Camcorders
Cameras
Datasets
Deep learning
encoder–decoder
Image Processing, Computer-Assisted - methods
Localization
Machine learning
Methods
multiscale DRT
Neural networks
Neural Networks, Computer
pixelwise segmentation
Radon transform
scale-space methods
Semantics
Sensors
scale-space methods
classical signal processing
encoder–decoder
Radon transform
barcodes
multiscale DRT
pixelwise segmentation
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23136109