RT info:eu-repo/semantics/article
T1 An Encoder-Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation
A1 Gómez Cárdenes, Óscar
A1 Marichal Hernández, José Gil
A1 Son, Jung Young
A1 Pérez Jiménez, Rafael
A1 Rodríguez Ramos, José Manuel
A2 Ingeniería Industrial
K1 Radon transform
K1 scale-space methods
K1 multiscale DRT
K1 barcodes
K1 encoder–decoder
K1 pixelwise segmentation
K1 classical signal processing
AB 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.
YR 2023
FD 2023
LK http://riull.ull.es/xmlui/handle/915/41146
UL http://riull.ull.es/xmlui/handle/915/41146
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 25-abr-2025