An encoder-decoder architecture within a classical signal processing framework for real-time barcode segmentation.

Authors of the paper

• Óscar Gómez-Cárdenes (University of La Laguna)
• José Gil Marichal-Hernández (University of La Laguna)
• Jung-Young Son (Konyang University)
• Rafael Pérez Jiménez (IDeTIC, University of Las Palmas de Gran Canaria)
• José Manuel Rodríguez-Ramos (Wooptix S.L., University of La Laguna)

Citing this work

If using this work, please cite us.

Gómez-Cárdenes, Ó.; Marichal-Hernández, J.G.; Son, J.-Y.; Pérez Jiménez, R.; Rodríguez-Ramos, J.M. An Encoder–Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation. Sensors 2023, 23, 6109. https://doi.org/10.3390/s23136109

Description

This code accompanies the paper "An encoder-decoder architecture within a classical signal processing framework for real-time barcode segmentation".

Link to the paper: https://www.mdpi.com/2371152

The four algorithms analyzed in this paper are provided in the code:

• PDRT 2: Partial Discrete Radon Transform with tile_size=2.
• PDRT 32: Partial Discrete Radon Transform with tile_size=32.
• PS DRT: Partial Strided DRT based detector with tile_size=32 and stride=2.
• MDD DRT: Multiscale Domain Detector based on the DRT.

For each of these algorithms, two different implementations are provided,

1. Python with Numba: used for prototyping and design (located in ./python)
2. Halide in C++: used for performance targeting different architectures (located in ./cpp)

Using the Python version

See python/README.md

Using the C++/Halide version

See cpp/README.md

License

See license file