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 |
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| 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Marichal-Hernández, José Gil Rodríguez-Ramos, José Manuel Pérez Jiménez, Rafael Gómez-Cárdenes, Óscar Son, Jung-Young |
| AuthorAffiliation | 4 Research & Development Department, Wooptix S.L., 38204 La Laguna, Spain 1 Department of Industrial Engineering, Universidad de La Laguna, 38200 La Laguna, Spain; ogomezca@ull.edu.es (Ó.G.-C.); jmramos@ull.edu.es (J.M.R.-R.) 2 Biomedical Engineering Department, Konyang University, Nonsan-si 320-711, Republic of Korea; jyson@konyang.ac.kr 3 Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas, Spain; rafael.perez@ulpgc.es |
| AuthorAffiliation_xml | – name: 1 Department of Industrial Engineering, Universidad de La Laguna, 38200 La Laguna, Spain; ogomezca@ull.edu.es (Ó.G.-C.); jmramos@ull.edu.es (J.M.R.-R.) – name: 2 Biomedical Engineering Department, Konyang University, Nonsan-si 320-711, Republic of Korea; jyson@konyang.ac.kr – name: 3 Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas, Spain; rafael.perez@ulpgc.es – name: 4 Research & Development Department, Wooptix S.L., 38204 La Laguna, Spain |
| Author_xml | – sequence: 1 givenname: Óscar orcidid: 0000-0002-7951-982X surname: Gómez-Cárdenes fullname: Gómez-Cárdenes, Óscar – sequence: 2 givenname: José Gil orcidid: 0000-0003-2297-8483 surname: Marichal-Hernández fullname: Marichal-Hernández, José Gil – sequence: 3 givenname: Jung-Young orcidid: 0000-0001-6099-0577 surname: Son fullname: Son, Jung-Young – sequence: 4 givenname: Rafael orcidid: 0000-0002-8849-592X surname: Pérez Jiménez fullname: Pérez Jiménez, Rafael – sequence: 5 givenname: José Manuel surname: Rodríguez-Ramos fullname: Rodríguez-Ramos, José Manuel |
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| SubjectTerms | 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 |
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| Title | An Encoder–Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation |
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