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Super-resolution of license plate images using attention modules and sub-pixel convolution layers.

V. Nascimento, R. Laroca, J. de A. Lambert, W. Schwartz, and D. Menotti. Comput. Graph., (June 2023)

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Simultaneous Iris and Periocular Region Detection Using Coarse Annotations.D. Lucio, R. Laroca, L. Zanlorensi, G. Moreira, and D. Menotti. SIBGRAPI, page 178-185. IEEE, (2019)Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning.R. Laroca, A. Boslooper, and D. Menotti. CoRR, (2020)Convolutional neural networks for automatic meter reading.R. Laroca, V. Barroso, M. Diniz, G. Gonçalves, W. Schwartz, and D. Menotti. J. Electronic Imaging, 28 (1): 013023 (2019)Towards Image-Based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach.R. Laroca, A. Araujo, L. Zanlorensi, E. de Almeida, and D. Menotti. IEEE Access, (2021)Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks.I. de Oliveira, R. Laroca, D. Menotti, K. Fonseca, and R. Minetto. IEEE Access, (2021)Tell me what you see: A zero-shot action recognition method based on natural language descriptions.V. Estevam, R. Laroca, H. Pedrini, and D. Menotti. Multim. Tools Appl., 83 (9): 28147-28173 (March 2024)Ocular recognition databases and competitions: a survey.L. Zanlorensi, R. Laroca, E. Luz, A. Jr., L. Oliveira, and D. Menotti. Artif. Intell. Rev., 55 (1): 129-180 (2022)Tell me what you see: A zero-shot action recognition method based on natural language descriptions.V. Junior, R. Laroca, D. Menotti, and H. Pedrini. CoRR, (2021)A Benchmark for Iris Location and a Deep Learning Detector Evaluation.E. Severo, R. Laroca, C. Bezerra, L. Zanlorensi, D. Weingaertner, G. Moreira, and D. Menotti. IJCNN, page 1-7. IEEE, (2018)Multi-task Learning for Low-Resolution License Plate Recognition.G. Gonçalves, M. Diniz, R. Laroca, D. Menotti, and W. Schwartz. CIARP, volume 11896 of Lecture Notes in Computer Science, page 251-261. Springer, (2019)

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Super-resolution of license plate images using attention modules and sub-pixel convolution layers.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Super-resolution of license plate images using attention modules and sub-pixel convolution layers.

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Super-resolution of license plate images using attention modules and sub-pixel convolution layers.