An evaluation of deep learning techniques for QR code detection (2019)
- Authors:
- USP affiliated authors: HIRATA, NINA SUMIKO TOMITA - IME ; BLANGER, LEONARDO - IME
- Unidade: IME
- DOI: 10.1109/ICIP.2019.8803075
- Subjects: APRENDIZAGEM PROFUNDA; RECONHECIMENTO DE PADRÕES
- Keywords: QR code; single shot detector; part-based object detection
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2019
- Source:
- Título: Proceedings
- Conference titles: IEEE International Conference on Image Processing - ICIP
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
BLANGER, Leonardo e HIRATA, Nina Sumiko Tomita. An evaluation of deep learning techniques for QR code detection. 2019, Anais.. Piscataway: IEEE, 2019. Disponível em: https://doi.org/10.1109/ICIP.2019.8803075. Acesso em: 25 mar. 2026. -
APA
Blanger, L., & Hirata, N. S. T. (2019). An evaluation of deep learning techniques for QR code detection. In Proceedings. Piscataway: IEEE. doi:10.1109/ICIP.2019.8803075 -
NLM
Blanger L, Hirata NST. An evaluation of deep learning techniques for QR code detection [Internet]. Proceedings. 2019 ;[citado 2026 mar. 25 ] Available from: https://doi.org/10.1109/ICIP.2019.8803075 -
Vancouver
Blanger L, Hirata NST. An evaluation of deep learning techniques for QR code detection [Internet]. Proceedings. 2019 ;[citado 2026 mar. 25 ] Available from: https://doi.org/10.1109/ICIP.2019.8803075 - An analysis of sample synthesis for deep learning based object detection
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- Morphological operator design from training data
- Automatic labeling of handwritten mathematical symbols via expression matching
- Fast QR code detection in arbitrarily acquired images
- Remoção de compasso usando combinações de operadores heurísticos e treinados automaticamente
- Mathematical symbol hypothesis recognition with rejection option
- Matching based ground-truth annotation for online handwritten mathematical expressions
- Top-down online handwritten mathematical expression parsing with graph grammar
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