Optical-flow features empirical mode decomposition for motion anomaly detection (2017)
- Authors:
- Autor USP: PONTI, MOACIR ANTONELLI - ICMC
- Unidade: ICMC
- DOI: 10.1109/ICASSP.2017.7952387
- Subjects: PROCESSAMENTO DE IMAGENS; BANCO DE DADOS
- Keywords: Signal decomposition; surveillance; video processing
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2017
- Source:
- Título: Proceedings
- ISSN: 2379-190X
- Conference titles: IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
PONTI, Moacir Antonelli e NAZARÉ, Tiago Santana de e KITTLER, Josef. Optical-flow features empirical mode decomposition for motion anomaly detection. 2017, Anais.. Piscataway: IEEE, 2017. Disponível em: https://doi.org/10.1109/ICASSP.2017.7952387. Acesso em: 20 jan. 2026. -
APA
Ponti, M. A., Nazaré, T. S. de, & Kittler, J. (2017). Optical-flow features empirical mode decomposition for motion anomaly detection. In Proceedings. Piscataway: IEEE. doi:10.1109/ICASSP.2017.7952387 -
NLM
Ponti MA, Nazaré TS de, Kittler J. Optical-flow features empirical mode decomposition for motion anomaly detection [Internet]. Proceedings. 2017 ;[citado 2026 jan. 20 ] Available from: https://doi.org/10.1109/ICASSP.2017.7952387 -
Vancouver
Ponti MA, Nazaré TS de, Kittler J. Optical-flow features empirical mode decomposition for motion anomaly detection [Internet]. Proceedings. 2017 ;[citado 2026 jan. 20 ] Available from: https://doi.org/10.1109/ICASSP.2017.7952387 - Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis
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Informações sobre o DOI: 10.1109/ICASSP.2017.7952387 (Fonte: oaDOI API)
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