Locating urban trees near electric wires using Google Street View Photos: a new dataset and a semi-supervised learning approach in the wild (2022)
- Authors:
- USP affiliated authors: HIRATA JUNIOR, ROBERTO - IME ; OLIVEIRA, ARTUR ANDRÉ ALMEIDA DE MACEDO - IME
- Unidade: IME
- DOI: 10.1109/CVPRW56347.2022.00474
- Subjects: VISÃO COMPUTACIONAL; REDES NEURAIS; PROCESSAMENTO DE IMAGENS
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2022
- Source:
- Título: Proceedings
- Conference titles: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops - CVPRW
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
OLIVEIRA, Artur André Almeida de Macedo e WANG, Zhangyang e HIRATA JÚNIOR, Roberto. Locating urban trees near electric wires using Google Street View Photos: a new dataset and a semi-supervised learning approach in the wild. 2022, Anais.. Piscataway: IEEE, 2022. Disponível em: https://doi.org/10.1109/CVPRW56347.2022.00474. Acesso em: 27 dez. 2025. -
APA
Oliveira, A. A. A. de M., Wang, Z., & Hirata Júnior, R. (2022). Locating urban trees near electric wires using Google Street View Photos: a new dataset and a semi-supervised learning approach in the wild. In Proceedings. Piscataway: IEEE. doi:10.1109/CVPRW56347.2022.00474 -
NLM
Oliveira AAA de M, Wang Z, Hirata Júnior R. Locating urban trees near electric wires using Google Street View Photos: a new dataset and a semi-supervised learning approach in the wild [Internet]. Proceedings. 2022 ;[citado 2025 dez. 27 ] Available from: https://doi.org/10.1109/CVPRW56347.2022.00474 -
Vancouver
Oliveira AAA de M, Wang Z, Hirata Júnior R. Locating urban trees near electric wires using Google Street View Photos: a new dataset and a semi-supervised learning approach in the wild [Internet]. Proceedings. 2022 ;[citado 2025 dez. 27 ] Available from: https://doi.org/10.1109/CVPRW56347.2022.00474 - INACITY - INvestigate and Analyze a CITY
- INvestigate and Analyse a City - INACITY
- SDBM: Supervised Decision Boundary Maps for machine learning classifiers
- Overcoming challenging crban images: deep learning and data integration methods for detecting trees entangled with power lines
- Detecting tree and wire entanglements with deep learning
- Improving self-supervised dimensionality reduction: exploring hyperparameters and pseudo-labeling strategies
- Improving image classification tasks using fused embeddings and multimodal models
- Stability analysis of supervised decision boundary maps
- Segmentação de imagens por morfologia matemática
- Projeto de operadores morfológicos para imagens e sinais: abordagem de reticulados finitos discretos
Informações sobre o DOI: 10.1109/CVPRW56347.2022.00474 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 |
3123222.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
