Добірка наукової літератури з теми "2D Encoding representation"
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Статті в журналах з теми "2D Encoding representation"
He, Qingdong, Hao Zeng, Yi Zeng, and Yijun Liu. "SCIR-Net: Structured Color Image Representation Based 3D Object Detection Network from Point Clouds." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4486–94. http://dx.doi.org/10.1609/aaai.v36i4.20371.
Повний текст джерелаWu, Banghe, Chengzhong Xu, and Hui Kong. "LiDAR Road-Atlas: An Efficient Map Representation for General 3D Urban Environment." Field Robotics 3, no. 1 (January 10, 2023): 435–59. http://dx.doi.org/10.55417/fr.2023014.
Повний текст джерелаYuan, Hangjie, and Dong Ni. "Learning Visual Context for Group Activity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 3261–69. http://dx.doi.org/10.1609/aaai.v35i4.16437.
Повний текст джерелаYang, Xiaobao, Shuai He, Junsheng Wu, Yang Yang, Zhiqiang Hou, and Sugang Ma. "Exploring Spatial-Based Position Encoding for Image Captioning." Mathematics 11, no. 21 (November 4, 2023): 4550. http://dx.doi.org/10.3390/math11214550.
Повний текст джерелаRebollo-Neira, Laura, and Aurelien Inacio. "Enhancing sparse representation of color images by cross channel transformation." PLOS ONE 18, no. 1 (January 26, 2023): e0279917. http://dx.doi.org/10.1371/journal.pone.0279917.
Повний текст джерелаTripura Sundari, Yeluripati Bala, and K. Usha Mahalakshmi. "Enhancing Brain Tumor Diagnosis: A 3D Auto-Encoding Approach for Accurate Classification." International Journal of Scientific Methods in Engineering and Management 01, no. 09 (2023): 38–46. http://dx.doi.org/10.58599/ijsmem.2023.1905.
Повний текст джерелаRybińska-Fryca, Anna, Anita Sosnowska, and Tomasz Puzyn. "Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids." Materials 13, no. 11 (May 30, 2020): 2500. http://dx.doi.org/10.3390/ma13112500.
Повний текст джерелаCohen, Lear, Ehud Vinepinsky, Opher Donchin, and Ronen Segev. "Boundary vector cells in the goldfish central telencephalon encode spatial information." PLOS Biology 21, no. 4 (April 25, 2023): e3001747. http://dx.doi.org/10.1371/journal.pbio.3001747.
Повний текст джерелаCiprian, David, and Vasile Gui. "2D Sensor Based Design of a Dynamic Hand Gesture Interpretation System." Advanced Engineering Forum 8-9 (June 2013): 553–62. http://dx.doi.org/10.4028/www.scientific.net/aef.8-9.553.
Повний текст джерелаHuang, Yuhao, Sanping Zhou, Junjie Zhang, Jinpeng Dong, and Nanning Zheng. "Voxel or Pillar: Exploring Efficient Point Cloud Representation for 3D Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (March 24, 2024): 2426–35. http://dx.doi.org/10.1609/aaai.v38i3.28018.
Повний текст джерелаДисертації з теми "2D Encoding representation"
Abidi, Azza. "Investigating Deep Learning and Image-Encoded Time Series Approaches for Multi-Scale Remote Sensing Analysis in the context of Land Use/Land Cover Mapping." Electronic Thesis or Diss., Université de Montpellier (2022-....), 2024. http://www.theses.fr/2024UMONS007.
Повний текст джерелаIn this thesis, the potential of machine learning (ML) in enhancing the mapping of complex Land Use and Land Cover (LULC) patterns using Earth Observation data is explored. Traditionally, mapping methods relied on manual and time-consuming classification and interpretation of satellite images, which are susceptible to human error. However, the application of ML, particularly through neural networks, has automated and improved the classification process, resulting in more objective and accurate results. Additionally, the integration of Satellite Image Time Series(SITS) data adds a temporal dimension to spatial information, offering a dynamic view of the Earth's surface over time. This temporal information is crucial for accurate classification and informed decision-making in various applications. The precise and current LULC information derived from SITS data is essential for guiding sustainable development initiatives, resource management, and mitigating environmental risks. The LULC mapping process using ML involves data collection, preprocessing, feature extraction, and classification using various ML algorithms. Two main classification strategies for SITS data have been proposed: pixel-level and object-based approaches. While both approaches have shown effectiveness, they also pose challenges, such as the inability to capture contextual information in pixel-based approaches and the complexity of segmentation in object-based approaches.To address these challenges, this thesis aims to implement a method based on multi-scale information to perform LULC classification, coupling spectral and temporal information through a combined pixel-object methodology and applying a methodological approach to efficiently represent multivariate SITS data with the aim of reusing the large amount of research advances proposed in the field of computer vision
Тези доповідей конференцій з теми "2D Encoding representation"
Özkil, Ali Gürcan, and Thomas Howard. "Automatically Annotated Mapping for Indoor Mobile Robot Applications." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71351.
Повний текст джерелаSong, Meishu, Emilia Parada-Cabaleiro, Zijiang Yang, Xin Jing, Kazumasa Togami, Kun Qian*, Björn W. Schuller, and Yoshiharu Yamamoto. "Parallelising 2D-CNNs and Transformers: A Cognitive-based approach for Automatic Recognition of Learners’ English Proficiency." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001000.
Повний текст джерелаLi, Shaohua, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong Liu, and Rick Goh. "Medical Image Segmentation using Squeeze-and-Expansion Transformers." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/112.
Повний текст джерела