Literatura académica sobre el tema "2D Encoding representation"
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Artículos de revistas sobre el tema "2D Encoding representation"
He, Qingdong, Hao Zeng, Yi Zeng y 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, n.º 4 (28 de junio de 2022): 4486–94. http://dx.doi.org/10.1609/aaai.v36i4.20371.
Texto completoWu, Banghe, Chengzhong Xu y Hui Kong. "LiDAR Road-Atlas: An Efficient Map Representation for General 3D Urban Environment". Field Robotics 3, n.º 1 (10 de enero de 2023): 435–59. http://dx.doi.org/10.55417/fr.2023014.
Texto completoYuan, Hangjie y Dong Ni. "Learning Visual Context for Group Activity Recognition". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 4 (18 de mayo de 2021): 3261–69. http://dx.doi.org/10.1609/aaai.v35i4.16437.
Texto completoYang, Xiaobao, Shuai He, Junsheng Wu, Yang Yang, Zhiqiang Hou y Sugang Ma. "Exploring Spatial-Based Position Encoding for Image Captioning". Mathematics 11, n.º 21 (4 de noviembre de 2023): 4550. http://dx.doi.org/10.3390/math11214550.
Texto completoRebollo-Neira, Laura y Aurelien Inacio. "Enhancing sparse representation of color images by cross channel transformation". PLOS ONE 18, n.º 1 (26 de enero de 2023): e0279917. http://dx.doi.org/10.1371/journal.pone.0279917.
Texto completoTripura Sundari, Yeluripati Bala y 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, n.º 09 (2023): 38–46. http://dx.doi.org/10.58599/ijsmem.2023.1905.
Texto completoRybińska-Fryca, Anna, Anita Sosnowska y Tomasz Puzyn. "Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids". Materials 13, n.º 11 (30 de mayo de 2020): 2500. http://dx.doi.org/10.3390/ma13112500.
Texto completoCohen, Lear, Ehud Vinepinsky, Opher Donchin y Ronen Segev. "Boundary vector cells in the goldfish central telencephalon encode spatial information". PLOS Biology 21, n.º 4 (25 de abril de 2023): e3001747. http://dx.doi.org/10.1371/journal.pbio.3001747.
Texto completoCiprian, David y Vasile Gui. "2D Sensor Based Design of a Dynamic Hand Gesture Interpretation System". Advanced Engineering Forum 8-9 (junio de 2013): 553–62. http://dx.doi.org/10.4028/www.scientific.net/aef.8-9.553.
Texto completoHuang, Yuhao, Sanping Zhou, Junjie Zhang, Jinpeng Dong y Nanning Zheng. "Voxel or Pillar: Exploring Efficient Point Cloud Representation for 3D Object Detection". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 3 (24 de marzo de 2024): 2426–35. http://dx.doi.org/10.1609/aaai.v38i3.28018.
Texto completoTesis sobre el tema "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.
Texto completoIn 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
Actas de conferencias sobre el tema "2D Encoding representation"
Özkil, Ali Gürcan y Thomas Howard. "Automatically Annotated Mapping for Indoor Mobile Robot Applications". En 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.
Texto completoSong, Meishu, Emilia Parada-Cabaleiro, Zijiang Yang, Xin Jing, Kazumasa Togami, Kun Qian*, Björn W. Schuller y Yoshiharu Yamamoto. "Parallelising 2D-CNNs and Transformers: A Cognitive-based approach for Automatic Recognition of Learners’ English Proficiency". En Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001000.
Texto completoLi, Shaohua, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong Liu y Rick Goh. "Medical Image Segmentation using Squeeze-and-Expansion Transformers". En 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.
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