Auswahl der wissenschaftlichen Literatur zum Thema „2D Encoding representation“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Inhaltsverzeichnis
Machen Sie sich mit den Listen der aktuellen Artikel, Bücher, Dissertationen, Berichten und anderer wissenschaftlichen Quellen zum Thema "2D Encoding representation" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Zeitschriftenartikel zum Thema "2D Encoding representation"
He, Qingdong, Hao Zeng, Yi Zeng und 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, Nr. 4 (28.06.2022): 4486–94. http://dx.doi.org/10.1609/aaai.v36i4.20371.
Der volle Inhalt der QuelleWu, Banghe, Chengzhong Xu und Hui Kong. „LiDAR Road-Atlas: An Efficient Map Representation for General 3D Urban Environment“. Field Robotics 3, Nr. 1 (10.01.2023): 435–59. http://dx.doi.org/10.55417/fr.2023014.
Der volle Inhalt der QuelleYuan, Hangjie, und Dong Ni. „Learning Visual Context for Group Activity Recognition“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 4 (18.05.2021): 3261–69. http://dx.doi.org/10.1609/aaai.v35i4.16437.
Der volle Inhalt der QuelleYang, Xiaobao, Shuai He, Junsheng Wu, Yang Yang, Zhiqiang Hou und Sugang Ma. „Exploring Spatial-Based Position Encoding for Image Captioning“. Mathematics 11, Nr. 21 (04.11.2023): 4550. http://dx.doi.org/10.3390/math11214550.
Der volle Inhalt der QuelleRebollo-Neira, Laura, und Aurelien Inacio. „Enhancing sparse representation of color images by cross channel transformation“. PLOS ONE 18, Nr. 1 (26.01.2023): e0279917. http://dx.doi.org/10.1371/journal.pone.0279917.
Der volle Inhalt der QuelleTripura Sundari, Yeluripati Bala, und 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, Nr. 09 (2023): 38–46. http://dx.doi.org/10.58599/ijsmem.2023.1905.
Der volle Inhalt der QuelleRybińska-Fryca, Anna, Anita Sosnowska und Tomasz Puzyn. „Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids“. Materials 13, Nr. 11 (30.05.2020): 2500. http://dx.doi.org/10.3390/ma13112500.
Der volle Inhalt der QuelleCohen, Lear, Ehud Vinepinsky, Opher Donchin und Ronen Segev. „Boundary vector cells in the goldfish central telencephalon encode spatial information“. PLOS Biology 21, Nr. 4 (25.04.2023): e3001747. http://dx.doi.org/10.1371/journal.pbio.3001747.
Der volle Inhalt der QuelleCiprian, David, und Vasile Gui. „2D Sensor Based Design of a Dynamic Hand Gesture Interpretation System“. Advanced Engineering Forum 8-9 (Juni 2013): 553–62. http://dx.doi.org/10.4028/www.scientific.net/aef.8-9.553.
Der volle Inhalt der QuelleHuang, Yuhao, Sanping Zhou, Junjie Zhang, Jinpeng Dong und Nanning Zheng. „Voxel or Pillar: Exploring Efficient Point Cloud Representation for 3D Object Detection“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 3 (24.03.2024): 2426–35. http://dx.doi.org/10.1609/aaai.v38i3.28018.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleIn 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
Konferenzberichte zum Thema "2D Encoding representation"
Özkil, Ali Gürcan, und 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.
Der volle Inhalt der QuelleSong, Meishu, Emilia Parada-Cabaleiro, Zijiang Yang, Xin Jing, Kazumasa Togami, Kun Qian*, Björn W. Schuller und 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.
Der volle Inhalt der QuelleLi, Shaohua, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong Liu und 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.
Der volle Inhalt der Quelle