Auswahl der wissenschaftlichen Literatur zum Thema „Scene coordinates regression (SCR)“
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Zeitschriftenartikel zum Thema "Scene coordinates regression (SCR)"
Huang, Min, Zexu Liu, Tianen Liu und Jingyang Wang. „CCDS-YOLO: Multi-Category Synthetic Aperture Radar Image Object Detection Model Based on YOLOv5s“. Electronics 12, Nr. 16 (18.08.2023): 3497. http://dx.doi.org/10.3390/electronics12163497.
Der volle Inhalt der QuelleHe, Rongru, Xiwen Luo, Zhigang Zhang, Wenyu Zhang, Chunyu Jiang und Bingxuan Yuan. „Identification Method of Rice Seedlings Rows Based on Gaussian Heatmap“. Agriculture 12, Nr. 10 (20.10.2022): 1736. http://dx.doi.org/10.3390/agriculture12101736.
Der volle Inhalt der QuelleBallesta, Mónica, Luis Payá, Sergio Cebollada, Oscar Reinoso und Francisco Murcia. „A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images“. Applied Sciences 11, Nr. 16 (16.08.2021): 7521. http://dx.doi.org/10.3390/app11167521.
Der volle Inhalt der QuelleShen, Xiaoyan, Shinan Zhou und Dongsheng Li. „Microdisplacement Measurement Based on F-P Etalon: Processing Method and Experiments“. Sensors 21, Nr. 11 (28.05.2021): 3749. http://dx.doi.org/10.3390/s21113749.
Der volle Inhalt der QuelleMa, Li, Ning Cao, Xiaoliang Feng und Minghe Mao. „Indoor Positioning Algorithm Based on Maximum Correntropy Unscented Information Filter“. ISPRS International Journal of Geo-Information 10, Nr. 7 (28.06.2021): 441. http://dx.doi.org/10.3390/ijgi10070441.
Der volle Inhalt der QuelleSpevakova, S. S., A. G. Spevakov und I. V. Chernetskaya. „Mathematical Model of Multispectral Data Processing for a Mobile Ecology Monitoring Platform“. Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering 13, Nr. 2 (03.08.2023): 153–69. http://dx.doi.org/10.21869/2223-1536-2023-13-2-153-169.
Der volle Inhalt der QuelleWang, Shuzhe, Zakaria Laskar, Iaroslav Melekhov, Xiaotian Li, Yi Zhao, Giorgos Tolias und Juho Kannala. „HSCNet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer“. International Journal of Computer Vision, 06.02.2024. http://dx.doi.org/10.1007/s11263-023-01982-9.
Der volle Inhalt der Quellezhang, kai, Xiaolin Meng und Qing Wang. „An End-to-end Learning Framework for Visual Camera Relocalization Using RGB and RGB-D Images“. Measurement Science and Technology, 22.05.2024. http://dx.doi.org/10.1088/1361-6501/ad4f02.
Der volle Inhalt der QuelleIzquierdo, Rubén, Álvaro Quintanar, David Fernández Llorca, Iván García Daza, Noelia Hernández, Ignacio Parra und Miguel Ángel Sotelo. „Vehicle trajectory prediction on highways using bird eye view representations and deep learning“. Applied Intelligence, 20.07.2022. http://dx.doi.org/10.1007/s10489-022-03961-y.
Der volle Inhalt der QuelleAmeperosa, Ezra, und Pranav A. Bhounsule. „Domain Randomization Using Deep Neural Networks for Estimating Positions of Bolts“. Journal of Computing and Information Science in Engineering 20, Nr. 5 (26.05.2020). http://dx.doi.org/10.1115/1.4047074.
Der volle Inhalt der QuelleDissertationen zum Thema "Scene coordinates regression (SCR)"
Martin-Lac, Victor. „Aerial navigation based on SAR imaging and reference geospatial data“. Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0400.
Der volle Inhalt der QuelleWe seek the algorithmic means of determining the kinematic state of an aerial device from an observation SAR image and reference geospatial data that may be SAR, optical or vector. We determine a transform that relates the observation and reference coordinates and whose parameters are the kinematic state. We follow three approaches. The first one is based on detecting and matching structures such as contours. We propose an iterative closest point algorithm and demonstrate how it can serve to estimate the full kinematic state. We then propose a complete pipeline that includes a learned multimodal contour detector. The second approach is based on a multimodal similarity metric, which is the means of measuring the likelihood that two local patches of geospatial data represent the same geographic point. We determine the kinematic state under the hypothesis of which the SAR image is most similar to the reference geospatial data. The third approach is based on scene coordinates regression. We predict the geographic coordinates of random image patches and infer the kinematic state from these predicted correspondences. However, in this approach, we do not address the fact that the modality of the observation and the reference are different
Konferenzberichte zum Thema "Scene coordinates regression (SCR)"
Cai, Ming, Huangying Zhan, Chamara Saroj Weerasekera, Kejie Li und Ian Reid. „Camera Relocalization by Exploiting Multi-View Constraints for Scene Coordinates Regression“. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00469.
Der volle Inhalt der QuelleFreitas, Rafael, Thiago Paixão, Rodrigo Berriel, Alberto Souza, Claudine Badue und Thiago Santos. „Relevant Traffic Light Localization via Deep Regression“. In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/eniac.2019.9306.
Der volle Inhalt der QuelleAmeperosa, Ezra, und Pranav A. Bhounsule. „Domain Randomization for Detection and Position Estimation of Multiples of a Single Object With Applications to Localizing Bolts on Structures“. In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97393.
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