Literatura científica selecionada sobre o tema "Photometric gaussian mixtures"
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Artigos de revistas sobre o assunto "Photometric gaussian mixtures"
Crombez, Nathan, El Mustapha Mouaddib, Guillaume Caron e Francois Chaumette. "Visual Servoing With Photometric Gaussian Mixtures as Dense Features". IEEE Transactions on Robotics 35, n.º 1 (fevereiro de 2019): 49–63. http://dx.doi.org/10.1109/tro.2018.2876765.
Texto completo da fonteHatfield, P. W., I. A. Almosallam, M. J. Jarvis, N. Adams, R. A. A. Bowler, Z. Gomes, S. J. Roberts e C. Schreiber. "Augmenting machine learning photometric redshifts with Gaussian mixture models". Monthly Notices of the Royal Astronomical Society 498, n.º 4 (11 de setembro de 2020): 5498–510. http://dx.doi.org/10.1093/mnras/staa2741.
Texto completo da fonteJones, Daniel M., e Alan F. Heavens. "Gaussian mixture models for blended photometric redshifts". Monthly Notices of the Royal Astronomical Society 490, n.º 3 (27 de setembro de 2019): 3966–86. http://dx.doi.org/10.1093/mnras/stz2687.
Texto completo da fonteAnsari, Zoe, Adriano Agnello e Christa Gall. "Mixture models for photometric redshifts". Astronomy & Astrophysics 650 (junho de 2021): A90. http://dx.doi.org/10.1051/0004-6361/202039675.
Texto completo da fonteWagenveld, J. D., A. Saxena, K. J. Duncan, H. J. A. Röttgering e M. Zhang. "Revealing new high-redshift quasar populations through Gaussian mixture model selection". Astronomy & Astrophysics 660 (abril de 2022): A22. http://dx.doi.org/10.1051/0004-6361/202142445.
Texto completo da fonteD’Isanto, A., e K. L. Polsterer. "Photometric redshift estimation via deep learning". Astronomy & Astrophysics 609 (janeiro de 2018): A111. http://dx.doi.org/10.1051/0004-6361/201731326.
Texto completo da fonteDuncan, Kenneth J. "All-purpose, all-sky photometric redshifts for the Legacy Imaging Surveys Data Release 8". Monthly Notices of the Royal Astronomical Society 512, n.º 3 (8 de março de 2022): 3662–83. http://dx.doi.org/10.1093/mnras/stac608.
Texto completo da fonteJang, J. K., Sukyoung K. Yi, Yohan Dubois, Jinsu Rhee, Christophe Pichon, Taysun Kimm, Julien Devriendt et al. "Translators of Galaxy Morphology Indicators between Observation and Simulation". Astrophysical Journal 950, n.º 1 (1 de junho de 2023): 4. http://dx.doi.org/10.3847/1538-4357/accd68.
Texto completo da fonteEscudero, Carlos G., Arianna Cortesi, Favio R. Faifer, Leandro A. Sesto, Analía V. Smith Castelli, Evelyn J. Johnston, Victoria Reynaldi et al. "The complex globular cluster system of the S0 galaxy NGC 4382 in the outskirts of the Virgo Cluster". Monthly Notices of the Royal Astronomical Society 511, n.º 1 (8 de janeiro de 2022): 393–412. http://dx.doi.org/10.1093/mnras/stac021.
Texto completo da fonteTardugno Poleo, Valentina, Steven L. Finkelstein, Gene Leung, Erin Mentuch Cooper, Karl Gebhardt, Daniel J. Farrow, Eric Gawiser et al. "Identifying Active Galactic Nuclei at z ∼ 3 from the HETDEX Survey Using Machine Learning". Astronomical Journal 165, n.º 4 (10 de março de 2023): 153. http://dx.doi.org/10.3847/1538-3881/acba92.
Texto completo da fonteTeses / dissertações sobre o assunto "Photometric gaussian mixtures"
Guerbas, Seif Eddine. "Modélisation adaptée des images omnidirectionnelles pour agrandir le domaine de convergence de l'asservissement visuel virtuel direct". Electronic Thesis or Diss., Amiens, 2022. http://www.theses.fr/2022AMIE0026.
Texto completo da fonteOmnidirectional vision captures a scene in real-time in all directions with a wider field of view than a conventional camera. Within the environment, linking the visual features contained in the camera images to its movements is a central issue for visual servoing. Direct approaches, however, are characterized by a limited range of convergence. The main objective of this dissertation is to significantly extend the area of convergence in the context of virtual visual servoing by representing the omnidirectional image by a Photometric Gaussian Mixtures (PGM). This approach is further extended in the second step to the registration and direct tracking based on 3D models in omnidirectional images. This proposed methodology allows for studying the localization of a mobile robot equipped with a panoramic camera in a 3D urban model. The results show a significant enlargement of the convergence domain for high robustness to large interframe movements, as evidenced by experiments in virtual environments and with real images captured with a mobile robot and a vehicle
Trabalhos de conferências sobre o assunto "Photometric gaussian mixtures"
Crombez, Nathan, Guillaume Caron e El Mustapha Mouaddib. "Photometric Gaussian mixtures based visual servoing". In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015. http://dx.doi.org/10.1109/iros.2015.7354154.
Texto completo da fonte