Letteratura scientifica selezionata sul tema "Photometric gaussian mixtures"
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Articoli di riviste sul tema "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. 1 (febbraio 2019): 49–63. http://dx.doi.org/10.1109/tro.2018.2876765.
Hatfield, 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. 4 (11 settembre 2020): 5498–510. http://dx.doi.org/10.1093/mnras/staa2741.
Jones, Daniel M., e Alan F. Heavens. "Gaussian mixture models for blended photometric redshifts". Monthly Notices of the Royal Astronomical Society 490, n. 3 (27 settembre 2019): 3966–86. http://dx.doi.org/10.1093/mnras/stz2687.
Ansari, Zoe, Adriano Agnello e Christa Gall. "Mixture models for photometric redshifts". Astronomy & Astrophysics 650 (giugno 2021): A90. http://dx.doi.org/10.1051/0004-6361/202039675.
Wagenveld, 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 (aprile 2022): A22. http://dx.doi.org/10.1051/0004-6361/202142445.
D’Isanto, A., e K. L. Polsterer. "Photometric redshift estimation via deep learning". Astronomy & Astrophysics 609 (gennaio 2018): A111. http://dx.doi.org/10.1051/0004-6361/201731326.
Duncan, 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. 3 (8 marzo 2022): 3662–83. http://dx.doi.org/10.1093/mnras/stac608.
Jang, 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. 1 (1 giugno 2023): 4. http://dx.doi.org/10.3847/1538-4357/accd68.
Escudero, 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. 1 (8 gennaio 2022): 393–412. http://dx.doi.org/10.1093/mnras/stac021.
Tardugno 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. 4 (10 marzo 2023): 153. http://dx.doi.org/10.3847/1538-3881/acba92.
Tesi sul tema "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.
Omnidirectional 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
Atti di convegni sul tema "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.