Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Miguel Angel Veganzones"'
Autor:
Miguel Angel Veganzones, Jocelyn Chanussot, Guillaume Tochon, Thierry Géraud, Mauro Dalla Mura
Publikováno v:
Pattern Recognition
Pattern Recognition, Elsevier, 2019, 95, pp.162-172. ⟨10.1016/j.patcog.2019.05.029⟩
Pattern Recognition, Elsevier, 2019, 95, pp.162-172. ⟨10.1016/j.patcog.2019.05.029⟩
International audience; Hierarchical data representations are powerful tools to analyze images and have found numerous applications in image processing. When it comes to multimodal images however, the fusion of multiple hierarchies remains an open qu
Autor:
Gerardo González-Seco, Eugenio Martínez-Cámara, Francisco López Herrera, Miguel Angel Veganzones, M. Victoria Luzón, Nuria Rodríguez-Barroso, José Antonio Ruiz-Millán, Daniel Jiménez-López, Goran Stipcich
The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit those requirements. Federated learning has the ambition to protect data privacy thro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb705126fbf34b7321b18468c1a84f86
Autor:
Lucas Drumetz, Jocelyn Chanussot, Giorgio Licciardi, Mauro Dalla Mura, Ruben Marrero Gomez, Miguel Angel Veganzones, Christian Jutten, Guillaume Tochon
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2016, 54 (7), pp.4063-4078. ⟨10.1109/TGRS.2016.2536480⟩
IEEE Transactions on Geoscience and Remote Sensing, 2016, 54 (7), pp.4063-4078. ⟨10.1109/TGRS.2016.2536480⟩
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2016, 54 (7), pp.4063-4078. ⟨10.1109/TGRS.2016.2536480⟩
IEEE Transactions on Geoscience and Remote Sensing, 2016, 54 (7), pp.4063-4078. ⟨10.1109/TGRS.2016.2536480⟩
International audience; The Intrinsic Dimensionality (ID) of multivariate data is a very important concept in spectral unmixing of hyperspectral images. A good estimation of the ID is crucial for a correct retrieval of the number of endmembers (the s
Autor:
Guillaume Tochon, Jocelyn Chanussot, M. Dalla Mura, Miguel Angel Veganzones, Philippe Salembier, Silvia Valero
Publikováno v:
Reference Module in Earth Systems and Environmental Sciences
Reference Module in Earth Systems and Environmental Sciences, 143, Elsevier, pp.77-107, 2018, ⟨10.1016/B978-0-12-409548-9.10340-9⟩
Reference Module in Earth Systems and Environmental Sciences, 143, Elsevier, pp.77-107, 2018, ⟨10.1016/B978-0-12-409548-9.10340-9⟩
The latest developments in sensor design for remote sensing and Earth observation purposes are leading to images always more complex to analyze. Low-level pixel-based processing is becoming unadapted to efficiently handle the wealth of information th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18fd363ef3c1a513b2b12def009a95a8
https://hal.archives-ouvertes.fr/hal-01961382
https://hal.archives-ouvertes.fr/hal-01961382
Autor:
Guillaume Tochon, Christian Jutten, Lucas Drumetz, Miguel Angel Veganzones, Jocelyn Chanussot
Publikováno v:
ICASSP 2017-Proceedings
ICASSP 2017-IEEE International Conference on Acoustics, Speech and Signal Processing
ICASSP 2017-IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Mar 2017, New Orleans, United States. pp.6190-6194, ⟨10.1109/ICASSP.2017.7953346⟩
ICASSP
ICASSP 2017-IEEE International Conference on Acoustics, Speech and Signal Processing
ICASSP 2017-IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Mar 2017, New Orleans, United States. pp.6190-6194, ⟨10.1109/ICASSP.2017.7953346⟩
ICASSP
International audience; Local Spectral Unmixing (LSU) methods perform the unmixing of hyperspectral data locally in regions of the image. The endmembers and their abundances in each pixel are extracted region-wise, instead of globally to mitigate spe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::232ddc07b5269559c002f9fbc087a068
https://hal.archives-ouvertes.fr/hal-01581521/file/drumetz_ICASSP2017.pdf
https://hal.archives-ouvertes.fr/hal-01581521/file/drumetz_ICASSP2017.pdf
Autor:
Jocelyn Chanussot, J.-P. Dedieu, Pascal Sirguey, Marie Dumont, Miguel Angel Veganzones, T. Masson, M. Dalla Mura
Publikováno v:
WHISPERS
Spectral Unmixing is the most recent method used to recover the Snow Cover Fraction of an area, but it depends particularly on the relevance of the set of endmembers. This communication investigates different strategies for defining set of endmembers
Autor:
Miguel Angel Veganzones, Jeremy E. Cohen, Pierre Comon, Rodrigo Cabral Farias, Jocelyn Chanussot
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2016, 54 (5), pp.2577-2588. ⟨10.1109/TGRS.2015.2503737⟩
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2016, 54 (5), pp.2577-2588. ⟨10.1109/TGRS.2015.2503737⟩
IEEE Transactions on Geoscience and Remote Sensing, 2016, 54 (5), pp.2577-2588. ⟨10.1109/TGRS.2015.2503737⟩
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2016, 54 (5), pp.2577-2588. ⟨10.1109/TGRS.2015.2503737⟩
International audience; New hyperspectral missions will collect huge amounts of hyperspectral data. Besides, it is possible now to acquire time series and multiangular hyperspectral images. The process and analysis of these big data collections will
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce6abd7ac9151e105640a00d2aa3e56b
https://hal.science/hal-01134470v2/document
https://hal.science/hal-01134470v2/document
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2016, 9 (2), pp.720-731. ⟨10.1109/JSTARS.2015.2453014⟩
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2016, 9 (2), pp.720-731. ⟨10.1109/JSTARS.2015.2453014⟩
International audience; Anomaly detection methods are devoted to target detection schemes in which no a priori information about the spectra of the targets of interest is available. This paper reviews classical anomaly detection schemes such as the w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9326d8ffdf8195a450ab1f95a432f1d8
https://hal.archives-ouvertes.fr/hal-01377668/document
https://hal.archives-ouvertes.fr/hal-01377668/document
Autor:
Simon Henrot, Christian Jutten, Jocelyn Chanussot, Ronald Phlypo, Lucas Drumetz, Miguel-Angel Veganzones
Publikováno v:
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2016, IEEE Transactions on Image Processing, 25 (8), pp.3890-3905. ⟨10.1109/TIP.2016.2579259⟩
IEEE Transactions on Image Processing, 2016, IEEE Transactions on Image Processing, 25 (8), pp.3890-3905. ⟨10.1109/TIP.2016.2579259⟩
IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2016, IEEE Transactions on Image Processing, 25 (8), pp.3890-3905. ⟨10.1109/TIP.2016.2579259⟩
IEEE Transactions on Image Processing, 2016, IEEE Transactions on Image Processing, 25 (8), pp.3890-3905. ⟨10.1109/TIP.2016.2579259⟩
International audience; Spectral Unmixing is one of the main research topics in hyperspectral imaging. It can be formulated as a source separation problem whose goal is to recover the spectral signatures of the materials present in the observed scene
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d364706a225b681195057521c0a28cbb
https://hal.archives-ouvertes.fr/hal-01336279
https://hal.archives-ouvertes.fr/hal-01336279
Autor:
Miguel Angel Veganzones, Manuel Graña
Publikováno v:
Pattern Recognition. 45:3472-3489
We propose a specific content-based image retrieval (CBIR) system for hyperspectral images exploiting its rich spectral information. The CBIR image features are the endmember signatures obtained from the image data by endmember induction algorithms (