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Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array

Autor: Aschersleben, J., Vecchi, M., Wilkinson, M. H. F., Peletier, R. F.

The Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at groun

Externí odkaz: http://arxiv.org/abs/2302.11876

Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data

Autor: Aschersleben, J., Peletier, R. F., Vecchi, M., Wilkinson, M. H. F.

The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, offering 5 - 10 x better flux sensitivity than current generation gamma

Externí odkaz: http://arxiv.org/abs/2108.00834

Fast Pattern Spectra using Tree Representation of the Image for Patch Retrieval

Autor: Jérôme Moré, Behzad Mirmahboub, David Youssefi, François Merciol, Sébastien Lefèvre, Alain Giros

Publikováno v: Discrete Geometry and Mathematical Morphology 2021
DGMM 2021-IAPR International Conference on Discrete Geometry and Mathematical Morphology
DGMM 2021-IAPR International Conference on Discrete Geometry and Mathematical Morphology, May 2021, Uppsala / Virtual, Sweden. pp.107-119, ⟨10.1007/978-3-030-76657-3_7⟩
Lecture Notes in Computer Science ISBN: 9783030766566
DGMM
Discrete Geometry and Mathematical Morphology 2021, pp.107-119, 2021, ⟨10.1007/978-3-030-76657-3_7⟩

International audience; We extend the notion of content based image retrieval to patch retrieval where the goal is to find the similar patches to a query patch in a large image. Naive searching for similar patches by sequentially computing and compar

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7b59e72f3f4756fcb622888043b8ec9
https://hal.science/hal-03354933/file/dgmm2021behzad.pdf

Retrieval of Remote Sensing Images with Pattern Spectra Descriptors

Autor: Petra Bosilj, Erchan Aptoula, Sébastien Lefèvre, Ewa Kijak

Publikováno v: ISPRS International Journal of Geo-Information, Vol 5, Iss 12, p 228 (2016)

The rapidly increasing volume of visual Earth Observation data calls for effective content based image retrieval solutions, specifically tailored for their high spatial resolution and heterogeneous content. In this paper, we address this issue with a

Externí odkaz: https://doaj.org/article/85fdf6ec34994006824d7b59e9164d52