Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Lefebvre, Sidonie"'
Autor:
Ciocarlan, Alina, Hégarat-Mascle, Sylvie Le, Lefebvre, Sidonie, Woiselle, Arnaud, Barbanson, Clara
Detecting small to tiny targets in infrared images is a challenging task in computer vision, especially when it comes to differentiating these targets from noisy or textured backgrounds. Traditional object detection methods such as YOLO struggle to d
Externí odkaz:
http://arxiv.org/abs/2402.02288
The detection of small objects is a challenging task in computer vision. Conventional object detection methods have difficulty in finding the balance between high detection and low false alarm rates. In the literature, some methods have addressed thi
Externí odkaz:
http://arxiv.org/abs/2303.01363
Publikováno v:
2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 2022, pp. 1322-1328
Deep learning based methods for single-image super-resolution (SR) have drawn a lot of attention lately. In particular, various papers have shown that the learning stage can be performed on a single image, resulting in the so-called internal approach
Externí odkaz:
http://arxiv.org/abs/2302.01648
We propose an auto-encoder architecture for multi-texture synthesis. The approach relies on both a compact encoder accounting for second order neural statistics and a generator incorporating adaptive periodic content. Images are embedded in a compact
Externí odkaz:
http://arxiv.org/abs/2302.01616
Publikováno v:
GRETSI, Sep 2022, Nancy, France
Small target detection is an essential yet challenging task in defense applications, since differentiating low-contrast targets from natural textured and noisy environment remains difficult. To better take into account the contextual information, we
Externí odkaz:
http://arxiv.org/abs/2210.00755
Publikováno v:
In Pattern Recognition June 2024 150
Akademický článek
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A novel approach to perform unsupervised sequential learning for functional data is proposed. Our goal is to extract reference shapes (referred to as templates) from noisy, deformed and censored realizations of curves and images. Our model generalize
Externí odkaz:
http://arxiv.org/abs/1604.00570
Publikováno v:
In Atmospheric Environment 15 March 2021 249
Autor:
Klotz, Emile, Lefebvre, Sidonie, Vedrenne, Nicolas, Musso, Christian, Poulenard, Sylvain, Fusco, Thierry
Publikováno v:
International Journal of Satellite Communications & Networking; Jan2024, Vol. 42 Issue 1, p38-56, 19p