Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Ren, Chengfang"'
Due to its all-weather and day-and-night capabilities, Synthetic Aperture Radar imagery is essential for various applications such as disaster management, earth monitoring, change detection and target recognition. However, the scarcity of labeled SAR
Externí odkaz:
http://arxiv.org/abs/2407.00851
Publikováno v:
Proceedings of the 2023 IEEE International Symposium on Circuits and Systems (ISCAS)
Much work has been dedicated to estimating and optimizing workloads in high-performance computing (HPC) and deep learning. However, researchers have typically relied on few metrics to assess the efficiency of those techniques. Most notably, the accur
Externí odkaz:
http://arxiv.org/abs/2310.09554
The detection of multiple targets in an enclosed scene, from its outside, is a challenging topic of research addressed by Through-the-Wall Radar Imaging (TWRI). Traditionally, TWRI methods operate in two steps: first the removal of wall clutter then
Externí odkaz:
http://arxiv.org/abs/2307.12592
Autor:
Barrachina, Jose Agustin, Ren, Chengfang, Vieillard, Gilles, Morisseau, Christele, Ovarlez, Jean-Philippe
This work explains in detail the theory behind Complex-Valued Neural Network (CVNN), including Wirtinger calculus, complex backpropagation, and basic modules such as complex layers, complex activation functions, or complex weight initialization. We a
Externí odkaz:
http://arxiv.org/abs/2302.08286
Autor:
Barrachina, José Agustin, Ren, Chengfang, Morisseau, Christèle, Vieillard, Gilles, Ovarlez, Jean-Philippe
In this paper, we investigated the semantic segmentation of Polarimetric Synthetic Aperture Radar (PolSAR) using Complex-Valued Neural Network (CVNN). Although the coherency matrix is more widely used as the input of CVNN, the Pauli vector has recent
Externí odkaz:
http://arxiv.org/abs/2210.17419
In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as abnormal patterns that deviate from their surroundings but without any prior knowledge of their cha
Externí odkaz:
http://arxiv.org/abs/2210.16038
This paper studies the statistical model of the non-centered mixture of scaled Gaussian distributions (NC-MSG). Using the Fisher-Rao information geometry associated to this distribution, we derive a Riemannian gradient descent algorithm. This algorit
Externí odkaz:
http://arxiv.org/abs/2209.03315
This paper proposes new algorithms for the metric learning problem. We start by noticing that several classical metric learning formulations from the literature can be viewed as modified covariance matrix estimation problems. Leveraging this point of
Externí odkaz:
http://arxiv.org/abs/2202.11550
Publikováno v:
In Signal Processing January 2024 214
Publikováno v:
IEEE Signal Processing Letters, 26 2 (2019) 367-371
The joint estimation of means and scatter matrices is often a core problem in multivariate analysis. In order to overcome robustness issues, such as outliers from Gaussian assumption, M-estimators are now preferred to the traditional sample mean and
Externí odkaz:
http://arxiv.org/abs/1901.02640