Source: ISSN: 1959-0318 ; Innovation and Research in BioMedical engineering ; https://hal.science/hal-01325355 ; Innovation and Research in BioMedical engineering, 2016, 37 (3), pp.131-143.

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Dissertation/ Thesis

Optimization of Fire Protection Systems Using Machine Learning : Application to Intumescent Systems ; Optimisation des systèmes de protection incendie par machine learning : Application aux systèmes intumescents

Authors : Verret, Éric;

Subjects: Bayesian optimization; Machine learning; Flame retardant formulation

Source: https://theses.hal.science/tel-04976497 ; Material chemistry. Centrale Lille Institut, 2024. English. ⟨NNT : 2024CLIL0033⟩.

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Academic Journal

Impact of consensus contours from multiple PET-segmentation methods on the accuracy of functionalvolume delineation

Authors : Schaefer, A.; Vermandel, Maximilien; Baillet, C. ...

Subjects: PET image segmentation; consensus algorithms; STAPLE

Source: ISSN: 1619-7070.

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Academic Journal

MSFA-Net: A convolutional neural network based on multispectral filter arrays for texture feature extraction

Authors : Amziane, Anis; Losson, Olivier; Mathon, Benjamin ...

Subjects: Multispectral Imaging; deep learning; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing

Source: ISSN: 0167-8655 ; Pattern Recognition Letters ; https://hal.science/hal-04018052 ; Pattern Recognition Letters, 2023, 168, pp.93-99. ⟨10.1016/j.patrec.2023.03.004⟩.

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Academic Journal

CristalX: Facilitating simulations for experimentally obtained grain-based microstructures

Authors : Csati, Zoltan; Witz, Jean-Francois; Magnier, Vincent ...

Subjects: Meshing; Microstructure; Segmentation

Source: ISSN: 2352-7110 ; SoftwareX ; https://hal.science/hal-03409334 ; SoftwareX, 2021, 14, pp.100669. ⟨10.1016/j.softx.2021.100669⟩.

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Report

Frugal Gaussian clustering of huge imbalanced datasets through a bin-marginal approach

Authors : Antonazzo, Filippo; Biernacki, Christophe; Keribin, Christine ...

Subjects: Imbalanced clustering; Large size data; Gaussian mixture models

Source: https://hal.science/hal-03485364 ; 2021.

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Dissertation/ Thesis

Unsupervised learning of huge data sets with limited computed resources ; Apprentissage non supervisé pour données extrêmement volumineuses en situation de ressources informatiques arbitrairement limitées

Authors : Antonazzo, Filippo;

Subjects: Unsupervised learning; Mixture models; EM algorithm

Source: https://theses.hal.science/tel-03846222 ; Data Structures and Algorithms [cs.DS]. Université de Lille, 2022. English. ⟨NNT : 2022ULILB015⟩.

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نتائج البحث

فلترة النتائج

Fuzzy Color Aura Matrices for Texture Image Segmentation

Constrained feature selection for semisupervised color-texture image segmentation using spectral clustering

A robust method for MR image segmentation and multiple scleroses detection

On image segmentation methods applied to glioblastoma: state of art and new trends

Optimization of Fire Protection Systems Using Machine Learning : Application to Intumescent Systems ; Optimisation des systèmes de protection incendie par machine learning : Application aux systèmes intumescents

Impact of consensus contours from multiple PET-segmentation methods on the accuracy of functionalvolume delineation

MSFA-Net: A convolutional neural network based on multispectral filter arrays for texture feature extraction

CristalX: Facilitating simulations for experimentally obtained grain-based microstructures

Frugal Gaussian clustering of huge imbalanced datasets through a bin-marginal approach

Unsupervised learning of huge data sets with limited computed resources ; Apprentissage non supervisé pour données extrêmement volumineuses en situation de ressources informatiques arbitrairement limitées

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