Zobrazeno 1 - 10 of 10 pro vyhledávání: '"Nikolay M. Sirakov"'
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Akademický článek
Image Classification Based on Sparse Representation in the Quaternion Wavelet Domain
Autor: Long H. Ngo, Nikolay M. Sirakov, Marie Luong, Emmanuel Viennet, Thuong Le-Tien
Publikováno v: IEEE Access, Vol 10, Pp 31548-31560 (2022)
In this study, we propose a novel sparse representation learning method in the Quaternion Wavelet (QW) domain for multi-class image classification. The proposed method takes advantages from: i) the QW decomposition, which promotes sparsity and provid
Externí odkaz: https://doaj.org/article/b2f01025d5974959a30d9fa1fe83070f
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2
On the Usefulness of the Vector Field Singular Points Shapes for Classification
Autor: Oluwaseyi Igbasanmi, Nikolay M. Sirakov
The objects’ features play significant role in the machine learning (ML) classification. The present paper proofs and validates that the shapes of vector field (VF) singular points (SPs) embedded into image objects may improve classification accura
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6db3135a56128114057171e9595afe2a
https://doi.org/10.21203/rs.3.rs-2862010/v1
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4
Clifford Algebra Multivectors And Kernels For Melanoma Classification
Autor: Mutlu Akar, Nikolay M. Sirakov, Mutlu Mete
Melanoma is a deadly skin disease. Availability of digital skin lesion datasets ease the exploration of ample classification studies. Both theoretical and heuristics improvements are achieved thanks to these new datasets. Being one of many high-level
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7406e6c673637e71b8df1a68055fad2d
https://aperta.ulakbim.gov.tr/record/235916
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7
A Quaternary Classifier for the Clinical Evaluation of Pigmented Skin Lesions
Autor: Alan Menter, Gregory A. Hosler, Lauren Dickson, Nikolay M. Sirakov, John W. Griffin, Mutlu Mete, Jillian Frieder
Publikováno v: BIBE
This study reports results of a pilot study, in which pigmented skin lesions are automatically classified into four classes: benign, dysplastic nevus with mild atypia, dysplastic nevus with severe atypia, and melanoma. The pilot study enrolled subjec
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9cfabfa0996a35656aded8681232e64d
https://doi.org/10.1109/bibe50027.2020.00125
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8
Classification with Stochastic Learning Methods and Convolutional Neural Networks
Autor: Reginald Bolman, Nikolay M. Sirakov, Natasha M. Astudillo
Publikováno v: SN Computer Science. 1
The present study illustrates a convolution neural network designed to classify skin lesion images to benign and malignant melanoma. The convolutional neural network consists of 4 convolution layers, rectified linear activation function and a softmax
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::82bd4fa024da7cade1f648daaecff073
https://doi.org/10.1007/s42979-020-00126-x
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9
Image Database Query Using Shape-Based Boundary Descriptors
Autor: James W. Swift, Nikolay M. Sirakov, P.A. Mlsna
Publikováno v: Intelligent Systems Design and Applications ISBN: 9783540404262
Interest in the potential of digital images has increased enormously over the last few years. The internet collection of images has been estimated well in excess of 30 × 106. Examples of large subject-specific image collections include medical and e
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5f334cec1e80efbfce31e09a0be2eedd
https://doi.org/10.1007/978-3-540-44999-7_36
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10
Classification using sparse representation and applications to skin lesion diagnosis
Autor: Long H. Ngo
Publikováno v: Image Processing [eess.IV]. Université Sorbonne Paris Nord, 2021. English
HAL
To be filled; La classification d'images est une discipline majeure en traitement d’images et en intelligence artificielle. La classification est d'une importance fondamentale pour qu'un système intelligent puisse exploiter et gérer efficacement
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::c8c920a8b65f1b78f18e8b256946ba25
https://hal.archives-ouvertes.fr/tel-03324943
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