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Optimized clustering method for spectral reflectance recovery

An optimized method based on dynamic partitional clustering was proposed for the recovery of spectral reflectance from camera response values. The proposed method produced dynamic clustering subspaces using a combination of dynamic and static clustering, which determined each testing sample as a pri...

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Detalles Bibliográficos
Autores principales: Xiong, Yifan, Wu, Guangyuan, Li, Xiaozhou, Wang, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9726731/
https://www.ncbi.nlm.nih.gov/pubmed/36506952
http://dx.doi.org/10.3389/fpsyg.2022.1051286
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author Xiong, Yifan
Wu, Guangyuan
Li, Xiaozhou
Wang, Xin
author_facet Xiong, Yifan
Wu, Guangyuan
Li, Xiaozhou
Wang, Xin
author_sort Xiong, Yifan
collection PubMed
description An optimized method based on dynamic partitional clustering was proposed for the recovery of spectral reflectance from camera response values. The proposed method produced dynamic clustering subspaces using a combination of dynamic and static clustering, which determined each testing sample as a priori clustering center to obtain the clustering subspace by competition. The Euclidean distance weighted and polynomial expansion models in the clustering subspace were adaptively applied to improve the accuracy of spectral recovery. The experimental results demonstrated that the proposed method outperformed existing methods in spectral and colorimetric accuracy and presented the effectiveness and robustness of spectral recovery accuracy under different color spaces.
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spelling pubmed-97267312022-12-08 Optimized clustering method for spectral reflectance recovery Xiong, Yifan Wu, Guangyuan Li, Xiaozhou Wang, Xin Front Psychol Psychology An optimized method based on dynamic partitional clustering was proposed for the recovery of spectral reflectance from camera response values. The proposed method produced dynamic clustering subspaces using a combination of dynamic and static clustering, which determined each testing sample as a priori clustering center to obtain the clustering subspace by competition. The Euclidean distance weighted and polynomial expansion models in the clustering subspace were adaptively applied to improve the accuracy of spectral recovery. The experimental results demonstrated that the proposed method outperformed existing methods in spectral and colorimetric accuracy and presented the effectiveness and robustness of spectral recovery accuracy under different color spaces. Frontiers Media S.A. 2022-11-23 /pmc/articles/PMC9726731/ /pubmed/36506952 http://dx.doi.org/10.3389/fpsyg.2022.1051286 Text en Copyright © 2022 Xiong, Wu, Li and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Xiong, Yifan
Wu, Guangyuan
Li, Xiaozhou
Wang, Xin
Optimized clustering method for spectral reflectance recovery
title Optimized clustering method for spectral reflectance recovery
title_full Optimized clustering method for spectral reflectance recovery
title_fullStr Optimized clustering method for spectral reflectance recovery
title_full_unstemmed Optimized clustering method for spectral reflectance recovery
title_short Optimized clustering method for spectral reflectance recovery
title_sort optimized clustering method for spectral reflectance recovery
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9726731/
https://www.ncbi.nlm.nih.gov/pubmed/36506952
http://dx.doi.org/10.3389/fpsyg.2022.1051286
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