Optimal eigenvectors of spectral datasets : sequential selection from one set vs a collection from two sets / Morteza Maali Amiri in COLORATION TECHNOLOGY, Vol. 132, N° 6 (12/2016)  

[article]  inCOLORATION TECHNOLOGY > Vol. 132, N° 6 (12/2016) . - p. 441 448 Titre : Optimal eigenvectors of spectral datasets : sequential selection from one set vs a collection from two sets Type de document : texte imprimé Auteurs : Morteza Maali Amiri, Auteur ; Seyed Hossein Amirshahi, Auteur Année de publication : 2016 Article en page(s) : p. 441 448 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Analyse des données Analyse vectorielle Spectroscopie de réflectance Index. décimale : 667.3 Teinture et impression des tissus Résumé : In this paper, the influence of spectral datasets and the method of selection of the corresponding feature vectors on the compression and reconstruction of data is scrutinised. To fulfil this aim, two different sets of reflectance data with the least spectral similarity are selected from different sets of spectral databases and the most optimal eigenvectors are chosen using different strategies. Six and 12 arrangements of eigenvectors obtained from different individual or combined databases are then used for the compression of reflectance spectra of learning sets, as well as those that have not been used in extraction of eigenvectors. The validity of the desired reduced subspaces is assessed by computing the spectral errors between the actual and the reconstructed spectra of samples of learning sets. Moreover, the efficiencies of designed compressed subspaces are evaluated through the numbers of out-of-range reconstructed spectra, as well as the spectral and colorimetric deviations between the actual and compressed-reconstructed reflectance spectra of samples of datasets that were not employed in learning sequence. The results show that in the restricted subspaces, i.e. six-dimensional subspace, the most effective results are achieved when the reduced subspace is created from a collection of two separate sets of eigenvectors of two different datasets with the maximum degree of dissimilarity, and the reduced spaces that have been made from six eigenvectors of individual datasets lead to higher errors. Note de contenu : - EXTRACTION AND SELECTION OF AN OPTIMAL EIGENVECTOR SET - EXPERIMENT AND PROCEDURE - RESULTS AND DISCUSSION : Compression-reconstruction of Munsell and textile databases - Compression-reconstruction of other datasets DOI : 10.1111/cote.12236 En ligne : https://drive.google.com/file/d/12YHaAeS72nA2RyXGU06tlAD2AeX_mJtu/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=27281 [article]

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