Optimal Learning Samples for Two-Constant Kubelka-Munk Theory to Match the Color of Pre-colored Fibe...
Optimal Learning Samples for Two-Constant Kubelka-Munk Theory to Match the Color of Pre-colored Fiber Blends
About this item
Full title
Author / Creator
Li, Junfeng , Xie, Dehong , Li, Miaoxin , Liu, Shiwei and Wei, Chun’Ao
Publisher
Lausanne: Frontiers Research Foundation
Journal title
Language
English
Formats
Publication information
Publisher
Lausanne: Frontiers Research Foundation
Subjects
More information
Scope and Contents
Contents
Due to the dyeing process, learning samples used for color prediction of pre-colored fiber blends should be re-prepared once the batches of the fiber change. The preparation of the sample is time-consuming and leads to manpower and material waste. The two-constant Kubelka-Munk theory is selected in this article to investigate the feasibility to min...
Alternative Titles
Full title
Optimal Learning Samples for Two-Constant Kubelka-Munk Theory to Match the Color of Pre-colored Fiber Blends
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2682963280
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2682963280
Other Identifiers
ISSN
1662-453X,1662-4548
E-ISSN
1662-453X
DOI
10.3389/fnins.2022.945454
