An Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital Image Processing and Neural Networks
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Issue Date
2023-01-01Keywords
Color differenceFabric color
Illuminants
Metamerism
Textile image processing
Color Difference Measurement
Fabric Selection
Illuminant Analysis
Metamerism Reduction
Enclosure Design
RGB to CIELAB Conversion
Delta E (CMC) Equation
Neural Network
Colorimeter Validation
Homogeneous Color Identification
Metadata
Show full item recordJournal
Communications in Computer and Information ScienceDOI
10.1007/978-3-031-24985-3_14Additional Links
https://link.springer.com/chapter/10.1007/978-3-031-24985-3_14Abstract
This work proposes an electronic equipment aimed at measuring the color difference between fabrics for a homogeneous selection of these. The proposed method analyzes tissues with different illuminants so that, by taking measurements with a camera, the impact of effects such as metamerism can be reduced, resulting in a more accurate method that represents reality. For this, an enclosure was designed and built to allow constant lighting conditions and spotlights with standard lighting were installed. On the software side, the camera was manually configured to ensure persistent measurements over time and the colors obtained from the RGB color space were converted to CIELAB. For the calculation of the color difference, the Delta E (CMC) color difference equation was used because this is the standard used in the textile industry. Finally, a neural network was trained to estimate the color difference between the fabrics. To validate the results, the measurements obtained with the proposed equipment were compared with those obtained using a colorimeter to measure the colors of the fabrics and calculate whether their color difference is significant. The developed equipment achieved an accuracy of 95% in the correct identification of fabrics that are homogenous in color.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/embargoedAccessLanguage
engISSN
18650929EISSN
18650937ae974a485f413a2113503eed53cd6c53
10.1007/978-3-031-24985-3_14
Scopus Count
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