Automatic color pattern recognition of multispectral printed fabric images

Printed fabrics often have rich colors and variable patterns in different sizes and shapes, which make it difficult to achieve accurate pattern recognition and color measurement using traditional spectrophotometers and digital cameras. This paper develops a grid-based density peaks clustering (GDPC)...

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Vydané v:Journal of intelligent manufacturing Ročník 34; číslo 6; s. 2747 - 2763
Hlavní autori: Zhang, Jie, Yao, Pengpeng, Wu, Hochung, Xin, John H.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.08.2023
Springer Nature B.V
Predmet:
Advanced manufacturing technologies
Algorithms
Business and Management
Clustering
Color imagery
Color measurement
Computing time
Control
Digital cameras
Fabrics
Image processing
Machines
Manufacturing
Mathematical analysis
Mechatronics
Pattern recognition
Pixels
Principal components analysis
Processes
Production
Robotics
Spectrophotometers
Grid-based density peaks clustering algorithm
Pattern recognition
Printed fabrics
Multispectral imaging method
ISSN:0956-5515, 1572-8145
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Popis
Shrnutí:Printed fabrics often have rich colors and variable patterns in different sizes and shapes, which make it difficult to achieve accurate pattern recognition and color measurement using traditional spectrophotometers and digital cameras. This paper develops a grid-based density peaks clustering (GDPC) algorithm to automatically recognize patterns and extract colors of multispectral images of printed fabrics. The multispectral images captured by a self-developed multispectral imaging system is firstly converted into color images in CIELAB color space and three principal components are calculated by applying principal component analysis to reduce the dimensions of the multispectral images. During the multispectral image processing, the noise pixels are removed by calculating the local stability of each pixel, and then the remaining stable pixels are clustered using proposed GDPC algorithm based on three CIELAB color channels and three principal components. Compared with widely-used color clustering algorithms, the proposed GDPC algorithm can recognize the color patterns from more intricate multispectral printed fabric images with higher accuracy and less computational time.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-022-01947-8