The application of image analysis technology in the extraction of human body feature parameters

With the improvement of living standards, personalized clothing customization has become a trend of people’s apparel demand. The key factor in personalized clothing customization is a three-dimensional human modeling. With the development of image analysis technology, it is possible to use image ana...

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Vydáno v:EURASIP journal on image and video processing Ročník 2018; číslo 1; s. 1 - 10
Hlavní autoři: Wei, Pengcheng, Jiang, Jiao, Li, Li
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 30.10.2018
Springer Nature B.V
SpringerOpen
Témata:
Back propagation
Biometrics
BP neural network
Computer-aided design (CAD)
Engineering
Feature extraction
Human characteristics
Image analysis
Image Processing and Computer Vision
Neural networks
Parameters
Pattern Recognition
Signal,Image and Speech Processing
Technology assessment
Three dimensional models
Visual Information Learning and Analytics on Cross-Media Big Data
Feature extraction
Image analysis
BP neural network
Human characteristics
Computer-aided design (CAD)
ISSN:1687-5281, 1687-5176, 1687-5281
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Popis
Shrnutí:With the improvement of living standards, personalized clothing customization has become a trend of people’s apparel demand. The key factor in personalized clothing customization is a three-dimensional human modeling. With the development of image analysis technology, it is possible to use image analysis technology to extract human characteristics. In this paper, two-dimensional human feature regions and characteristic parameter extraction methods of images are used. The backpropagation neural network (BP neural network) is used to curve the three-dimensional human characteristics, and the neck, chest, waist, and buttocks of 22 subjects are verified. The results show that the use of this method can well achieve the extraction of human characteristic parameters.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1687-5281
1687-5176
1687-5281
DOI:10.1186/s13640-018-0338-y