NDLI: Optimized Image Recognition Based Sewing Pattern File Auto-Generating Method for Intelligent Industrial Equipments

Content Provider	IEEE Xplore Digital Library
Author	Erfeng Su Kailong Zhang Xingshe Zhou
Copyright Year	2012
Abstract	The development of embedded system (ES) promotes the trend that the numerical-control equipment becomes more networked and intelligent. ES is the kernal of network and intelligence, yet the image data with contained information is the key point to realize high antomatization and intelligence of ES. The overall automatization level of industrial equipment becomes higher than before. However, in some basic industries, such as the sewing industry, the automatization is backward. This paper analyzes the working mechanism of current pattern-designing software and concludes that the system input is complicated, and the usability and automatization is low. To deal with the problem, the paper proposes a sewing pattern generating method based on optimized image recognition. This method is data-driven, which includes image calibration, edge detecting and curve fitting to simplify the system input. A new algorithm is designed to transfer the system input from vectors to scalars, or to accept both of them, which improves the system's scalability and usability. Corresponding software is developed and the results show that it is more reliable, and it can satisfy the industrial requirements in an acceptable degree.
Starting Page	379
Ending Page	384
File Size	592191
Page Count	6
File Format	PDF
ISBN	9781467315364
DOI	10.1109/ICIS.2012.80
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image edge detection Vectors Data structures Software Cameras Curve fitting Lenses automatic generation embedded system intelligent equipment pattern recognition edge detecting curve fitting
Content Type	Text
Resource Type	Article

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