High-efficiency sub-microscale uncertainty measurement method using pattern recognition

Chenyang Zhao, Chi Fai Cheung, Peng Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

57 Citations (Scopus)


This study presents a fast precision measurement method that uses pattern recognition. First, a specific micro-structured surface was designed and manufactured, providing a unique pattern for recognition and matching. Second, a measurement system was proposed based on the algorithms of circle Hough transform (CHT), neural classifier (NC), template matching (TM) and sub-pixel interpolation (SI). Then, a series of experiments were carried out from three aspects: circle detection, length uncertainty, and measurement speed and range. The results showed the correct circle classification percentage was more than 96% and the CHT search accuracy was within a two-pixel level. The length uncertainty test demonstrated the method was able to achieve 90-nm length uncertainty, and a comparison of measurement speeds showed it helped to speed up measurements by a factor of 1000 compared to the original one.

Original languageEnglish
Pages (from-to)503-514
Number of pages12
JournalISA Transactions
Issue numberJune
Publication statusPublished - Jun 2020


  • Image processing
  • Neural network
  • Polar microstructure
  • Precision measurement

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Instrumentation
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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