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Automated defect identification from carrier fringe patterns using Wigner-Ville distribution and a machine learning-based method
- Source :
- Applied optics. 60(15)
- Publication Year :
- 2021
-
Abstract
- The paper presents a method for automated defect identification from fringe patterns. The method relies on computing the fringe signal’s Wigner–Ville distribution followed by a supervised machine learning algorithm. Our machine learning approach enables robust detection of fringe pattern defects of varied shapes and alleviates the limitations associated with thresholding-based techniques that require careful control of the threshold parameter. The potential of the proposed method is demonstrated via numerical simulations to identify different types of defect patterns at various noise levels. In addition, the practical applicability of the method is validated by experimental results.
- Subjects :
- Computer simulation
Noise (signal processing)
business.industry
Computer science
Machine learning
computer.software_genre
Holographic interferometry
01 natural sciences
Signal
Atomic and Molecular Physics, and Optics
010309 optics
Interferometry
Identification (information)
symbols.namesake
Fourier transform
0103 physical sciences
symbols
Speckle imaging
Artificial intelligence
Electrical and Electronic Engineering
business
Engineering (miscellaneous)
computer
Subjects
Details
- ISSN :
- 15394522
- Volume :
- 60
- Issue :
- 15
- Database :
- OpenAIRE
- Journal :
- Applied optics
- Accession number :
- edsair.doi.dedup.....db8b2b0b4322934d4f26dbee5e03ac14