1. Automated defect identification from carrier fringe patterns using Wigner-Ville distribution and a machine learning-based method
- Author
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Sreeprasad Ajithaprasad, Aditya Madipadaga, Rajshekhar Gannavarpu, and Ankur Vishnoi
- 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 - 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.
- Published
- 2021