A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry

Two-dimensional windowed Fourier transform for fringe pattern analysis: Principles, applications and implementations

Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring

Fringe patterns in optical metrology systems need to be demodulated to yield the desired parameters. Time-frequency analysis is a useful concept for fringe demodulation, and a windowed Fourier transform is chosen for the determination of phase and phase derivative. Two approaches are developed: the first is based on the concept of filtering the fringe patterns, and the second is based on the best match between the fringe pattern and computer-generated windowed exponential elements. I focus on the extraction of phase and phase derivatives from either phase-shifted fringe patterns or a single carrier fringe pattern. Principles as well as examples are given to show the effectiveness of the proposed methods.

Windowed Fourier transform for fringe pattern analysis

Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals

As a practical and effective tool for quantitative in-plane deformation measurement of a planar object surface, two-dimensional digital image correlation (2D DIC) is now widely accepted and commonly used in the field of experimental mechanics. It directly provides full-field displacements to sub-pixel accuracy and full-field strains by comparing the digital images of a test object surface acquired before and after deformation. In this review, methodologies of the 2D DIC technique for displacement field measurement and strain field estimation are systematically reviewed and discussed. Detailed analyses of the measurement accuracy considering the influences of both experimental conditions and algorithm details are provided. Measures for achieving high accuracy deformation measurement using the 2D DIC technique are also recommended. Since microscale and nanoscale deformation measurement can easily be realized by combining the 2D DIC technique with high-spatial-resolution microscopes, the 2D DIC technique should find more applications in broad areas.

https://iopscience.iop.org/article/10.1088/0957-0233/20/6/062001/pdf

Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review

Abstract With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined. 

/pdf/deep-learning-in-optical-metrology-a-review-2ggtgtrp.pdf

Deep learning in optical metrology: a review

Fringe pattern denoising based on deep learning

/pdf/deep-learning-in-optical-metrology-a-review-1etdbnzu.pdf

We optimize an image sensor-based indoor visible light positioning (VLP) system by improving the positioning algorithm. Specifically, we derive a close-form expression to determine the receiver’s position and orientation using the singular value decomposition (SVD) technique, which speeds up the positioning process and enhances the robustness. Simulation results show that the proposed SVD-based noniterative positioning algorithm is 50–80 times faster than the conventional iterative Levenberg–Marquardt-based algorithm and avoids the possible failures caused by the bad initial guesses. Meanwhile, we theoretically investigate the VLP system by deriving the Cramer–Rao lower bound and the root mean square error bound as the positioning accuracy limit and study the impact of system parameters on the positioning error. Finally, we experimentally evaluate the performance of the improved VLP system. It achieves highly robust and fast 3-D positioning with centimeter-level accuracy.

Kemao Qian

Papers

Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review

Deep learning in optical metrology: a review

Fringe pattern denoising based on deep learning

Deep learning in optical metrology: a review

Image Sensor Based Visible Light Positioning System With Improved Positioning Algorithm