Mr. Wei Yin Profile

Wei Yin

at Nanjing Univ. of Science and Technology

SPIE Involvement:

Author

Publications (24)

Proceedings Article | 27 September 2024 Paper

Fast, wide-field-of-view, and accurate 3D imaging using reference-phase-based number-theoretical temporal phase unwrapping

Long Yin, Wei Yin

Proceedings Volume 13282, 132820A (2024) https://doi.org/10.1117/12.3044724

KEYWORDS: Phase unwrapping, Stereoscopy, 3D metrology, Fringe analysis, Imaging systems, Phase shifts, Stereo vision systems, Phase recovery, Phase measurement

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SPIE Journal Paper | 17 May 2023 Open Access

Fringe-pattern analysis with ensemble deep learning

Shijie Feng, Yile Xiao, Wei Yin, Yan Hu, Yixuan Li, Chao Zuo, Qian Chen

APN, Vol. 2, Issue 03, 036010, (May 2023) https://doi.org/10.1117/12.10.1117/1.APN.2.3.036010

KEYWORDS: Education and training, Data modeling, Deep learning, Optical metrology, Fringe analysis, Visual process modeling, Feature extraction, Photonics, Performance modeling, Error analysis

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Proceedings Article | 21 December 2022 Poster

Fast 3D measurement based on fringe projection profilometry and deep learning

Yuxuan Che, Wei Yin

Proceedings Volume 12319, 1231915 (2022) https://doi.org/10.1117/12.2642326

KEYWORDS: Fringe analysis, Network security, Optical metrology, Phase retrieval, Mobile devices, Holograms

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Proceedings Article | 19 December 2022 Poster + Paper

High-precision 3D sensors based on multiple infrared speckle projection modules

Mingyu Li, Wei Yin

Proceedings Volume 12319, 1231916 (2022) https://doi.org/10.1117/12.2642327

KEYWORDS: Speckle, 3D metrology, 3D modeling, Infrared radiation, Sensors, Infrared sensors, Image information entropy, 3D image reconstruction, Digital filtering, Detection and tracking algorithms

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Proceedings Article | 19 December 2022 Poster + Paper

The defect detection method for automobile surfaces based on a lighting system with light fields

Han Yu, Wei Yin

Proceedings Volume 12319, 1231918 (2022) https://doi.org/10.1117/12.2642331

KEYWORDS: Defect detection, Light sources and illumination, Image fusion, Detection and tracking algorithms, Reflectivity, Machine vision, Optical inspection

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Proceedings Article | 19 December 2022 Poster + Paper

A stereo camera self-calibration method using DIC-based planar feature matching

Yifan Ji, Wei Yin

Proceedings Volume 12319, 1231917 (2022) https://doi.org/10.1117/12.2642330

KEYWORDS: Calibration, Cameras, Stereoscopic cameras, 3D metrology, Speckle, 3D modeling, Distortion, Optimization (mathematics), Digital image correlation, Detection and tracking algorithms

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Proceedings Article | 19 December 2022 Poster + Paper

An efficient speckle matching network based on deep learning

Hang Zhao, Wei Yin

Proceedings Volume 12319, 1231914 (2022) https://doi.org/10.1117/12.2642325

KEYWORDS: Speckle, Feature extraction, 3D metrology, Speckle pattern, Structured light, Stereoscopic cameras, Reconstruction algorithms, Projection systems

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Proceedings Article | 24 November 2021 Paper

Stereo rectification of Scheimpflug telecentric lenses

Yan Hu, Zhongwei Liang, Shijie Feng, Wei Yin, Jiaming Qian, Qian Chen, Chao Zuo

Proceedings Volume 12065, 1206518 (2021) https://doi.org/10.1117/12.2605321

KEYWORDS: Cameras, Calibration, Sensors, Lenses, 3D metrology, Imaging systems, Distortion, 3D modeling, Digital image correlation, Visualization

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Proceedings Article | 15 January 2021 Paper

An openCL-based speckle matching on the monocular 3D sensor using speckle projection

Wei Yin, Chao Zuo, Shijie Feng, Tianyang Tao, Qian Chen

Proceedings Volume 11761, 1176122 (2021) https://doi.org/10.1117/12.2587482

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Proceedings Article | 10 October 2020 Presentation + Paper

An end-to-end speckle matching network for 3D imaging

Wei Yin, Chao Zuo, Shijie Feng, Tianyang Tao, Qian Chen

Proceedings Volume 11552, 115520S (2020) https://doi.org/10.1117/12.2573817

KEYWORDS: 3D metrology, Phase retrieval, Speckle, Fringe analysis, Fourier transforms, Spatial resolution, Calibration, Composites, Speckle pattern

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Proceedings Article | 7 October 2020 Paper

Robust absolute 3D measurement using stereo cost-volume filtering for fringe orders

Yuhao Shang, Wei Yin, Tianyang Tao, Shijie Feng, Qian Chen, Chao Zuo

Proceedings Volume 11571, 1157117 (2020) https://doi.org/10.1117/12.2580542

KEYWORDS: 3D metrology, Fringe analysis, Speckle pattern, Phase shifts, Projection systems, 3D modeling, Speckle, Optical filters

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Proceedings Article | 7 October 2020 Paper

Learning-based absolute 3D shape measurement based on single fringe phase retrieval and speckle correlation

Wei Yin, Chao Zuo, Shijie Feng, Tianyang Tao, Qian Chen

Proceedings Volume 11571, 115710M (2020) https://doi.org/10.1117/12.2580148

KEYWORDS: 3D metrology, Phase retrieval, Fringe analysis, Neural networks, Cameras, Speckle, Feature extraction, Phase shifts, Data processing

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Proceedings Article | 18 November 2019 Paper

Temporal phase unwrapping using multi-scale deep neural networks

Wei Yin, Chao Zuo, Shijie Feng, Tianyang Tao, Qian Chen

Proceedings Volume 11189, 111890A (2019) https://doi.org/10.1117/12.2537582

KEYWORDS: Fringe analysis, Neural networks, Phase shifts, 3D metrology, Reliability, Data processing, Machine learning, Imaging systems, Phase measurement, Projection systems

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The multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical phase unwrapping algorithm for fringe projection profilometry (FPP), is capable of eliminating the phase ambiguities even in the presence of surface discontinuities or spatially isolated objects. For the simplest and most efficient case, two sets of 3-step phase-shifting fringe patterns are used: the high-frequency one is for 3D measurement and the unit-frequency one is for unwrapping the phase obtained from the high-frequency pattern set. The final measurement precision or sensitivity is determined by the number of fringes used within the high-frequency pattern, under the precondition that the phase can be successfully unwrapped without triggering the fringe order error. Consequently, in order to guarantee a reasonable unwrapping success rate, the fringe number (or period number) of the high-frequency fringe patterns is generally restricted to about 16, resulting in limited measurement accuracy. On the other hand, using additional intermediate sets of fringe patterns can unwrap the phase with higher frequency, but at the expense of a prolonged pattern sequence. Inspired by recent successes of deep learning techniques for computer vision and computational imaging, in this work, we report that the deep neural networks can learn to perform TPU after appropriate training, as called deep-learning based temporal phase unwrapping (DL-TPU), which can substantially improve the unwrapping reliability compared with MF-TPU. We further experimentally demonstrate for the first time, to our knowledge, that the high-frequency phase obtained from 64-period 3-step phase-shifting fringe patterns can be directly and reliably unwrapped from one unit-frequency phase using DLTPU. These results highlight that challenging issues in optical metrology can be potentially overcome through machine learning, opening new avenues to design powerful and extremely accurate high-speed 3D imaging systems ubiquitous in nowadays science, industry, and multimedia.

Proceedings Article | 18 November 2019 Paper

Single-shot 3D shape measurement with spatial frequency multiplexing using deep learning

Chen Yang, Wei Yin, Hao Xu, Jiachao Li, Shijie Feng, Tianyang Tao, Qian Chen, Chao Zuo

Proceedings Volume 11189, 111891P (2019) https://doi.org/10.1117/12.2537732

KEYWORDS: Fringe analysis, 3D metrology, Neural networks, Phase measurement, Spatial frequencies, Multiplexing, Phase shifts, Fourier transforms, Electroluminescent displays, Composites

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Proceedings Article | 18 November 2019 Paper

Microscopic fringe projection profilometry comparison based on stereoscopic microscope and telecentric lenses

Yan Hu, Yichao Liang, Tianyang Tao, Wei Yin, Jiaming Qian, Shijie Feng, Chao Zuo, Qian Chen

Proceedings Volume 11189, 1118909 (2019) https://doi.org/10.1117/12.2537483

KEYWORDS: 3D metrology, Calibration, 3D modeling, Reconstruction algorithms, Cameras, Algorithm development, Projection systems, Lenses, Imaging systems, Distortion

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Proceedings Article | 18 November 2019 Paper

High-speed 3D shape measurement with the multi-view system using deep learning

Wei Yin, Chao Zuo, Shijie Feng, Tianyang Tao, Qian Chen

Proceedings Volume 11189, 111890B (2019) https://doi.org/10.1117/12.2537573

KEYWORDS: 3D metrology, Neural networks, Cameras, Fringe analysis, 3D image processing, Imaging systems, Phase shifts, Speckle, Reliability, Projection systems

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Proceedings Article | 16 October 2019 Paper

Full-surface 3-D reconstruction based on surround structured lighting

Hao Xu, Wei Yin, Chen Yang, Jiachao Li, Shijie Feng, Tianyang Tao, Qian Chen, Chao Zuo

Proceedings Volume 11205, 1120527 (2019) https://doi.org/10.1117/12.2548032

KEYWORDS: Mirrors, Clouds, 3D metrology, Cameras, Imaging systems, Calibration, Projection systems, Data conversion, 3D modeling, Light sources and illumination

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Proceedings Article | 16 October 2019 Paper

Fast panoramic 3D shape measurement using the multi-view system with plane mirrors

Wei Yin, Chao Zuo, Shijie Feng, Tianyang Tao, Qian Chen

Proceedings Volume 11205, 112052E (2019) https://doi.org/10.1117/12.2547971

KEYWORDS: Mirrors, 3D metrology, Calibration, Panoramic photography, Cameras, Imaging systems, 3D modeling, Projection systems, Data conversion, Phase compensation

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Proceedings Article | 16 October 2019 Paper

High dynamic range and fast 3D measurement based on a telecentric stereo-microscopic system

Yan Hu, Yichao Liang, Tianyang Tao, Wei Yin, Jiaming Qian, Shijie Feng, Chao Zuo, Qian Chen

Proceedings Volume 11205, 112052C (2019) https://doi.org/10.1117/12.2547762

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Proceedings Article | 13 May 2019 Presentation + Paper

Bi-frequency temporal phase unwrapping using deep learning

Wei Yin, Chao Zuo, Shijie Feng, Tianyang Tao, Qian Chen

Proceedings Volume 10991, 109910D (2019) https://doi.org/10.1117/12.2520201

KEYWORDS: Fringe analysis, Neural networks, Reliability, 3D metrology, Phase shifts, Phase measurement, Algorithm development, Machine learning, Cameras, Data acquisition

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SPIE Journal Paper | 28 February 2019 Open Access

Fringe pattern analysis using deep learning

Shijie Feng, Qian Chen, Guohua Gu, Tianyang Tao, Liang Zhang, Yan Hu, Wei Yin, Chao Zuo

AP, Vol. 1, Issue 02, 025001, (February 2019) https://doi.org/10.1117/12.10.1117/1.AP.1.2.025001

KEYWORDS: Fringe analysis, Fourier transforms, Picosecond phenomena, Neural networks, Demodulation, Convolutional neural networks, Photonics, Error analysis, 3D modeling, Optical spheres

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Proceedings Article | 2 November 2018 Paper

High-speed 3D shape measurement using composite structured-light patterns and multiview system

Wei Yin, Chao Zuo, Shijie Feng, Tianyang Tao, Qian Chen

Proceedings Volume 10819, 108190G (2018) https://doi.org/10.1117/12.2500696

KEYWORDS: 3D metrology, Speckle pattern, Composites, 3D acquisition, Fringe analysis, Speckle, Projection systems, Phase shifts, Binary data, Cameras

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Proceedings Article | 24 July 2018 Paper

Real-time binocular stereo vision system based on FPGA

Jiawei Ma, Wei Yin, Chao Zuo, Shijie Feng, Qian Chen

Proceedings Volume 10827, 108271U (2018) https://doi.org/10.1117/12.2500769

KEYWORDS: Field programmable gate arrays, Stereo vision systems, Cameras, Image processing, Image storage, Calibration, Imaging systems, Image transmission, Image resolution

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Proceedings Article | 24 July 2018 Paper

High-speed three-dimensional shape measurement using improved bi-frequency scheme and number-theoretical phase unwrapping

Wei Yin, Chao Zuo, Shijie Feng, Tianyang Tao, Qian Chen

Proceedings Volume 10827, 108271T (2018) https://doi.org/10.1117/12.2500529

KEYWORDS: Projection systems, 3D metrology, Cameras, Fringe analysis, Phase shifts, Binary data, Imaging systems, Ceramics, 3D image processing, High speed cameras

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Showing 5 of 24 publications

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