scholarly journals Fringe Calibration using Neural Network Signal Mapping for Structured Light Profilometers

2006 ◽  
Author(s):  
Matthew J. Baker ◽  
Jiangtao Xi ◽  
Joe F. Chicharo
Keyword(s):  
Neural Network ◽  
Structured Light

Neural network digital fringe calibration technique for structured light profilometers

2007 ◽  
Vol 46 (8) ◽  
pp. 1233 ◽  
Author(s):  
Matthew J. Baker ◽  
Jiangtao Xi ◽  
Joe F. Chicharo
Keyword(s):  
Neural Network ◽  
Structured Light ◽  
Calibration Technique

scholarly journals Real-Time Stripe Width Computation Using Back Propagation Neural Network for Adaptive Control of Line Structured Light Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2618 ◽  
Author(s):  
Jingbo Zhou ◽  
Laisheng Pan ◽  
Yuehua Li ◽  
Peng Liu ◽  
Lijian Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Real Time ◽  
Exposure Time ◽  
Control Method ◽  
Structured Light ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Stripe Width ◽  
Accuracy Result

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.

Center Extraction of Structured Light Stripe Based on Back Propagation Neural Network

2019 ◽  
Vol 39 (12) ◽  
pp. 1212005
Author(s):  
李玥华 Li Yuehua ◽  
刘朋 Liu Peng ◽  
周京博 Zhou Jingbo ◽  
任有志 Ren Youzhi ◽  
靳江艳 Jin Jiangyan
Keyword(s):  
Neural Network ◽  
Structured Light ◽  
Back Propagation ◽  
Back Propagation Neural Network

Research on System Calibration of Structured-Light Measurement Based on Neural Network

2016 ◽  
Vol 45 (5) ◽  
pp. 512002 ◽  
Author(s):  
齐召帅 QI Zhao-shuai ◽  
王昭 WANG Zhao ◽  
黄军辉 HUANG Jun-hui ◽  
薛琦 XUE Qi ◽  
高建民 GAO Jian-min
Keyword(s):  
Neural Network ◽  
Structured Light ◽  
System Calibration ◽  
Light Measurement

3D Double-Vision Inspection Based on Structured Light

2003 ◽  
Vol 125 (3) ◽  
pp. 617-623 ◽  
Author(s):  
Guangjun Zhang ◽  
Zhenzhong Wei ◽  
Xin Li
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Structured Light ◽  
Inspection System ◽  
Double Vision ◽  
3D Vision ◽  
Vision Inspection ◽  
Blind Area ◽  
World Coordinate System ◽  
Setting Error

3D double-vision inspection is very necessary. It has a larger field of view, and can solve the problem of “blind area” for 3D measurement, as proposed by 3D single-vision inspection. At the beginning of this paper, the principle of structured-light based 3D vision inspection is introduced. Then, a method of gaining calibration points for 3D double-vision inspection system is proposed in detail. In order to gain calibration points with high precision, a double-directional photoelectric aiming device is designed as well, and a method for compensating the position-setting error of the aiming device is described. The coordinates of all calibration points are precisely unified in a world coordinate system. The application of RBF (radial basis function) neural network in establishing the inspection model of structured-light based 3D vision is described in detail. Finally, with the use of the calibration points, the inspection model of 3D double-vision based on RBF neural network is successfully established. The model’s training accuracy is 0.078 mm, and the testing accuracy is 0.084 mm.

scholarly journals Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 459
Author(s):  
Hieu Nguyen ◽  
Zhaoyang Wang
Keyword(s):  
Neural Network ◽  
Structured Light ◽  
Three Dimensional ◽  
Industrial Applications ◽  
Shape Reconstruction ◽  
Reconstruction Technique ◽  
Neural Network Learning ◽  
3D Shape ◽  
Triple Frequency ◽  
3D Shape Reconstruction

Accurate three-dimensional (3D) shape reconstruction of objects from a single image is a challenging task, yet it is highly demanded by numerous applications. This paper presents a novel 3D shape reconstruction technique integrating a high-accuracy structured-light method with a deep neural network learning scheme. The proposed approach employs a convolutional neural network (CNN) to transform a color structured-light fringe image into multiple triple-frequency phase-shifted grayscale fringe images, from which the 3D shape can be accurately reconstructed. The robustness of the proposed technique is verified, and it can be a promising 3D imaging tool in future scientific and industrial applications.

Neural network classification of structured light in optical turbulence

2021 ◽  
Author(s):  
William A. Jarrett ◽  
Svetlana Avramov-Zamurovic ◽  
Charles Nelson ◽  
Joel Esposito ◽  
Milo W. Hyde
Keyword(s):  
Neural Network ◽  
Structured Light ◽  
Optical Turbulence ◽  
Neural Network Classification
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