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3D Shape, Deformation, Motion and Vi...

Nguyen, Hieu.

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3D Shape, Deformation, Motion and Vibration Measurements Using the Stereo-Vision-Based Techniques.

Record Type:	Electronic resources : Monograph/item
Title/Author:	3D Shape, Deformation, Motion and Vibration Measurements Using the Stereo-Vision-Based Techniques./
Author:	Nguyen, Hieu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	126 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13859429
ISBN:	9781392231197

3D Shape, Deformation, Motion and Vibration Measurements Using the Stereo-Vision-Based Techniques.
Nguyen, Hieu.

3D Shape, Deformation, Motion and Vibration Measurements Using the Stereo-Vision-Based Techniques. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 126 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--The Catholic University of America, 2019.

This item must not be added to any third party search indexes.

Full-field 3D sensing techniques for shape, deformation, motion, and vibration measurements have emerged as an important tool for numerous applications in various fields. As technologies evolve, there is a high demand to extend the capabilities of the 3D sensing to achieve fast-speed, high-accuracy, and broad-range performance.This dissertation aims to conduct imaging-based research on exploring algorithms and techniques to carry out the 3D shape, deformation, motion and vibration measurements with high accuracy and fast speed. Two stereo-vision-based 3D imaging techniques are investigated: fringe projection profilometry (FPP) and digital image correlation (DIC) techniques. They normally include three key steps: (1) camera calibration, (2) image matching and (3) coordinate determination. Previously, a high-accuracy camera calibration technique based on hyper-precise control-point detection has been developed. Therefore, this dissertation puts emphasis on exploring algorithms related to image matching and 3D coordinate determination as well as the design of the hardware setup. The research work comprises three main components:-Exploration of real-time, high-accuracy 3D imaging and shape measurement techniques using the FPP approach. By encoding three phase-shifted patterns into the red, green, and blue (RGB) channels of a color image and controlling a projector to project the RGB channels individually, the technique can conduct the 3D measurements in real time by synchronizing the projector and the camera. Meanwhile, the measurement accuracy is dramatically improved by introducing novel phase determination schemes.-Exploration of high-accuracy 3D shape, deformation, motion and vibration measurement techniques using the DIC approach. In this work, infrared patterns projected from the Kinect sensor are adopted to considerably facilitate the correlation analysis with enhanced accuracy. Moreover, a technique to acquire 3D digital images of human face without the use of active lighting and artificial patterns is proposed. A few advanced schemes, such as feature-matching-based initial guess, multiple subsets, iterative optimization algorithm, and reliability-guided computation path, are employed.-Incorporation of the deep convolution neural networks (CNNs) concepts into the 3D sensing technique. A single-shot 3D shape reconstruction technique integrating the FPP with the CNNs is proposed. Unlike other complex methods, the novel technique uses an end-to-end network to directly reconstruct a 3D image from its 2D counterpart.

ISBN: 9781392231197Subjects--Topical Terms:

649730
Mechanical engineering.

3D Shape, Deformation, Motion and Vibration Measurements Using the Stereo-Vision-Based Techniques.
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520 $a Full-field 3D sensing techniques for shape, deformation, motion, and vibration measurements have emerged as an important tool for numerous applications in various fields. As technologies evolve, there is a high demand to extend the capabilities of the 3D sensing to achieve fast-speed, high-accuracy, and broad-range performance.This dissertation aims to conduct imaging-based research on exploring algorithms and techniques to carry out the 3D shape, deformation, motion and vibration measurements with high accuracy and fast speed. Two stereo-vision-based 3D imaging techniques are investigated: fringe projection profilometry (FPP) and digital image correlation (DIC) techniques. They normally include three key steps: (1) camera calibration, (2) image matching and (3) coordinate determination. Previously, a high-accuracy camera calibration technique based on hyper-precise control-point detection has been developed. Therefore, this dissertation puts emphasis on exploring algorithms related to image matching and 3D coordinate determination as well as the design of the hardware setup. The research work comprises three main components:-Exploration of real-time, high-accuracy 3D imaging and shape measurement techniques using the FPP approach. By encoding three phase-shifted patterns into the red, green, and blue (RGB) channels of a color image and controlling a projector to project the RGB channels individually, the technique can conduct the 3D measurements in real time by synchronizing the projector and the camera. Meanwhile, the measurement accuracy is dramatically improved by introducing novel phase determination schemes.-Exploration of high-accuracy 3D shape, deformation, motion and vibration measurement techniques using the DIC approach. In this work, infrared patterns projected from the Kinect sensor are adopted to considerably facilitate the correlation analysis with enhanced accuracy. Moreover, a technique to acquire 3D digital images of human face without the use of active lighting and artificial patterns is proposed. A few advanced schemes, such as feature-matching-based initial guess, multiple subsets, iterative optimization algorithm, and reliability-guided computation path, are employed.-Incorporation of the deep convolution neural networks (CNNs) concepts into the 3D sensing technique. A single-shot 3D shape reconstruction technique integrating the FPP with the CNNs is proposed. Unlike other complex methods, the novel technique uses an end-to-end network to directly reconstruct a 3D image from its 2D counterpart.
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