Three-core fiber far field structured light pattern generator and its shape sensing application

2008 ◽  
Author(s):  
Libo Yuan ◽  
Jun Yang ◽  
Zhihai Liu ◽  
Chunying Guan ◽  
Qiang Dai ◽  
...  
Keyword(s):  
Structured Light ◽  
Pattern Generator ◽  
Far Field ◽  
Light Pattern ◽  
Core Fiber ◽  
Sensing Application ◽  
Shape Sensing

Three-core fiber-based shape-sensing application

2008 ◽  
Vol 33 (6) ◽  
pp. 578 ◽  
Author(s):  
Libo Yuan ◽  
Jun Yang ◽  
Chunying Guan ◽  
Qiang Dai ◽  
Fengjun Tian
Keyword(s):  
Core Fiber ◽  
Sensing Application ◽  
Shape Sensing

Stereo vision with image‐guided structured‐light pattern matching

2015 ◽  
Vol 51 (3) ◽  
pp. 238-239 ◽  
Author(s):  
Min‐Gyu Park ◽  
Jonghee Park ◽  
Yongho Shin ◽  
Eul‐Gyoon Lim ◽  
Kuk‐Jin Yoon
Keyword(s):  
Pattern Matching ◽  
Stereo Vision ◽  
Structured Light ◽  
Image Guided ◽  
Light Pattern

Real-time 3D shape inspection system of automotive parts based on structured light pattern

2011 ◽  
Vol 43 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Jing Xu ◽  
Ning Xi ◽  
Chi Zhang ◽  
Quan Shi ◽  
John Gregory
Keyword(s):  
Real Time ◽  
Structured Light ◽  
Inspection System ◽  
3D Shape ◽  
Light Pattern ◽  
Automotive Parts ◽  
Shape Inspection

An absolute phase technique for 3D profile measurement using four-step structured light pattern

2012 ◽  
Vol 50 (9) ◽  
pp. 1274-1280 ◽  
Author(s):  
Jing Xu ◽  
Shaoli Liu ◽  
An Wan ◽  
Bingtuan Gao ◽  
Qiang Yi ◽  
...  
Keyword(s):  
Structured Light ◽  
Profile Measurement ◽  
Light Pattern ◽  
Absolute Phase ◽  
3D Profile

scholarly journals One-Shot M-Array Pattern Based on Coded Structured Light for Three-Dimensional Object Reconstruction

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xiaojun Jia ◽  
Zihao Liu
Keyword(s):  
Structured Light ◽  
Three Dimensional ◽  
Training Dataset ◽  
3D Objects ◽  
Light Pattern ◽  
Array Pattern ◽  
Key Points ◽  
Deep Convolution Neural Network ◽  
Chain Sequence ◽  
Dimensional Object

Pattern encoding and decoding are two challenging problems in a three-dimensional (3D) reconstruction system using coded structured light (CSL). In this paper, a one-shot pattern is designed as an M-array with eight embedded geometric shapes, in which each 2 × 2 subwindow appears only once. A robust pattern decoding method for reconstructing objects from a one-shot pattern is then proposed. The decoding approach relies on the robust pattern element tracking algorithm (PETA) and generic features of pattern elements to segment and cluster the projected structured light pattern from a single captured image. A deep convolution neural network (DCNN) and chain sequence features are used to accurately classify pattern elements and key points (KPs), respectively. Meanwhile, a training dataset is established, which contains many pattern elements with various blur levels and distortions. Experimental results show that the proposed approach can be used to reconstruct 3D objects.

Recent trends and role of large area flexible electronics in shape sensing application – a review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Riyaz Ali Shaik ◽  
Elizabeth Rufus
Keyword(s):  
Flexible Electronics ◽  
Humanoid Robots ◽  
Shape Reconstruction ◽  
Large Area ◽  
Content Type ◽  
3D Shape ◽  
3D Shape Reconstruction ◽  
3D Scanners ◽  
Sensing Application ◽  
Shape Sensing

Purpose This paper aims to review the shape sensing techniques using large area flexible electronics (LAFE). Shape perception of humanoid robots using tactile data is mainly focused. Design/methodology/approach Research papers on different shape sensing methodologies of objects with large area, published in the past 15 years, are reviewed with emphasis on contact-based shape sensors. Fiber optics based shape sensing methodology is discussed for comparison purpose. Findings LAFE-based shape sensors of humanoid robots incorporating advanced computational data handling techniques such as neural networks and machine learning (ML) algorithms are observed to give results with best resolution in 3D shape reconstruction. Research limitations/implications The literature review is limited to shape sensing application either two- or three-dimensional (3D) LAFE. Optical shape sensing is briefly discussed which is widely used for small area. Optical scanners provide the best 3D shape reconstruction in the noncontact-based shape sensing; here this paper focuses only on contact-based shape sensing. Practical implications Contact-based shape sensing using polymer nanocomposites is a very economical solution as compared to optical 3D scanners. Although optical 3D scanners can provide a high resolution and fast scan of the 3D shape of the object, they require line of sight and complex image reconstruction algorithms. Using LAFE larger objects can be scanned with ML and basic electronic circuitory, which reduces the price hugely. Social implications LAFE can be used as a wearable sensor to monitor critical biological parameters. They can be used to detect shape of large body parts and aid in designing prosthetic devices. Tactile sensing in humanoid robots is accomplished by electronic skin of the robot which is a prime example of human–machine interface at workplace. Originality/value This paper reviews a unique feature of LAFE in shape sensing of large area objects. It provides insights from mechanical, electrical, hardware and software perspective in the sensor design. The most suitable approach for large object shape sensing using LAFE is also suggested.

Vibration amplitude analysis with a single frame using a structured light pattern of a four-core optical fibre

2005 ◽  
Vol 249 (4-6) ◽  
pp. 515-522 ◽  
Author(s):  
S. Tunç Yılmaz ◽  
Umut D. Özugˇurel ◽  
Karahan Bulut ◽  
M. Naci Inci
Keyword(s):  
Optical Fibre ◽  
Vibration Amplitude ◽  
Structured Light ◽  
Amplitude Analysis ◽  
Light Pattern ◽  
Single Frame
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