Real-Time 3D Surface Digitization

2012 ◽  
Keyword(s):  
Real Time ◽  
3D Surface

Real-time 3D surface imaging for patient positioning in radiotherapy

2003 ◽  
Vol 57 (2) ◽  
pp. S187 ◽  
Author(s):  
N Smith ◽  
I Meir ◽  
G Hale ◽  
R Howe ◽  
L Johnson ◽  
...  
Keyword(s):  
Real Time ◽  
Patient Positioning ◽  
Surface Imaging ◽  
3D Surface ◽  
3D Surface Imaging

scholarly journals Real time 3D surface imaging for the analysis of respiratory motion during radiotherapy

2004 ◽  
Vol 60 (1) ◽  
pp. S603-S604 ◽  
Author(s):  
U. Johnson ◽  
D. Landau ◽  
J. Lindgren-Turner ◽  
N. Smith ◽  
I. Meir ◽  
...  
Keyword(s):  
Real Time ◽  
Respiratory Motion ◽  
Surface Imaging ◽  
3D Surface ◽  
3D Surface Imaging

Quantitative real-time detection of carcinoembryonic antigen (CEA) from pancreatic cyst fluid using 3-D surface molecular imprinting

The Analyst ◽  
2016 ◽  
Vol 141 (14) ◽  
pp. 4424-4431 ◽  
Author(s):  
Yingjie Yu ◽  
Qi Zhang ◽  
Jonathan Buscaglia ◽  
Chung-Chueh Chang ◽  
Ying Liu ◽  
...  
Keyword(s):  
Carcinoembryonic Antigen ◽  
Real Time ◽  
Molecular Imprinting ◽  
Pancreatic Cyst ◽  
Cyst Fluid ◽  
Surface Molecular Imprinting ◽  
3D Surface ◽  
Real Time Detection ◽  
Pancreatic Cyst Fluid

In this study, a real time potentiometric biosensor based on the 3D surface molecular imprinting was developed for CEA detection.

scholarly journals Real-Time 3D Surface Measurement in Additive Manufacturing Using Deep Learning

2021 ◽  
Author(s):  
Liu Chenang ◽  
Wang Rongxuan ◽  
Zhenyu Kong ◽  
Babu Suresh ◽  
Joslin Chase ◽  
...  
Keyword(s):  
Deep Learning ◽  
Additive Manufacturing ◽  
Surface Morphology ◽  
Real Time ◽  
Learning Algorithm ◽  
Surface Measurement ◽  
Monitoring And Control ◽  
Deep Learning Algorithm ◽  
3D Surface ◽  
3D Surface Morphology

Layer-wise 3D surface morphology information is critical for the quality monitoring and control of additive manufacturing (AM) processes. However, most of the existing 3D scan technologies are either contact or time consuming, which are not capable of obtaining the 3D surface morphology data in a real-time manner during the process. Therefore, the objective of this study is to achieve real-time 3D surface data acquisition in AM, which is achieved by a supervised deep learning-based image analysis approach. The key idea of this proposed method is to capture the correlation between 2D image and 3D point cloud, and then quantify this relationship by using a deep learning algorithm, namely, convolutional neural network (CNN). To validate the effectiveness and efficiency of the proposed method, both simulation and real-world case studies were performed. The results demonstrate that this method has strong potential to be applied for real-time surface morphology measurement in AM, as well as other advanced manufacturing processes.

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