CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12319 > Article
Presentation
21 December 2022 Learning-based fringe analysis with uncertainty quantification
Shijie Feng
Author Affiliations +
Proceedings Volume 12319, Optical Metrology and Inspection for Industrial Applications IX; 1231902 (2022) https://doi.org/10.1117/12.2641613
Event: SPIE/COS Photonics Asia, 2022, Online Only
Abstract
Recently, the deep learning technology has been successfully applied to many applications of optical metrology, e.g., fringe-pattern analysis, fringe denoising, digital holography, and three-dimensional shape measurements. However, deep neural networks (DNNs) cannot always produce a provably correct solution, and the prediction error cannot be easily detected and evaluated unless the ground truth is available. This issue is critical for optical metrology, as the reliability and repeatability of the measurement are of major importance for high-stakes scenarios. As most deep neural networks are driven by data completely and work without considering any physical principles, how to believe the prediction of the DNN in optical metrology is a big challenge. Inspired by recent successful Bayesian deep learning approaches, we demonstrate that a Bayesian convolutional neural network (BNN) can be trained to not only retrieve the phase from a single fringe pattern but also produce uncertainty maps depicting the pixel-wise confidence measure of the estimated phase. Experimental results show that the proposed BNN can quantify the reliability of phase predictions under conditions of various training dataset sizes and never-before-experienced inputs. We believe that a DNN that can provide confidence measure of the estimated phase is crucial to fringe-pattern analysis and it has great potentials for inspiring novel and reliable learning-based optical metrology approaches.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Shijie Feng "Learning-based fringe analysis with uncertainty quantification", Proc. SPIE 12319, Optical Metrology and Inspection for Industrial Applications IX, 1231902 (21 December 2022); https://doi.org/10.1117/12.2641613
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 PRESENTATION
WATCH
PRESENTATION SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
KEYWORDS
Fringe analysis
Optical metrology
Neural networks
Reliability
3D metrology
Convolutional neural networks
Denoising
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top