Mr. Aditya Madipadaga Profile

Aditya Madipadaga

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 10 October 2020 Presentation + Paper

Investigation of temporally varying fringe pattern defects using machine learning for optical metrology

Aditya Madipadaga, Rajshekhar Gannavarpu

Proceedings Volume 11552, 115520L (2020) https://doi.org/10.1117/12.2584950

KEYWORDS: Fringe analysis, Machine learning, Optical metrology, Signal to noise ratio, Nondestructive evaluation, Image classification, Detection and tracking algorithms, Spatial frequencies, Interferometry, Interference (communication)

Read Abstract +

Defect identification for quality control and industrial inspection necessitates the need for novel techniques in the field of non-destructive testing and experimental mechanics. Optical interferometric techniques are quite popular for non-destructive testing, and hence they are extensively used to locate and identify defects from fringe patterns. A rapid variation of the fringe density in the vicinity of the defect’s region serves as a means for the detection algorithms to identify them. With the advent of machine learning over recent years, it has paved way for algorithms with automatic detection, thereby, eliminating the problem of manual thresholding. In this paper, we propose an elegant technique which relies on computing windowed Fourier spectrum of the fringe pattern at a given spatial frequency and subsequently utilizing this spectrum with a GPU accelerated Support Vector Machine (SVM) algorithm for classification of a defect and a non-defect region. The windowed Fourier spectrum of the fringe pattern serves as a feature vector for the GPU accelerated SVM algorithm which internally performs a pixel classification, thereby producing a binary output of the defect. The performance of the proposed technique is tested on computer-generated fringe patterns at severe noise levels. A machine learning model is trained using the windowed Fourier spectrum of a 1024 x 1024 fringe pattern which is corrupted with an additive Gaussian noise at a signal to noise ratio of 5dB. The best set of hyper-parameters are deduced from the validation data and the proposed method is tested on the fringe patterns of size 1024 x 1024 which contain temporally varying defects. The results indicate that the proposed method is computationally efficient, robust against noise, and also capable of automating the defect identification problem.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years