Automatic feature set selection using the modified Karhunen-Loeve transform: industrial application in visual inspection

Noel A. Murphy; Kenneth Lodge

doi:10.1117/12.150292

6 August 1993 Automatic feature set selection using the modified Karhunen-Loeve transform: industrial application in visual inspection

Noel A. Murphy, Kenneth Lodge

Author Affiliations +

Proceedings Volume 2064, Machine Vision Applications, Architectures, and Systems Integration II; (1993) https://doi.org/10.1117/12.150292
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States

Abstract

For many vision-based inspection tasks, clear measurable features inherent in an image are sufficient to allow classification of the image content. Sometimes, however, it is difficult to select suitable feature sets, as the classification can only be made on the basis of subtle, diffuse relationships within the image. It has previously been shown that it is possible to automatically select sets of 'feature values' in such applications, using a procedure based on a modified version of the Karhunen-Loeve Transform (KLT), applied to window (imagelets) within images. This paper discusses the extension of that work in three directions. It describes the possibilities for using this data- reduction procedure in conjunction with more traditional and better understood classification methods for the decision-making stage. It discusses the potential for application of these ideas by combining the statistical transform coding stage with a range of image pre-processing operations. It also examines some of the issues of industrial integration of this procedure.

Citation Download Citation

Noel A. Murphy and Kenneth Lodge "Automatic feature set selection using the modified Karhunen-Loeve transform: industrial application in visual inspection", Proc. SPIE 2064, Machine Vision Applications, Architectures, and Systems Integration II, (6 August 1993); https://doi.org/10.1117/12.150292

ACCESS THE FULL ARTICLE

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