Determining composition of grain mixtures using texture energy operators

Bradley Pryor Kjell

doi:10.1117/12.131548

1 November 1992 Determining composition of grain mixtures using texture energy operators

Bradley Pryor Kjell

Proceedings Volume 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision; (1992) https://doi.org/10.1117/12.131548
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

Abstract

Images of texture may be convolved with a set of small operators to produce texture energy features for classification or for image segmentation. These operators are usually picked from a standard set, such as Laws' texture energy operators or the variations discussed by other researchers. In this paper, texture energy features are used to determine the percentage composition of mixtures of rice and barley. The grains of white rice and pearled barley are similar in size and reflectivity, and hundreds of overlapping grains appear in each image. This problem is representative of many visual inspection tasks. Two approaches are used: multi- linear regression, and linear classification into discrete composition classes. The texture energy features used are standard Laws' operators in two sizes, and operators found through a stochastic optimization procedure.

Citation Download Citation

Bradley Pryor Kjell "Determining composition of grain mixtures using texture energy operators", Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); https://doi.org/10.1117/12.131548

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