By Topic

Texture analysis for foreign object detection using a single layer neural network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
D. Patel ; Dept. of Phys., R. Holloway & Bedford New Coll., Egham, UK ; I. Hannah ; E. R. Davies

Inspection of food products for quality control to ensure that products are free from impurities (foreign objects) such as stone, glass or metal is a demanding part of a production process. This paper presents a method to detect foreign objects in bags of frozen vegetables and in particular using bags of frozen corn kernels. X-ray imaging is used to view the contents of the bag. We use principal component analysis (PCA) techniques to find the orthogonal vectors in data space that account for as much as possible of the variance of the data. The vectors are then used as the coefficients of the convolution masks. We briefly mention the various texture analysis methods using PCA and describe the artificial neural network texture description method used in this study

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994