Abstract/Details

Methods for improving the feature representations of 2-D image patterns


1991 1991

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Abstract (summary)

When pattern recognition from 2-D images is implemented using a template matching scheme, several problems are encountered in generating a satisfactory feature representation of each image pattern. This dissertation proposes improved solutions to three of these problems: (1) feature extraction that is invariant to in-plane rotation and uniform radial scaling variations of the image patterns, (2) removal of background shadows from 2-D image patterns, and (3) locating image patterns prior to classifying them. The performance level of the proposed improvements is illustrated using several examples, and the scale and rotation invariant algorithms are tested using Monte Carlo simulations. The experimental results indicate an improvement in the quality of features extracted from shadow removed images, and they show good invariance to scale and rotation variations. In addition, the spectral similarity feature extraction algorithm distinguishes well between out-of-class patterns. The pattern locating algorithm performs well in locating image patterns resulting in a large reduction in the number of features extracted from an image over conventional local window feature extraction techniques.

Indexing (details)


Subject
Electrical engineering
Classification
0544: Electrical engineering
Identifier / keyword
Applied sciences; pattern recognition; shadow removal
Title
Methods for improving the feature representations of 2-D image patterns
Author
Scanlan, Joseph M.
Number of pages
218
Publication year
1991
Degree date
1991
School code
0022
Source
DAI-B 52/05, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
Advisor
Chabries, Douglas M.
University/institution
Brigham Young University
University location
United States -- Utah
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
9131832
ProQuest document ID
303938195
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/303938195
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