A Radial Basis Function Approach to a Color Image Classification Problem in a Real Time Industrial Application

Simple item page
dc.contributor.authorSahin, Feraten
dc.contributor.committeechairBay, John S.en
dc.contributor.committeememberVanLandingham, Hugh F.en
dc.contributor.committeememberAbbott, A. Lynnen
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2014-03-14T20:51:59Zen
dc.date.adate1997-06-27en
dc.date.available2014-03-14T20:51:59Zen
dc.date.issued1997-06-27en
dc.date.rdate1997-06-27en
dc.date.sdate1997-06-27en
dc.description.abstractIn this thesis, we introduce a radial basis function network approach to solve a color image classification problem in a real time industrial application. Radial basis function networks are employed to classify the images of finished wooden parts in terms of their color and species. Other classification methods are also examined in this work. The minimum distance classifiers are presented since they have been employed by the previous research. We give brief definitions about color space, color texture, color quantization, color classification methods. We also give an intensive review of radial basis functions, regularization theory, regularized radial basis function networks, and generalized radial basis function networks. The centers of the radial basis functions are calculated by the k-means clustering algorithm. We examine the k-means algorithm in terms of starting criteria, the movement rule, and the updating rule. The dilations of the radial basis functions are calculated using a statistical method. Learning classifier systems are also employed to solve the same classification problem. Learning classifier systems learn the training samples completely whereas they are not successful to classify the test samples. Finally, we present some simulation results for both radial basis function network method and learning classifier systems method. A comparison is given between the results of each method. The results show that the best classification method examined in this work is the radial basis function network method.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-6197-223641en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-6197-223641/en
dc.identifier.urihttp://hdl.handle.net/10919/36847en
dc.publisherVirginia Techen
dc.relation.hasparttitle.PDFen
dc.relation.haspartabs-out.PDFen
dc.relation.haspartch1.pdfen
dc.relation.haspartch2.pdfen
dc.relation.haspartCh3.pdfen
dc.relation.haspartCh4a.PDFen
dc.relation.haspartch4b.PDFen
dc.relation.haspartCh5.PDFen
dc.relation.haspartref.pdfen
dc.relation.haspartvita.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectimage processingen
dc.subjectRadial basis fucntionsen
dc.subjectradial basis function networksen
dc.subjectk-means algorithmen
dc.subjectpattern recognitionen
dc.subjectcolor quantizationen
dc.subjectlearning classifier systemsen
dc.titleA Radial Basis Function Approach to a Color Image Classification Problem in a Real Time Industrial Applicationen
dc.typeThesisen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

Files

Original bundle
Now showing 1 - 5 of 10
Thumbnail Image
Name:
title.PDF
Size:
6.03 KB
Format:
Adobe Portable Document Format
Download
Thumbnail Image
Name:
abs-out.PDF
Size:
19.03 KB
Format:
Adobe Portable Document Format
Download
Thumbnail Image
Name:
ch1.pdf
Size:
101.94 KB
Format:
Adobe Portable Document Format
Download
Thumbnail Image
Name:
ch2.pdf
Size:
83.42 KB
Format:
Adobe Portable Document Format
Download
Thumbnail Image
Name:
Ch3.pdf
Size:
159.35 KB
Format:
Adobe Portable Document Format
Download

Collections

Masters Theses