The Resource Hybrid multivariate classification technique and its application in tissue image analysis, by Iyad Hatem

Hybrid multivariate classification technique and its application in tissue image analysis, by Iyad Hatem

Label
Hybrid multivariate classification technique and its application in tissue image analysis
Title
Hybrid multivariate classification technique and its application in tissue image analysis
Statement of responsibility
by Iyad Hatem
Creator
Subject
Language
eng
Summary
Discriminant analysis and neural network techniques were used in this work to develop a hybrid multivariate classification machine. The design of this machine was based on a theoretical interpretation of human recognition of patterns. This interpretation considers the pattern recognition task as a two-step process: difference detection and classifier training. The classification machine, called machine of classifiers (MC), has two components. The first component performs unsupervised clustering of data based on an iterative Fisher discriminant technique. The second component is a supervised classifier designed on the basis of the committee machine technique. The MC was tested on artificially-generated multivariate data. The results showed the effectiveness of the MC in classification. Two applications in tissue image analysis were investigated. The first application involved segmenting cartilage and bone areas from other tissues in beef vertebra images. This segmentation was essential for developing an image processing system to determine beef maturity. The MC was applied on image functions in two color systems and successfully segmented the cartilage and bone areas. The second application was meat tenderness prediction. In this application, near-infrared (NIR) images of beef samples were used for tenderness score prediction. The image features were used to develop an MC to predict meat tenderness. The MC was useful in classifying the samples into "tough" and "tender" groups
Additional physical form
Also available on the Internet.
Cataloging source
MUU
http://library.link/vocab/creatorDate
1965-
http://library.link/vocab/creatorName
Hatem, Iyad
Degree
Ph. D.
Dissertation year
2003.
Government publication
government publication of a state province territory dependency etc
Granting institution
University of Missouri-Columbia
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • bibliography
  • theses
http://library.link/vocab/subjectName
  • Image analysis
  • Pattern formation (Biology)
  • Neural networks (Computer science)
  • Pattern recognition systems
Target audience
specialized
Label
Hybrid multivariate classification technique and its application in tissue image analysis, by Iyad Hatem
Instantiates
Publication
Note
  • Typescript
  • Vita
Bibliography note
Includes bibliographical references (leaves 135-143)
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Control code
53987548
Dimensions
29 cm
Dimensions
unknown
Extent
xviii, 144 leaves
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations
Specific material designation
remote
Label
Hybrid multivariate classification technique and its application in tissue image analysis, by Iyad Hatem
Publication
Note
  • Typescript
  • Vita
Bibliography note
Includes bibliographical references (leaves 135-143)
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Control code
53987548
Dimensions
29 cm
Dimensions
unknown
Extent
xviii, 144 leaves
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations
Specific material designation
remote

Library Locations

    • Engineering Library & Technology CommonsBorrow it
      W2001 Lafferre Hall, Columbia, MO, 65211, US
      38.946102 -92.330125
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