rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine vision inspection of fresh market carrots
  name:
    name part: Howarth, Matthew Scott
    name part: 1962-
    role:
      role term: creator
  name:
    name part: Coble, Charlie G.
    role:
      role term: degree committee member.
  name:
    name part: Kehtarnavaz, Nasser
    role:
      role term: degree committee member.
  name:
    name part: Pike, Leonard M.
    role:
      role term: degree committee member.
  name:
    name part: Searcy, Stephen W.
    role:
      role term: degree supervisor.
  name:
    name part: Whittaker, A. Dale
    role:
      role term: degree committee member.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  genre: Academic theses.
  origin info:
    place:
      place term: xx
    date issued: 1991
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xvii, 145 leaves : illustrations ; 29 cm
  abstract: A machine vision system was developed to inspect fresh-market carrots. Software and hardware components were designed and developed into a working inspection system, INSPECT. The main objectives of this study were to characterize normal and defective carrots, develop feature extraction techniques, develop Bayes decision functions for classification, and finally, integrate and test all developments with a sample of carrots. The reflectance properties of fresh-market carrots were measured over the visible and near infrared portion of the electromagnetic spectrum. Characteristic reflectance curves were developed. Soft rot, dry rot, and black crown were significantly different from normal carrot flesh. Cavity spots were not significantly different. It was determined that the electromagnetic range for optimal contrast between normal and defect carrot tissue was 535 to 722 nm. An adaptive thresholding technique was developed to enhance surface defects while retaining edge information. In addition, feature extraction techniques were developed to characterize surface defects, forking, curvature, and brokenness. Using a modified connected components algorithm, the segmented image was divided into blocks. The relationship between blocks was used to extract features for surface defects and forking. A curvature profile was developed from which three features were extracted to measure curvature. Using the diameter profile, three features were derived to characterize brokenness. Using the different features, Bayes classifiers were developed and tested. Normal probability distributions were assumed and used to define the state-conditional probabilities for each of the features. For curvature and brokenness, a log-normal probability distribution was used as well. For curvature classification, the Bayes classifier was not improved. However when a log-normal probability distribution was used for brokenness classification, improve was noticed. Two different neural networks were develo…
  note: by Matthew Scott Howarth.
  note: "Major subject: Agricultural Engineering."
  note: Typescript (photocopy).
  note: Vita.
  note: Ph. D. Texas A & M University 1991
  note: Includes bibliographical references.
  subject:
    topic: Carrots
    topic: Inspection
    topic: Automation
  subject:
    topic: Computer vision
  subject:
    topic: Pattern recognition systems
  subject:
    topic: Agricultural Engineering
  subject:
    topic: Major agricultural engineering
  identifier: http://proxy.library.tamu.edu/login
  identifier: https://hdl.handle.net/1969.1/DISSERTATIONS-1282533
  related item:
    location:
      url: http://proxy.library.tamu.edu/login
  related item:
    location:
      url: https://hdl.handle.net/1969.1/DISSERTATIONS-1282533
  record info:
    record content source: TXA
    record creation date: 930113
    record change date: 20220103134954.0
    record identifier: in00001113933
    language of cataloging:
      language term: eng