Classification of dates varieties and effect of motion blurring on standardized moment features

Gabriel Thomas, A. Manickavasagan, R. Al-Yahyai

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Computer vision technology has been used as a successful non-destructive quality assessment tool for various food products. In general, several features are extracted from the images of interest, and used for the classification models. Furthermore, in most of the studies, static images have been used in the calibration and evaluation models. Classification models with a reduced number of features, and a mechanism to test the capability of the algorithm for moving objects by means of simulating the blurring effect on the static images would be beneficial to determine the performance of the system in real-time quality monitoring in industries. Using three date varieties as model food, motion was simulated for the date's images and a successful neural network classifier was designed with only three statistical features (mean, standard deviation, and skewness). The reduced number of features and simplicity of the classifier yielded a solution that can be potentially implemented in hardware fast enough so that to consider the case of classification of the dates in a conveyor belt. To test the solution under such conditions, a blurring degradation function was used to verify that the classifier would work. The effects that motion blurring causes to these statistical moments in a general sense were examined using random numbers drawn from the distribution in the Pearson system. Because motion blurring showed a tendency to change the distribution to a Gaussian density, the same features and classifier yielded similar results despite of motion.

Original languageEnglish
Pages (from-to)21-26
Number of pages6
JournalJournal of Food Measurement and Characterization
Volume6
Issue number1-4
Publication statusPublished - Dec 2012

Fingerprint

dates (fruit)
Classifiers
Food
computer vision
Computer Systems
neural networks
Computer vision
Calibration
Industry
foods
calibration
testing
Technology
Neural networks
industry
Hardware
Degradation
degradation
Monitoring
monitoring

Keywords

  • Bayes classifier
  • Classification
  • Image motion
  • Neural network
  • Pearson random numbers
  • Statistical moment

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality
  • Food Science

Cite this

Classification of dates varieties and effect of motion blurring on standardized moment features. / Thomas, Gabriel; Manickavasagan, A.; Al-Yahyai, R.

In: Journal of Food Measurement and Characterization, Vol. 6, No. 1-4, 12.2012, p. 21-26.

Research output: Contribution to journalArticle

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