Intensified Multispectral Imaging System for Poultry Carcass Inspection

Park, B.; Chen, Y.-R.

doi:10.13031/2013.28291

Cited by 28 publications

(14 citation statements)

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“…Early studies of image texture analysis have involved autocorrelation functions (Liu et al, 1993), power spectra, and relative frequencies of various grey levels on unnormalized images (Park and Chen, 1994). Grey-tone spatial-dependence matrices, or co-occurrence matrices (COMs), also have been used for image texture analysis in agricultural applications (Shearer and Holmes, 1990;Park et al, 1992).…”

Section: Introductionmentioning

confidence: 99%

Discriminant analysis of dual-wavelength spectral images for classifying poultry carcasses

Park

Lawrence

Windham

et al. 2002

Computers and Electronics in Agriculture

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An analysis of texture features, based on co-occurrence matrices (COMs), was conducted to determine the performance of dual-wavelength imaging for discriminating unwholesome poultry carcasses from wholesome carcasses. The variance, sum average, sum variance, and sum entropy of COMs were the most significant texture features (P B0.005) for identifying unwholesome poultry carcasses. However, the feature values of angular second moment, variance, sum average, sum variance, and sum entropy did not vary with the COM parameters, distance and direction. The characteristics of variance and sum variance texture features varied with the wavelength of spectral images and with condemnation of poultry carcasses, as well. The sum variance of wholesome carcasses was higher (P B 0.005) than unwholesome carcasses for spectral images at 542 nm. For 542 and 700 nm images, linear discriminant models were able to identify unwholesome carcasses with a classification accuracy of 91.4%. However, a single linear discriminant model was not acceptable for identifying three different types of carcasses (wholesome, septicemic and cadaver), because of extreme inaccuracy for septicemic carcasses. In this case, the classifier that demonstrated the highest accuracy was 89.6% accurate at 542 nm. Thus, a dual-wavelength imaging system with optical filters of 542 and 700 nm wavelengths appears promising for detecting unwholesome poultry carcasses. Published by Elsevier Science B.V.

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Section: Introductionmentioning

confidence: 99%

Discriminant analysis of dual-wavelength spectral images for classifying poultry carcasses

Park

Lawrence

Windham

et al. 2002

Computers and Electronics in Agriculture

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“…There was an interesting study in bovine carcass classification addressing the problem of classifier effect and repeatability in bovine carcass grading (Díez et al, 2003), demonstrating another possible application of ANN for the purposes of monitoring. Much work has also been devoted to the automatic inspection of wholesomeness of chicken carcasses using different optical techniques (Park & Chen, 1994;Chen et al, 1996Chen et al, 1998a,b;Chao et al, 2000Chao et al, , 2002 LD -longissimus dorsi; VIS -visible; NIR -near infrared; R 2 -coefficient of determination; r -correlation coefficient; P -prediction; C -classification; RPD -residual predictive deviation. , 2002).…”

Section: Application Of Ann For Carcass Quality or Classificationmentioning

confidence: 99%

Application of Artificial Neural Networks in Meat Production and Technology

Prevolnik¹,

Škorjanc²,

Čandek-Potokar³

et al. 2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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“…Muir et al (1982) used spatial information at eight wavelengths to detect 12 Á/15 kinds of blemishes on a potato. At ISL, Park and Chen (1994) used an intensified multispectral imaging system to discriminate wholesome poultry carcasses from unwholesome carcasses. Park and Chen (1996) reported the performance of a co-occurrence matrix textural analysis method as a tool of multispectral image analysis for detecting unwholesome poultry carcasses.…”

Section: Multispectral Imagingmentioning

confidence: 99%

Machine vision technology for agricultural applications

Chen

Chao

Kim

2002

Computers and Electronics in Agriculture

361

140

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Current applications of machine vision in agriculture are briefly reviewed. The requirements and recent developments of hardware and software for machine vision systems are discussed, with emphases on multispectral and hyperspectral imaging for modern food inspection. Examples of applications for detection of disease, defects, and contamination on poultry carcasses and apples are also given. Future trends of machine vision technology applications are discussed. #

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Intensified Multispectral Imaging System for Poultry Carcass Inspection

Cited by 28 publications

References 0 publications

Discriminant analysis of dual-wavelength spectral images for classifying poultry carcasses

Discriminant analysis of dual-wavelength spectral images for classifying poultry carcasses

Application of Artificial Neural Networks in Meat Production and Technology

Machine vision technology for agricultural applications

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