Categorization of pork quality using Gabor filter-based hyperspectral imaging technology

Journal of Food Engineering

Volumn 99, Issue 3, 2010, Pages 284-293

  Liu, L ^a   Ngadi, M O ^a   Prasher, S O ^a   Gariepy C ^b  

^a MCGILL UNIVERSITY   (Canada)

^b AGRICULTURE AND AGRI FOOD CANADA   (Canada)

Author keywords

Gabor filter; Hyperspectral image; K means clustering; LDA; PCA; Pork quality; Texture

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; CONFIDENCE INTERVAL; CROSS-VALIDATION TECHNIQUE; FILTERED IMAGES; GABOR FILTER; GABOR FUNCTION; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGING; IMAGE TEXTURE; K-MEAN CLUSTERING; K-MEANS CLUSTERING; LINEAR DISCRIMINANT ANALYSIS; MEAT INDUSTRY; OBJECTIVE ASSESSMENT; PRINCIPAL COMPONENTS; QUALITY LEVELS; SAMPLE SETS; SPECTRAL FEATURE; STATISTICAL CLASSIFICATION; TEXTURE FEATURES; TRAINING AND TESTING;

CLUSTERING ALGORITHMS; DISCRIMINANT ANALYSIS; FINANCIAL DATA PROCESSING; IMAGE PROCESSING; IMAGING TECHNIQUES; INDEPENDENT COMPONENT ANALYSIS; INDUSTRIAL APPLICATIONS; MEATS; MICROCOMPUTERS; TEXTURES; WATER SUPPLY SYSTEMS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77950860803     PISSN: 02608774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfoodeng.2010.03.001     Document Type: Article

References (19)

1. Clausi D.A., and Ed Jernigan M. Designing Gabor filters for optimal texture separability. Pattern Recognition 33 1 (2000) 1835-1849
2. Daugman J.G. Two-dimensional spectral analysis of cortical receptive field profiles. Vision Research 20 (1980) 847-856
3. Daugman J.G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A 2 7 (1985) 1160-1169
4. Daugman J.G. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions of the Pattern Analysis and Machine Intelligence 15 11 (1993) 1148-1161
5. Duda R.O., Hart P.E., and Stork D. Pattern Classification (2000), Wiley
6. Geesink G.H., Schreutelkamp F.H., Frankhuizen R., Vedder H.W., Faber N.M., Kranen R.W., and Gerritzen M.A. Prediction of pork quality attributes from near infrared reflectance spectra. Meat Science 65 (2003) 661-668
7. Hartigan J.A., and Wong M.A. A k-means clustering algorithm. Applied Statistics 28 1 (1979) 100-108
8. ISO 12232, 1997. Photography - Electronic Still Picture Cameras - Determining ISO Speed Here.
9. Kauffman R.G., Cassens R.G., Scherer A., and Meeker D.L. Variations in Pork Quality (1992), National Pork Producer Council, Des Moines, IA
10. Li J., Tan J., Martz F.A., and Heymann H. Image texture features as indicators of beef tenderness. Meat Science 53 1 (1999) 17-22
11. Lloyd S.P. Least squares quantization in PCM. IEEE Transactions of the Information Theory 28 2 (1982) 129-136
12. Ma L., Tan T., Wang Y., and Zhang D. Personal identification based on iris texture analysis. IEEE Transactions of the Pattern Recognition and Machine Intelligence 25 12 (2002) 1519-1533
13. Manjunath B.S., and Ma W.Y. Texture feature for browing and retrieval of image data. IEEE Transactions of the Pattern Analysis and Machine Intelligence 18 8 (1996) 837-842
14. Murray, A.C., 2000. Reducing losses from farm gate to packer: a Canadian's perspective. In: Proceeding of First International Virtual Conference on Pork Quality, Concordia, Brazil. pp. 72-84.
15. Nam K.C., Du M., Jo C., and Ahn D.U. Effect of ionizing radiation on quality characteristics of vacuum-packaged normal, pale-soft-exudative, and dark-firm-dry pork. Innovative Food Science and Emerging Technologies 3 (2002) 73-79
16. NPB (National Pork Board), 1999. Pork Quality Standards. Des Moines, IA USA.
17. Qiao J., Ngadi M.O., Wang N., Gariepy C., and Prasher S.O. Pork quality and marbling level assessment using a hyperspectral imaging system. Journal of Food Engineering 83 (2007) 10-16
18. Tan J. Meat quality evaluation by computer vision. Journal of Food Engineering 61 1 (2004) 27-35
19. Zhang D., Kong W.K., You J., and Wong M. Online palmprint identification. IEEE Transactions of the Pattern Recognition and Machine Intelligence 25 9 (2003) 1041-1050