Statistical model-based thresholding of multispectral images for contaminant detection on poultry carcasses

Transactions of the ASABE

Volumn 50, Issue 4, 2007, Pages 1433-1442

  Yoon, Seung Chul ^a   Lawrence, Kurt C ^a   Park, Osoon ^a   Windham, William R ^a  

^a RUSSELL RESEARCH CENTER   (United States)

Author keywords

Fecal contamination; Food safety; Hyperspectral; Multispectral imaging; Poultry inspection; Statistical density estimation; Target detection; Thresholding

Indexed keywords

FECAL CONTAMINATION; FOOD SAFETY; MULTISPECTRAL IMAGING; POULTRY INSPECTION; STATISTICAL DENSITY ESTIMATION;

ALGORITHMS; CONTAMINATION; GAUSSIAN DISTRIBUTION; IMAGE ANALYSIS; MATHEMATICAL MODELS; PROBABILITY DENSITY FUNCTION; TARGET TRACKING;

FOOD PRESERVATION;

ALGORITHM; FOOD SAFETY; GAUSSIAN METHOD; MULTISPECTRAL IMAGE; PIXEL; POULTRY; THRESHOLD;

AVES;

EID: 34548790794     PISSN: 21510032     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (25)

1. Duda, R. O., P. E. Hart, and D. G. Stork. 2001. Pattern Classification. 2nd ed. New York, N.Y.: Wiley.
2. Heitschmidt, G. W., K. C. Lawrence, W. R. Windham, B. Park, and D. P. Smith. 2004. Improved imaging system for fecal detection. In Proc. SPIE 5587:112-120.
3. Kay, S. M. 1998. Fundamentals of Statistical Signal Processing: Detection Theory. Vol. II. Upper Saddle River, N.J.: Prentice-Hall.
4. Kim, M. S., A. M. Lefcourt, Y. R. Chen, I. Kim, D. E. Chan, and K. Chao. 2002. Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part II. Application of hyperspectral fluorescence imaging. Trans. ASAE 45(6): 2039-2047.
5. Kim, M. S., A. M. Lefcourt, Y. R. Chen, and Y. Tao. 2005 Automated detection of fecal contamination of apples based on multispectral fluorescence image fusion. J. Food Eng. 71(1): 85-91.
6. Landgrebe, D. A. 2003. Signal Theory Methods in Multispectral Remote Sensing. New York, N.Y.: Wiley.
7. Lawrence, K. C., B. Park, W. R. Windham, and C. Mao. 2003. Calibration of a pushbroom hyperspectral imaging system for agricultural inspection. Trans. ASAE 46(2): 513-521.
8. Manolakis, D., C. Siracusa, and G. Shaw. 2001. Hyperspectral subpixel target detection using the linear mixing model. IEEE Trans. Geostience and Remote Sensing 39(7): 1392-1409.
9. Manolakis, D., and G. Shaw. 2002. Detection algorithms for hyperspectral imaging applications. IEEE Signal Processing Magazine 19(1): 23-43.
10. Nakano, K., K. Sasaoka, and Y. Ohtsuka. 2000. A study on non-destructive detection of abnormal eggs by using image processing. In Proc. 2nd Asian Conference for Information Technology in Agriculture, 345-352. Suwon, Korea: Asian Federation for Information Technology in Agriculture.
11. Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man and Cybernetics 9(1): 62-67.
12. Park, B., K. C. Lawrence, W. R. Windham, and R. J. Buhr. 2002. Hyperspectral imaging for detecting fecal and ingesta contaminants on poultry carcasses. Trans. ASAE 45(6): 2017-2026.
13. Park, B., K. C. Lawrence, W. R. Windham, and D. P. Smith. 2004. Multispectral imaging system for fecal and ingesta detection on poultry carcasses. J. Food Process Eng. 27: 311-327.
14. Park, B., S. C. Yoon, K. C. Lawrence, and W. R. Windham. 2005. Dynamic thresholding method for improving contaminant detection accuracy with hyperspectral images. ASAE Paper No. 053071. St. Joseph, Mich.: ASAE.
15. Poor, H. V. 1994. An Introduction to Signal Detection and Estimation. New York, N.Y.: Springer.
16. Qi, S. L., A. Konduris, D. W. Litke, and J. Dupree. 2002. USGS Water-Resources Investigations Report 02-4236. Washington, D.C.: U.S. Geological Survey.
17. San, B. T., E. O. Sumer, and B. Gurcay. 2004. Comparison of band ratioing and spectral indices methods for detecting alunite and kaolinite minerals using ASTER data in Biga region, Turkey. Int'l Archives of Photogrammetry: Remote Sensing and Spatial Info. Sci. 35(7): 77-82.
18. Sezgin, M., and B. Sankur. 2004. Survey over image thresholding techniques and quantitative performance evaluation. J. Electronic Imaging 13(1): 146-165.
19. Theodoridis, S., and K. Koutroumbas. 2003. Pattern Recognition. 2nd ed. New York, N.Y.: Academic Press.
20. Webb, A. 2002. Statistical Pattern Recognition. 2nd ed. New York, N.Y.: Wiley.
21. Windham, W. R., K. C. Lawrence, B. Park, and R. J. Buhr. 2003a. Visible/NIR spectroscopy for characterizing fecal contamination of chicken carcasses. Trans. ASAE 46(3): 747-751.
22. Windham, W. R., K. C. Lawrence, B. Park, L. A. Martinez, M. A. Lanoue, D. A. Smith, J. Heitschmidt, and G. H. Poole. 2003b. Method and system for contaminant detection curing food processing. U.S. Patent No. 6587575.
23. Windham, W. R., D. P. Smith, B. Park, K. C. Lawrence, and P. W. Feldner. 2003c. Algorithm development with visible/near-infrared spectra for detection of poultry feces and ingesta. Trans. ASAE 46(6): 1733-1738.
24. Yoon, S. C., K. C. Lawrence, B. Park, and W. R. Windham. 2007. Optimization of fecal detection using hyperspectral imaging and kernel density estimation. Trans. ASABE 50(3): 1063-1071.
25. Zhang, X. P., and M. D. Desai. 2001. Segmentation of bright targets using wavelets and adaptive thresholding. IEEE Trans. Image Processing 10(7): 1020-1030.