Recent advances in image processing using image texture features for food quality assessment

Trends in Food Science and Technology

Volumn 29, Issue 1, 2013, Pages 35-43

  Jackman, Patrick ^a   Sun, Da Wen ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION TECHNOLOGY; ECONOMIC COSTS; EFFICIENT ANALYSIS; FOOD QUALITY; FOOD SURFACES; HARDWARE FRAMEWORK; POTENTIAL BENEFITS; QUALITY FACTORS; SOFTWARE OPTIONS; TEXTURE FEATURES;

CHEMICAL CONTAMINATION; FACTOR ANALYSIS; HARDWARE;

COMPUTER VISION;

EID: 84872011905     PISSN: 09242244     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tifs.2012.08.008     Document Type: Review

References (68)

1. Absolute Medical Equipment Online catalogue 2012, Garnerville, NY, USA.
2. Antequera T., Caro A., Rodriguez P.G., Perez T. Monitoring the ripening process of Iberian ham by computer vision on magnetic resonance imaging. Meat Science 2007, 76(3):561-567.
3. Blasco J., Aleixos N., Cubero S., Gomez-Sanchis J., Molto E. Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features. Computers and Electronics in Agriculture 2009, 66(1):1-8.
4. Blasco J., Aleixos N., Molto E. Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering 2007, 81(3):535-543.
5. Blasco J., Cubero S., Gomez-Sanchis J., Mira P., Molto E. Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. Journal of Food Engineering 2009, 90(1):27-34.
6. Borggaard C., Madsen N.T., Thodberg H.H. In-line image analysis in the slaughter industry, illustrated by beef carcass classification. Meat Science 1996, 43(S1):151-163.
7. Brosnan T., Sun D.-W. Inspection and grading of agricultural and food products by computer vision systems - a review. Computers and Electronics in Agriculture 2002, 36(2-3):193-213.
8. Brosnan T., Sun D.-W. Improving quality inspection of food products by computer vision - a review. Journal of Food Engineering 2004, 61(1):3-16.
9. Chandraratne M.R., Kulasiri D., Frampton C., Samarasinghe S., Bickerstaffe R. Prediction of lamb carcass grades using features extracted from lamb chop images. Journal of Food Engineering 2006, 74(1):116-124.
10. Chandraratne M.R., Kulasiri D., Samarasinghe S. Classification of lamb carcass using machine vision: comparison of statistical and neural network analyses. Journal of Food Engineering 2007, 82(1):26-34.
11. Chandraratne M.R., Samarasinghe S., Kulasiri D., Bickerstaffe R. Prediction of lamb tenderness using image surface texture features. Journal of Food Engineering 2006, 77(3):492-499.
12. Du C.-J., Sun D.-W. Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science & Technology 2004, 15(5):230-249.
13. Du C.-J., Sun D.-W. Learning techniques used in computer vision for food quality evaluation: a review. Journal of Food Engineering 2006, 72(1):39-55.
14. Du C.-J., Sun D.-W. Estimating the surface area and volume of ellipsoidal ham using computer vision. Journal of Food Engineering 2006, 73(2):260-268.
15. Du C.-J., Sun D.-W. Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture. Meat Science 2006, 72(2):294-302.
16. Du C.-J., Sun D.-W. Retrospective shading correction of confocal laser scanning microscopy beef images for three-dimensional visualization. Food and Bioprocess Technology 2009, 2(2):167-176.
17. Edmund Optics Online catalogue 2011, Barrington, NJ, USA.
18. Goni S.M., Purlis E., Salvadori V.O. Geometry modelling of food materials from magnetic resonance imaging. Journal of Food Engineering 2008, 88(4):561-567.
19. Gonzales-Barron U., Butler F. Discrimination of crumb grain visual appearance of organic and non-organic bread loaves by image texture analysis. Journal of Food Engineering 2008, 84(3):480-488.
20. Haralick R.M., Shanmugam K., Dinstein I. Texture features for image classification. IEEE Transactions on Systems, Man and Cybernetics 1973, 3(6):610-621.
21. * color scale 2008, Reston, VA, USA.
22. Instron Inc. 5500 Series advanced materials testing systems 2008, Instron Inc., Norwood, MA, USA.
23. Jackman P. Development of a novel image analysis system for the prediction of beef palatability 2009, National University of Ireland, Dublin, Ireland.
24. Jackman P., Sun D.-W. Recent advances in the use of computer vision technology in the quality assessment of fresh meats. Trends in Food Science and Technology 2011, 22(4):185-197.
25. Jackman P., Sun D.-W. Application of computer vision systems for objective assessment of food qualities. Emerging technologies for evaluating food quality & food safety 2011, Taylor & Francis, Boca Raton, Florida, USA. Y.-J. Cho (Ed.).
26. Jackman P., Sun D.-W. Computer vision in the meat industry (fresh and processed). Computer vision technology in the food and beverage industries 2011, Woodhead, Cambridge, UK. I.D.-W. Sun (Ed.).
27. Jackman P., Sun D.-W., Allen P. Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. Meat Science 2009, 83(2):187-194.
28. Jackman P., Sun D.-W., Allen P. Comparison of various wavelet texture features to predict beef palatability. Meat Science 2009, 83(1):82-87.
29. Jackman P., Sun D.-W., Allen P. Comparison of the predictive power of beef surface wavelet texture features at high and low magnification. Meat Science 2009, 82(3):353-356.
30. Jackman P., Sun D.-W., Allen P. Prediction of beef palatability from colour, marbling and surface texture features of longissimus dorsi. Journal of Food Engineering 2009, 96(1):151-165.
31. Jackman P., Sun D.-W., Allen P., Brandon K., White A.M. Correlation of consumer assessment of longissimus dorsi beef palatability with image colour, marbling and surface texture features. Meat Science 2010, 84(3):564-568.
32. Jackman P., Sun D.-W., Allen P., Valous N.A., Mendoza F., Ward P. Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection. Meat Science 2010, 84(4):711-717.
33. Jackman P., Sun D.-W., Du C.-J., Allen P. Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment. Pattern Recognition 2009, 42(5):751-763.
34. Jackman P., Sun D.-W., Du C.-J., Allen P., Downey G. Prediction of beef eating quality from colour, marbling and wavelet texture features. Meat Science 2008, 80(4):1273-1281.
35. Kaiser G. A friendly guide to wavelets 1994, Birkhauser, Boston, MA, USA.
36. Lee D.-J., Archibald J.K., Chang Y.-C., Greco C.R. Robust color space conversion and color distribution analysis techniques for date maturity evaluation. Journal of Food Engineering 2008, 88(3):364-372.
37. Li J., Tan J., Martz F.A., Heymann H. Image texture features as indicators of beef tenderness. Meat Science 1999, 53(1):17-22.
38. Li J., Tan J., Shatadal P. Classification of tough and tender beef by image texture analysis. Meat Science 2001, 57(4):341-346.
39. Moreda G.P., Ortiz-Canavate J., Garcia-Ramos F.J., Ruiz-Altisent M. Non-destructive technologies for fruit and vegetable size determination - a review. Journal of Food Engineering 2009, 92(1):119-136.
40. Princeton Instruments Princeton instruments online catalogue 2011, Princeton Instruments, Trenton, NJ, USA.
41. Pydipati R., Burks T.F., Lee W.S. Identification of citrus disease using color texture features and discriminant analysis. Computers and Electronics in Agriculture 2006, 52(1).
42. Quevedo R.A., Aguilera J.M. Computer vision and stereoscopy for estimating firmness in the salmon (Salmon salar) fillets. Food and Bioprocess Technology 2010, 3(4):561-567.
43. Quevedo R.A., Aguilera J.M., Pedreschi F. Color of salmon fillets by computer vision and sensory panel. Food and Bioprocess Technology 2010, 3(5):637-643.
44. * color information from digital images. LWT - Food Science and Technology 2009, 42(8):1367-1373.
45. Quevedo R.A., Diaz O., Ronceros B., Pedreschi F., Aguilera J.M. Description of the kinetic enzymatic browning in banana (Musa cavendish) slices using non-uniform color information from digital images. Food Research International 2009, 42(9):1309-1314.
46. Quevedo R.A., Lopez C.G., Aguilera J.M., Cadoche L. Description of food surfaces and microstructural changes using fractal image texture analysis. Journal of Food Engineering 2002, 53(4):361-371.
47. Quevedo R.A., Mendoza F., Aguilera J.M., Chanona J., Gutierrez-Lopez G. Determination of senescent spotting in banana (Musa cavendish) using fractal texture Fourier image. Journal of Food Engineering 2008, 84(4):509-515.
48. Randen T., Husoy J.H. Filtering for texture classification: a comparative study. IEEE Transactions on Pattern Analysis and Machine Intelligence 1999, 21(4):291-310.
49. Riquelme M.T., Barreiro P., Altisent M.R., Valero C. Olive classification according to external damage using image analysis. Journal of Food Engineering 2008, 87(3):371-379.
50. Rosales-Juarez M., Gonzalez-Mendoza B., Lopez-Guel E.C., Lauzano-Bautista F., Chanona-Perez J., Gutierrez-Lopez G., et al. Changes on dough rheological characteristics and bread quality as a result of the addition of germinated and non-germinated soybean flour. Food and Bioprocess Technology 2008, 1(2):152-160.
51. Russell R.A., Adams N.M., Stevens D.A., Batty E., Jensen K., Freemont P.S. Segmentation of fluorescence microscopy images for quantitative analysis of cell nuclear architecture. Biophysics Journal 2009, 96(8):3379-3389.
52. Singh C.B., Choudhary R., Jayas D.S., Paliwal J. Wavelet analysis of signals in agriculture and food quality inspection. Food and Bioprocess Technology 2008, 10.1007/s11947-008-0093-7.
53. Specim Imaging Specim imaging online catalogue 2011, Specim Imaging, Oulu, Finland.
54. Stein E.M., Shakarchi R. Fourier analysis: An introduction 2003, Princeton University Press, Princeton, NJ, USA.
55. Stokes M., Anderson M., Chandrasekar S., Motta R. A standard default color space for the internet: sRGB 1996, International Color Consortium, Reston, VA, USA.
56. Sun D.-W. Inspecting pizza topping percentage and distribution by a computer vision method. Journal of Food Engineering 2000, 44(4):245-249.
57. Computer vision technology for food quality evaluation 2008, Academic Press/Elsevier, San Diego, California, USA, (583 pp.). D.-W. Sun (Ed.).
58. Sun D.-W., Brosnan T. Pizza quality evaluation using computer vision - part 1 - Pizza base and sauce spread. Journal of Food Engineering 2003, 57(1):81-89.
59. The Mathworks Matlab user guide R2011b 2011, Natick, Massachusetts, USA.
60. USDA United States standards for grades of peaches 2004, USDA, Washington, DC.
61. USDA Agricultural Marketing Service Grading, certification and verification: USDA quality standards 2011, USDA, Washington, DC.
62. Wakamatsu J., Odagiri H., Nishimura T., Hattori A. Observation of the distribution of Zn protoporphyrin IX (ZPP) in Parma ham by using purple LED and image analysis. Meat Science 2006, 74(4):594-599.
63. Yadav B.K., Jindal V.K. Dimensional changes in milled rice (Oryza sativa L.) kernel during cooking in relation to its physicochemical properties by image analysis. Journal of Food Engineering 2007, 81(4):710-720.
64. Zenoozian M.S., Devahastin S. Application of wavelet transform coupled with artificial neural network for predicting physicochemical properties of osmotically dehydrated pumpkin. Journal of Food Engineering 2009, 90(2):219-227.
65. Zheng C.X., Sun D.-W., Zheng L.Y. Recent developments and applications of image features for food quality evaluation and inspection - a review. Trends in Food Science & Technology 2006, 17(12):642-655.
66. Zheng C.X., Sun D.-W., Zheng L.Y. Recent applications of image texture for evaluation of food qualities - a review. Trends in Food Science & Technology 2006, 17(3):113-128.
67. Zion B., Alchanatis V., Ostrovsky V., Barki A., Karplus I. Real-time underwater sorting of edible fish species. Computers and Electronics in Agriculture 2007, 56(1):34-45.
68. Zion B., Alchanatis V., Ostrovsky V., Barki A., Karplus I. Classification of guppies' (Poecilia reticulata) gender by computer vision. Aquacultural Engineering 2008, 38(2):97-104.