24 October 2017 - سه شنبه 2 آبان 1396
جستجوی مقالات
کلید واژگان
جستجوی پیشرفته
شناسنامه ی نشریه
صاحب امتیاز:
موسسه پژوهشی علوم و فناوری رنگ و پوشش
مدیر مسئول:
پروفسور زهرا رنجبر
سردبیر:
دکتر شهره روحانی
مدیر اجرایی:
دکتر مریم عطائی فرد
شاپا چاپی:
2251-7278
شاپا الکترونیکی:
2383-2223
دسترسی سریع
آخرین شماره ها
نظر سنجی
نظر شما در مورد سایت نشریه دنیای رنگ چیست؟
مطلوب
نسبتا مطلوب
نیاز به بهسازی دارد
ضعیف

مروري بر كاربردهاي روش‌هاي پردازش تصوير بر كنترل كيفيت محصولات غذايي-بخش اول: روش‌های آماری پردازش بافتار

نشریه: سال ششم- شماره سوم- پاييز 1395 - مقاله 8   صفحات :  65 تا 77



کد مقاله:
JSCW-13-06-2016-10280

مولفین:
سجاد قدرتی: دانشگاه صنعتي اميركبير - مهندسي پليمر
محسن محسنی بزرگی: دانشگاه صنعتي امير كبير - دانشكده مهندسي پليمر و رنگ
سعیده گرجی کندی: دانشگاه صنعتي اميركبير - دانشكده مهندسي پليمر و رنگ


چکیده مقاله:



با افزایش حساسیت‌های قانونی و انتظارات مصرف‌کنندگان در ارتباط با کیفیت محصولات غذایی، نیاز به ارزیابی دقیق و سریع این محصولات در صنایع غذایی روبه افزایش است. بینایی ماشین با بهره‌گیری از روش‌های پردازش تصویر امکان نظارتِ مکانیزه و غیرمخرب بر کیفیت محصولات غذایی را فراهم نموده است. بافتار به‌عنوان یکی از مهم‌ترین ویژگی‌های تصویر، در کنترل کیفیت محصولات غذایی در سال‌های اخیر به‌طور گسترده به کار گرفته شده است. به‌طور کلی روش‌های ارزیابی بافتار به چهار دسته‌ی آماری، ساختاری، روش‌های مبتنی بر مدل و مبتنی بر تبدیل تقسیم‌بندی می‌شود. در بخش اول این پژوهش به روش‌های آماری پرداخته می‌شود و در بخش دوم روش‌های مبتنی بر مدل و مبتنی بر تبدیل‌های ریاضی مورد بررسی قرار خواهند گرفت. روش‌های آماری بر مبنای کمیت‌های آماری حاصل از روشنایی پیکسل‌های تصویر عمل می‌کنند؛ در صورتی که روش‌های ساختاری بر مبنای ساختارهای کوچک تکرار شونده -اولیه - در تصویر که از گردهمایی پیکسل‌های مشابه تشکیل می‌شوند، بافتار را ارزیابی می‌نمایند. در مقاله حاضر کاربردهای روش‌های آماری ارزیابی بافتار در صنایع غذایی بررسی شده است. از این روی پس از معرفی نحوه‌ی عملکرد این روش‌ها، مثال‌هایی از پژوهش‌های اخیر ارائه شده است که با به‌کارگیری روش‌های آماری پردازش بافتار تصویر، کنترل کیفیت مواد غذایی را ممکن ساخته‌اند. نتایج حاصل از تحقیقات گذشته نشان می‌دهد که رایج‌ترین روش‌های پردازش بافتار در صنایع غذایی روش‌های آماری هستند که علت محبوبیت آنها دقت عملکرد بالای آنها است.


Article's English abstract:

Consumers increased expectations of high quality food products as well as stringent regulations has increased the need for an accurate and fast method for quality assessment and control of the products in food industries. Machine vision with the aid of various image processing methods has been introduced as an objective, automate, and non-destructive approach capable for food quality control. Texture as one of the most important image's features has been used extensively in food quality monitoring applications. Generally, quantitative texture assessment methods are divided into four groups: statistical, structural, model-based, and transform-based methods. In the first part of this research (part A), the statistical methods are reviewed and in the second part (part B) the model-based and transform-based methods will be presented. Statistical methods work based on statistical quantities that obtained from image pixels' intensities, while structural methods operate based on texture primitives (a group of pixels with almost the same intensities). In the present paper, the applications of statistical image texture evaluation methods in food industries were investigated. Therefore, at first mechanisms of different statistical texture evaluation methods have been presented. Then, examples of recent studies related to employments of statistical image texture in quality control of food products have been reviewed. The results of the previous studies indicate that statistical methods are the most popular texture evaluation methods in food industries. This popularity is due to their highly accurate performances.


کلید واژگان:
بافتار تصویر، صنایع غذایی، روش‌های آماری، ماتریس هم-وقوعی، ماتریس طول پیمایش پیکسل، بینایی ماشین.

English Keywords:
Image texture, Food industries, Statistical methods, Co-occurrence matrix, Pixel run length matrix, Machine vision.

منابع:

English References:
1. T. Brosnan, D. W. Sun, "Improving quality inspection of food products by computer vision-a review", J. Food Eng., 61, 3–16, 2004. 2. R. L. Shewfelt, B. Bruckner, "Fruit and vegetable quality: an integrated view", 1st ed., USA, CRC Press, 2000. 3. F. J. Francis, "Color quality evaluation of horticultural crops", HortScience USA, 15, 58-59, 1980. 4. R. D. Tillett, "Image analysis for agricultural processes Divsion Note DN 1585", Silsoe Res. Inst., 1990. 5. D. J. He, Q. Yang, S. P. Xue, N. Geng, "Computer vision for colour sorting of fresh fruits", Trans. Chin. Soc. Agric. Eng., 14, 202–205, 1998. 6. Q. Z. Li and M. H. Wang, "Development and prospect of real time fruit grading technique based on computer vision", Trans. Chin. Soc. Agric. Mach., 30, 1–7, 1999. 7. D. W. Sun, "Inspecting pizza topping percentage and distribution by a computer vision method", J. Food Eng., 44, 245–249, 2000. 8. H. H. Wang, D. W. Sun, "Evaluation of the functional properties of cheddar cheese using a computer vision method", J. Food Eng., 49, 49–53, 2001. 9. M. Sonka, V. Hlavac, R. Boyle, "Image processing, analysis, and machine vision", 4th ed., USA, Cengage Learning, 2014. 10. C. Wilkinson, G. B. Dijksterhuis, and M. Minekus, “From food structure to texture”, Trends Food Sci. Technol., 11, 442–450, 2000. 11. J. Li, J. Tan, P. Shatadal, "Classification of tough and tender beef by image texture analysis", Meat Sci., 57, 341–346, 2001. 12. H. Kaizer, "A quantification of textures on aerial photographs", Tech Note 121, 1955. 13. R. M. Haralick, K. Shanmugam, I. H. Dinstein, "Textural features for image classification", Syst. Man Cybern. IEEE Trans. On, 6, 610–621, 1973. 14. R. M. Haralick, "Statistical and structural approaches to texture", Proc. IEEE, 67, 786–804, 1979. 15. S. Ghodrati, M. Mohseni, S. Gorji Kandi, "Dependence of adhesion strength of an acrylic clear coat on fractal dimension of abrasive blasted surfaces using image processing", 6th International Congress on Color and Coatings, 137, Tehran, Iran, 2015. 16. A. P. Pentland, "Fractal-based description of natural scenes", Pattern Anal. Mach. Intell. IEEE Trans. On, 6, 661–674, 1984. 17. A. R. Backes, D. Casanova, O. M. Bruno, "Plant leaf identification based on volumetric fractal dimension", Int. J. Pattern Recognit. Artif. Intell., 23, 1145–1160, 2009. 18. L. M. Kaplan, "Extended fractal analysis for texture classification and segmentation", Image Process. IEEE Trans. On, 8, 1572–1585, 1999. 19. R. Lopes, P. Dubois, I. Bhouri, M. H. Bedoui, S. Maouche, N. Betrouni, "Local fractal and multifractal features for volumic texture characterization", Pattern Recognit., 44, 1690–1697, 2011. 20. Y. Xu, H. Ji, C. Fermüller, "Viewpoint invariant texture description using fractal analysis", Int. J. Comput. Vis., 83, 85–100, 2009. 21. S. G. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation", Pattern Anal. Mach. Intell. IEEE Trans. On, 11, 674–693, 1989. 22. R. Azencott, J.-P. Wang, L. Younes, "Texture classification using windowed Fourier filters", Pattern Anal. Mach. Intell. IEEE Trans. On, 19, 148–153, 1997. 23. A. Laine, J. Fan, "Texture classification by wavelet packet signatures", Pattern Anal. Mach. Intell. IEEE Trans. On, 15, 1186–1191, 1993. 24. S. G. Kandi, "Machine vision analysis for textile texture identification", Fibres Text. East. Eur., 6, 53–57, 2011. 25. A. Conci, C. B. Proença, "A fractal image analysis system for fabric inspection based on a box-counting method", Comput. Netw. ISDN Syst., 30, 1887–1895, 1998. 26. O. G. Cula, K. J. Dana, F. P. Murphy, B. K. Rao, "Skin texture modeling", Int. J. Comput. Vis., 62, 97–119, 2005. 27. S. Tominaga and others, "Image analysis and synthesis of skin color textures by wavelet transform", Interpretation, 2006 IEEE Southwest Symposium on, 193–197, 2006. 28. C. Zheng, D. W. Sun, L. Zheng, "Recent applications of image texture for evaluation of food qualities-a review", Trends Food Sci. Technol., 17, 113–128, 2006. 29. R. Quevedo, L. G. Carlos, J. M. Aguilera, L. Cadoche, "Description of food surfaces and microstructural changes using fractal image texture analysis", J. Food Eng., 53, 361–371, 2002. 30. X. Gao, J. Tan, "Analysis of expanded-food texture by image processing part I: geometric properties", J. Food Process Eng., 19, 425–444, 1996. 31. J. Li, J. Tan, F. A. Martz, H. Heymann, "Image texture features as indicators of beef tenderness", Meat Sci., 53, 17–22, 1999. 32. D. D. Day, D. Rogers, "Fourier-based texture measures with application to the analysis of the cell structure of baked products", Digit. Signal Process., 6, 138–144, 1996. 33. J. Paliwal, N. S. Visen, D. S. Jayas, N. D. G. White, "Cereal grain and dockage identification using machine vision", Biosyst. Eng., 85, 51–57, 2003. 34. J. Paliwal, N. S. Visen, D. S. Jayas, N. D. G. White, "Comparison of a neural network and a non-parametric classifier for grain kernel identification", Biosyst. Eng., 85, 405–413, 2003. 35. N. Kondo, U. Ahmad, M. Monta, H. Murase, "Machine vision based quality evaluation of Iyokan orange fruit using neural networks", Comput. Electron. Agric., 29, 135–147, 2000. 36. W. Qiu, S. A. Shearer, "Maturity assessment of broccoli using the discrete Fourier transform", Trans. ASAE, 35, 2057–2062, 1992. 37. A. K. Thybo, P. M. Szczypi?ski, A. H. Karlsson, S. D?nstrup, H. S. St?dkilde-J?rgensen, H. J. Andersen, "Prediction of sensory texture quality attributes of cooked potatoes by NMR-imaging (MRI) of raw potatoes in combination with different image analysis methods", J. Food Eng., 61, 91–100, 2004. 38. H. H. Wang, D. W. Sun, "Correlation between cheese meltability determined with a computer vision method and with Arnott and Schreiber tests", J. Food Sci., 67, 745–749, 2002. 39. N. R. Sarkar, "Machine vision for quality control in the food industry", Instrum. Methods Qual. Assur. Foods, 167–187, 1991. 40. A. R. Novini, "The latest in vision technology in today’s food and beverage container manufacturing industry", Tech. Pap.-Soc. Manuf. Eng- Ser., 1995. 41. S. Gunasekaran, "Computer vision technology for food quality assurance", Trends Food Sci. Technol., 7, 245–256, 1996. 42. B. G. Bachelor, "Lighting and viewing techniques in automated visual inspection", Comput. Electron. Agric., 20, 117–130, 1985. 43. P. Wallin, P. Haycock, "Foreign body prevention, detection and control", 1st ed., USA, Blackie Academic & Professional, 1998. 44. S. Gunasekaran, K. Ding, "Using computer vision for food quality evaluation", Food Technol., 1994. 45. E. R. Davies, "Computer and machine vision: theory, algorithms, practicalities", 4th ed., USA, Academic Press, 2012. 46. G. W. Krutz, H. G. Gibson, D. L. Cassens, Z. Min, and others, "Colour vision in forest and wood engineering", Landwards, 55, 2–9, 2000. 47. M. H. Bharati, J. J. Liu, J. F. MacGregor, "Image texture analysis: methods and comparisons", Chemom. Intell. Lab. Syst., 72, 57–71, 2004. 48. C. Palm, "Color texture classification by integrative co-occurrence matrices", Pattern Recognit., 37, 965–976, 2004. 49. S. Majumdar, D. S. Jayas, "Classification of cereal grains using machine vision: III. Texture models", Trans. ASAE, 43, 1681-1687, 2000. 50. S. Ghodrati, "Investigation of the image processing methods for surface fractal dimension calculation and its relation with surface roughness and organic coatings adhesion", Master, Amirkabir University of Technology, Tehran, Iran, 2016. 51. K. Shiranita, T. Miyajima, R. Takiyama, "Determination of meat quality by texture analysis", Pattern Recognit. Lett., 19, 1319–1324, 1998. 52. E. Cernadas, P. Carri?n, P. G. Rodr?guez, E. Muriel, T. Antequera, "Analyzing magnetic resonance images of Iberian pork loin to predict its sensorial characteristics", Comput. Vis. Image Underst., 98, 344–360, 2005. 53. X. Gao, J. Tan, "Analysis of expanded-food texture by image processing part II: mechanical properties", J. Food Process Eng., 19, 445–456, 1996. 54. M. M. Galloway, "Texture analysis using gray level run lengths", Comput. Graph. Image Process., 4, 172–179, 1975. 55. A. Fardet, P. M. Baldwin, D. Bertrand, B. Bouchet, D. J. Gallant, J. L. Barry, "Textural images analysis of pasta protein networks to determine influence of technological processes", Cereal Chem., 75, 699–704, 1998. 56. C. Sun, W. G. Wee, "Neighboring gray level dependence matrix for texture classification", Comput. Vis. Graph. Image Process., 23, 341–352, 1983. 57. O. C. Rotunno Filho, P. M. Treitz, E. D. Soulis, P. J. Howarth, N. Kouwen, "Texture processing of synthetic aperture radar data using second-order spatial statistics", Comput. Geosci., 22, 27–34, 1996. 58. O. Basset, B. Buquet, S. Abouelkaram, P. Delachartre, J. Culioli, "Application of texture image analysis for the classification of bovine meat", Food Chem., 69, 437–445, 2000. 59. B. Park, K. C. Lawrence, W. R. Windham, Y.-R. Chen, K. Chao, "Discriminant analysis of dual-wavelength spectral images for classifying poultry carcasses", Comput. Electron. Agric., 33, 219–231, 2002. 60. J. D. Mccauley, B. R. Thane, A. D. Whittaker, "Fat estimation in beef ultrasound images using texture and adaptive logic networks", Trans. ASAE, 37, 997–1002, 1994. 61. I. Kavdir, D. E. Guyer, "Apple sorting using artificial neural networks and spectral imaging", Trans. ASAE, 45, 1995-2005, 2002. 62. H. L. Zhang, D. E. Wilson, G. H. Rouse, "Frequency and intensity texture analysis for beef quality evaluation and prediction from ultrasound images", Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE, 668–669, 1994. 63. B. Park, Y. R. Chen, "AE-automation and emerging technologies: co-occurrence matrix texture features of multi-spectral images on poultry carcasses", J. Agric. Eng. Res., 78, 127–139, 2001. 64. X. Gao, J. Tan, P. Shatadal, H. Heymann, "Evaluating expanded-food sensory properties by image analysis", J. Texture Stud., 30, 291–304, 1999.



فایل مقاله
تعداد بازدید: 1312
تعداد دریافت فایل مقاله : 23