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Abstract

Computer vision technique is becoming popular for quality assessment of many products in food industries. Image enhancement is the first step in analyzing the images in order to obtain detailed information for the determination of quality. In this study, Brightness preserving histogram equalization technique was used to enhance the features of gray scale images to classify three date varieties (Khalas, Fard and Madina). Mean, entropy, kurtosis and skewness features were extracted from the original and enhanced images. Mean and entropy from original images and kurtosis from the enhanced images were selected based on Lukka's feature selection approach. An overall classification efficiency of 93.72% was achieved with just three features. Brightness preserving histogram equalization technique has great potential to improve the classification in various quality attributes of food and agricultural products with minimum features.

 

Keywords

Computer vision Brightness preserving histogram equalization Dates variety.

Article Details

How to Cite
Thomas, G., Manickavasagan, A., Khriji, L., & Al-Yahyai, R. (2014). Contrast Enhancement Using Brightness Preserving Histogram Equalization Technique for Classification of Date Varieties. The Journal of Engineering Research [TJER], 11(1), 55–63. https://doi.org/10.24200/tjer.vol11iss1pp55-63
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References

  1. Al-Marshudi AS (2002), Oman traditional date palms: Production and improvement of date palms in Oman. Tropicultura 20:203-209.
  2. Al-Rahbi S, Manickavasagan A, Al-Yahyai R, Khriji L, Alahakoon P (2013), Detecting surface cracks on dates using color imaging technique. Food Science and Technology Research 19:1-10.
  3. Blasco J, Aleixos N, Molto E (2007), Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering 81:535-543.
  4. Blasco J, Aleixos N, Cuero S, Comez-Sanchis J, Molto E (2009), Automatic sorting of (Citrus unshiu) segments using computer vision and morphological features. Computers and Electronics in Agriculture 66:1-8.
  5. Brosnan T, Sun D (2002), Inspection and grading of agricultural and food products by computer vision systems - a review. Computers and Electronics in Agriculture 36(2):193-213.
  6. Fadel M, Kurmestegy L, Rashed M, Rashed Z (2006), Fruit color properties of different cultivars of dates (Phoenix dactylifera, L.). Agricultural Engineering International-CIGR Journal VIII:1-9.
  7. Fadel MA (2008), Sugar content estimation of date (Phoenix dactylifera, L.) fruits in tamr stage. Agricultural Engineering International-CIGR Journal X:1-9.
  8. FAO (2007), FAO Statistics. Available at: http://faostat. fao.org/site/342/default.aspx. Accessed on February 24, 2013.
  9. Gonzalez RC, Woods RE (2001), Digital Image Processing, Prentice Hall, second edition, NJ, USA.
  10. Gunasekaran S (1996), Computer vision technology for food quality assurance. Trends in Food Science and Technology 7:245-256.
  11. Kang SP, East AR, Trujillo FJ (2008), Colour vision system evaluation of bicolour fruit: A case study with 'B74' mango. Postharvest Biology and Technology 49:77-85.
  12. Kenney JF, Keeping ES (1962), Moments in standard units. Mathematics of Statistics, Pt. 1, 3rd ed. Van Nostrand, Princeton, NJ, USA.
  13. Kondo N, Ahmad U, Monta M, Murase H (2000), Machine vision based quality evaluation of Iyokan orange fruit using neural networks. Computers and Electronics in Agriculture 29:135-147.
  14. Liming X, Yanchao Z (2010). Automated strawberry grading system based on image processing. Computer and Electronics in Agriculture 71:32- 39.
  15. Lee D, Archibald JK, Chang Y, Greco R (2008a), Robust color space conversion and color distribution analysis techniques for date maturity evaluation. Journal of Food Engineering 88:364-372.
  16. Lee D, Schoenberger R, Archibald J, McCollum S (2008b), Development of machine vision system for automatic date grading using digital reflective near-infrared imaging. Journal of Food Engineering 88:388-398.
  17. Leemans V, Magein H, Destain MF (2002), On-line fruit grading according to their external quality using machine vision. Biosystems Engineering 84:397-404.
  18. Luca D, Termini S (1971), A definition of non-probabilistic entropy in setting of fuzzy set theory. Information Control 20:301-312.
  19. Luukka P (2011), Feature selection using fuzzy entropy measures similarity classifier. Expert Systems with Applications 38(4):4600-4607.
  20. Manickavasagan A, Sathya G, Jayas DS (2008), Comparison of illuminations to identify wheat classes using monochrome images. Computers and Electronics in Agriculture 63(2):237-244.
  21. Manickavasagan A, Al-Yahyai R (2012), Quality assessment of dates by computer vision technology. In Dates - Production, Processing, Food and Medicinal Values Ed. Manickavasagan A, Essa MM, Sukumar E, PP-217-226, Taylor & Francis Group: New York, NY, USA.
  22. Manickavasagan A, Al-Shekaili HN, Thomas G, Rahman MS, Guizani N, Jayas DS (2013), Edge detection features to evaluate hardness of dates using monochrome images. Food and Bioprocess Technology (DOI 10.1007/s11947-013-1219-0).
  23. Otsu N (1979), A threshold selection method from gray-level histogram. IEEE Transactions on System Man Cybernetics 9(1):62-66.
  24. Prakash OM, Sharma PK, Maharajan (2008), New measures of weighed fuxxy entropy and their applications for the study of maximum weighted fuzzy entropy principle. Information Sciences 178:2389-2395.
  25. Shannon CE (1948), A mathematical theory of communication. Bell System Technical Journal 27(3):379-423.
  26. Wang C, Ye Z (2005), Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Transactions on Consumer Electronics 51(4):1326-1334.