Journal of Power System Engineering (동력기계공학회지)

The Korean Society for Power System Engineering (한국동력기계공학회)
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Fish Injured Rate Measurement Using Color Image Segmentation Method Based on K-Means Clustering Algorithm and Otsu's Threshold Algorithm

  • Sheng, Dong-Bo (Department of Mechanical Design Engineering, Pukyong National University) ;
  • Kim, Sang-Bong (Department of Mechanical Design Engineering, Pukyong National University) ;
  • Nguyen, Trong-Hai (Department of Mechanical Design Engineering, Pukyong National University) ;
  • Kim, Dae-Hwan (Department of Mechanical Design Engineering, Pukyong National University) ;
  • Gao, Tian-Shui (Department of Mechanical Design Engineering, Pukyong National University) ;
  • Kim, Hak-Kyeong (Department of Mechanical Design Engineering, Pukyong National University)
  • Received : 2016.04.12
  • Accepted : 2016.08.10
  • Published : 2016.08.31
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Abstract

This paper proposes two measurement methods for injured rate of fish surface using color image segmentation method based on K-means clustering algorithm and Otsu's threshold algorithm. To do this task, the following steps are done. Firstly, an RGB color image of the fish is obtained by the CCD color camera and then converted from RGB to HSI. Secondly, the S channel is extracted from HSI color space. Thirdly, by applying the K-means clustering algorithm to the HSI color space and applying the Otsu's threshold algorithm to the S channel of HSI color space, the binary images are obtained. Fourthly, morphological processes such as dilation and erosion, etc. are applied to the binary image. Fifthly, to count the number of pixels, the connected-component labeling is adopted and the defined injured rate is gotten by calculating the pixels on the labeled images. Finally, to compare the performances of the proposed two measurement methods based on the K-means clustering algorithm and the Otsu's threshold algorithm, the edge detection of the final binary image after morphological processing is done and matched with the gray image of the original RGB image obtained by CCD camera. The results show that the detected edge of injured part by the K-means clustering algorithm is more close to real injured edge than that by the Otsu' threshold algorithm.

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References

  1. C. J. Du and D. W. Sun, 2003, "Recent Developments in The Applications of Image Processing Techniques for Food Quality Evaluation", Trends in Food Science & Technology, Vol. 15, No. 5, pp. 230-249. https://doi.org/10.1016/j.tifs.2003.10.006
  2. Q. Z. Li, M. H. Wang and W. K. Gu, 2002, "Computer Vision Based System for Apple Surface Defect Detection", Computers and Electronics in Agriculture, Vol. 36, No. 2, pp. 215-223. https://doi.org/10.1016/S0168-1699(02)00093-5
  3. T. McDonald and Y. R. Chen, 1990, " Separating Connected Muscle Tissues in Image of Beef Carcass Ribeyes", Transactions of the ASAE, Vol. 33, No. 6, pp. 2059-2065.
  4. F. Storbeck and B. Daan, 2001, "Fish Species Recognition Using Computer Vision and a Neural Network", Fisheries Research, Vol. 51, No. 1, pp. 11-15. https://doi.org/10.1016/S0165-7836(00)00254-X
  5. H. F. Ng, 2006, "Automatic Thresholding for Defect Detection," Pattern Recognition Letters, Vol. 27, No. 14, pp. 1644-1649. https://doi.org/10.1016/j.patrec.2006.03.009
  6. H. Chakravorty, R. Paul and P. Das, 2015, "Image Processing Technique to Detect Fish Disease", International Journal of Computer Science and Security (IJCSS), Vol. 9, No. 2, pp. 121-131.
  7. S. Ray and R. H. Rose, 1999, "Determination of Number of Clusters in K-means in Clustering and Application in Colour Image Segmentation", Proceeding of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques, pp. 137-143.
  8. N. Otsu, 1979, "A Threshold Selection Method from Gray-level Histograms", IEEE Transaction on Systems, Man and Cybernetics, Vol. 9, No. 1, pp. 62-66. https://doi.org/10.1109/TSMC.1979.4310076
  9. M. Barni, V. Cappellini and A. Mecocci, 1997, "Color-based Detection of Defects on Chicken Meat", Image and Vision Computing, Vol. 15, No.7, pp. 549-556. https://doi.org/10.1016/S0262-8856(97)01138-4
  10. K. Shiranita, K. Hayashi, A. Otsubo, T. Miyajima and R. Takiyama, 2000, "Grading Meat Quality by Image Processing", Pattern Recognition, Vol. 33, No.1, pp. 97-104. https://doi.org/10.1016/S0031-3203(99)00035-7
  11. F. Yoshikawa, K. Toraichi, K. Wada, N. Ostu, H. Nakai, M. Mitsumoto and K. Katagishi, 2000, "On a Grading System for Beef Marbling", Pattern Recognition Letters, Vol. 21, No. 12, pp. 1037-1050. https://doi.org/10.1016/S0167-8655(00)00058-1
  12. K. Shiranita, T. Miyajima and R. Takiyama, 1998, "Determination of Meat Quality by Texture Analysis", Pattern Recognition Letters, Vol. 19, No. 14, pp. 1319-1324. https://doi.org/10.1016/S0167-8655(98)00113-5