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Fish Injured Rate Measurement Using Color Image Segmentation Method Based on K-Means Clustering Algorithm and Otsu's Threshold Algorithm
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Type
Academic journal
Author
Dong-Bo Sheng (Pukyong National University) Sang-Bong Kim (Pukyong National University) Trong-Hai Nguyen (Pukyong National University) Dae-Hwan Kim (Pukyong National University) Tian-Shui Gao (Pukyong National University) Hak-Kyeong Kim (Pukyong National University)
Journal
The Korean Society For Power System Engineering Journal of Power System Engineering Vol.20 No.4 KCI Accredited Journals
Published
2016.8
Pages
32 - 37 (6page)

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Fish Injured Rate Measurement Using Color Image Segmentation Method Based on K-Means Clustering Algorithm and Otsu's Threshold Algorithm
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Abstract· Keywords

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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.
#Image Segmentation #RGB #HSI #K-means Clustering Algorithm #Otsu's Threshold Algorithm

Contents

Abstract
1. Introduction
2. Image processing system
3. Experimental results
5. Conclusions
References

References (12)

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[학술지(정기간행물)] - C. J. Du / 2003 / Recent Developments in The Applications of Image Processing Techniques for Food Quality Evaluation / Trends in Food Science &Technology 15 (5) : 230 ~ 249 google schola [학술지(정기간행물)] - Q. Z. Li / 2002 / Computer Vision Based System for Apple Surface Defect Detection / Computers and Electronics in Agriculture 36 (2) : 215 ~ 223 google schola [학술지(정기간행물)] - T. McDonald / 1990 / Separating Connected Muscle Tissues in Image of Beef Carcass Ribeyes / Transactions of the ASAE 33 (6) : 2059 ~ 2065 google schola [학술지(정기간행물)] - F. Storbeck / 2001 / Fish Species Recognition Using Computer Vision and a Neural Network / Fisheries Research 51 (1) : 11 ~ 15 google schola [학술지(정기간행물)] - H. F. Ng / 2006 / Automatic Thresholding for Defect Detection / Pattern Recognition Letters 27 (14) : 1644 ~ 1649 google schola

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