Improved Watershed Algorithm Based on Morphology and Distance Transform

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In this paper we proposed an improved watershed algorithm for the quasi-circle overlapping images of the bars end face. According to the classical watershed algorithm, which often causes over-segmentation, the improved algorithm does a series of pretreatment with the original image, such as sobel filter. With the gradient operator and mathematical morphology method, we firstly obtain the smooth image of the forced local maximum marks. Then, on the basis of the quasi-circle characteristic of the target image, we proceed to maximize the erosion with circular structure in order to prevent under-segmentation. Finally, we use the watershed algorithm to segment the gray image based on distance transform. So we can separate the target from each other to achieve the accurate counting purpose. By using the proposed algorithm in the article, we obtain satisfactory segmentation results of the quasi-circle overlapping image of the bars end face image.

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1071-1075

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July 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Peng, Z.T., Fang, K.L., Su Z.Q. (2011).

Google Scholar

[2] Wei, D.D., & Zhao, Y.H. (2010). Based on Concave Points Matching the Overlapping Image Segmentation Algorithm. Computers and Applied Chemistry. 27 (1): 99-102. (In Chinese).

Google Scholar

[3] Ji, B.J., Lv, J.P. (2009). Image Segmentation Based on the Gradient Reconstruction and Morphological Watershed Algorithm. Communication technologies. 05 (42): 98-100. (In Chinese).

Google Scholar

[4] You, Y.R., FAN Y.L., Pang, Q. (2005). Adherent Cells Segmentation Method based on Distance Transform. Computer Engineering and Applications. 41 (20): 206-209. (In Chinese).

Google Scholar

[5] Gao, J. (2008). Cell Image Segmentation Based on Morphological Watershed Algorithm. Jilin University, a master's degree thesis. 43-44. (In Chinese).

Google Scholar

[6] Rafael C. Gonzalez, Richard E. Woods (2002). Digital Image Processing. (Second Edition). Pearson Education Asia Limited. China: Publishing House of Electronics Industry.

Google Scholar

[7] Feng, Z.F., Fang, K.L. (2009). Bars Automatic Counting Method Based on the Active Contour Model Realization [J]. Mechanical and electronic. (11): 10-14. (In Chinese).

Google Scholar