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Proceedings Paper

Image segmentation based on data field and cloud model
Author(s): Kun Qin; Leihai Ou; Tao Wu; Yi Du
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Paper Abstract

There are many uncertainties in image segmentation, which needs theories and methods with uncertainty to handle. This paper proposes a novel method of image segmentation based on data field and cloud model, which considers the spatial information of image through data field, and handles the uncertainty of image through cloud model. The proposed method inspired from cognitive physics considers each pixel as a physical object, calculates the interactive force of these physical objects, and generates image data field and the potential values which are considered as spatial information. And then, uses cloud transformation and magnitude cloud synthesis to extract the concepts of potential-frequency histogram from low level to high level, realizes the clustering of pixels, finally uses maximum determination to partition the pixels into different classes and segment image into different regions. Results of many experiments indicate that the proposed method obtains better effect than those of Fuzzy C-means clustering, Otsu and cloud based hierarchical method, and it is feasible and effective.

Paper Details

Date Published: 20 August 2010
PDF: 8 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78202D (20 August 2010); doi: 10.1117/12.866958
Show Author Affiliations
Kun Qin, Wuhan Univ. (China)
Leihai Ou, Wuhan Univ. (China)
Tao Wu, Wuhan Univ. (China)
Yi Du, Communication Network Technology Management Ctr. (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering
Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du, Editor(s)

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