Image Segmentation Algorithm Research for Sport Graphics Based on Artificial Life

Jian Min Qi; Guo Ying Liang

doi:10.4028/www.scientific.net/AMM.513-517.3354

Paper Titles

Design and Implementation of Remote Intelligent Control System Based on Cortex-M3
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Electromagnetic Response for Horizontal Electric Dipole Source Considering Conduct & Displacement Current
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Research on Combined Environmental Stress Test for Hybrid Integrated DC/DC Converter
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Sparse Representation of the Human Vision Information and the Saliency Detection Algorithm
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Image Segmentation Algorithm Research for Sport Graphics Based on Artificial Life
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A Novel Approach Based on Multi-Layer Background Subtraction and Seed Region Growing for Sport Graphics Segmentation
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Enhancement Algorithm of Color Fog Image Based on the Adaptive Scale and S-Cosine Curve
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517Image Segmentation Algorithm Research for Sport...

Image Segmentation Algorithm Research for Sport Graphics Based on Artificial Life

Abstract:

In this paper, linking with the basic principle of artificial life for image segmentation, on the basis of theory research, the segmented partitions emerge when the state of the lives reaches an equilibrium. The artificial life approach is promising in image processing because it is inherently parallel and coincides with the self-governing biological process. The final segmentation result is achieved at last. The experiments demonstrate the feasibility of the artificial life approach on both intensity images and color images. The experiment results prove that in the view of the sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

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Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

3354-3357

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.3354

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Online since:

February 2014

Authors:

Jian Min Qi*, Guo Ying Liang

Keywords:

Artificial Life, Image Segmentation, Sport Graphics

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References

[1] Yujin ZHANG. Image Processing and Analysis [M], Beijing: Tsinghua University Press, (1999).

Google Scholar

[2] A. Rybak, V. I. Gusakova, A. Golovan, L. N. Podladchikova and N. A. Shevtsova, A model of attention-guided visual perception and recognition, Vis. Res. 38. 2009, 2387-2400.

DOI: 10.1016/s0042-6989(98)00020-0

Google Scholar

[3] Bezdek J C, Hathaway R J. Progressive sampling schemes for approximate clustering in very large data sets, [C]. Proceedings of 2004 IEEE International Conference on Fuzzy Systems, 2004, 1: 15-21.

DOI: 10.1109/fuzzy.2004.1375677

Google Scholar

[4] Chuang K S, Tzeng H L , Chen S, etal. Fuzzy c-means clustering with spatial information for image segmentation [J]. Computerized Medical Imaging and Graphics, 2006, 30: 9216.

DOI: 10.1016/j.compmedimag.2005.10.001

Google Scholar

[5] Z. Ameur, Image coding in view of high level segmentation : Application to satellite images [Codage des images en vue d'une segmentation de haut niveau: Application aux images satellitaires], Ph. D, Algeria, Tizi Ouzou : Mouloud Mammeri University, September (2005).

DOI: 10.15676/ijeei.2014.6.1.7

Google Scholar

[6] Thitimajshima, P. A new modified fuzzy c- means algorithm for multispectral satellite images segmentation , [C]. Proceedings of Geoscience and Remote Sensing Sy mposium Honolulu . HI, 2000(4), 1684-1686.

DOI: 10.1109/igarss.2000.857312

Google Scholar