Research on Image Vectorization Based on Improved Chaos Immune Genetic Algorithm

Abstract:

Image vectorization plays an important role in the digital image processing. Because the traditional linear vectorization methods have some shortcomings including processing data slowly, being sensitive to noises and being easy to be distorted, this paper proposes an image vectorization method based on mathematical morphology. In the paper we present an improving immune genetic algorithm based on chaos theory. The over-spread character and randomness of chaos can be used to initialize population and improve the searching speed, and the initial value sensitivity of chaos can be used to enlarge the searching space. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence.

• Periodical: Applied Mechanics and Materials (Volumes 543-547)
• Pages: 2619-2622
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.543-547.2619
• Online since: March 2014
• Authors: Shao Song Wan*, Jian Cao, Cong Yan
• Keywords: Algorithm (GA), Chaos, Image, Vectorization

References

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