원문정보
초록
영어
Aiming at the existing edge detection algorithm of edge vague, the pseudo-edge cannot be removed and algorithm results do not achieve optimal results by virtue. In order to improve the reliability and effectiveness of edge detection, the proposed optimization tool template coefficient method, to design the coding, Sobel filter and fitness function of genetic fuzzy clustering algorithm. Through interpolating, smooth handling and filtering with the updated active contour model. Based on the traditional edge detection algorithm is analyzed, combined with fuzzy membership functions and genetic operators for edge detection algorithm was improved by genetic fuzzy clustering. Through the simulation results showed that this new algorithm was feasible. Theoretical analysis and experimental results demonstrate that, the new algorithm in this paper is highly antinoise and able to get better image edges.
목차
1. Introduction
2. Genetic Fuzzy Clustering Algorithm
3. Improved Algorithm Based on Genetic Fuzzy Clustering
3.1. The Improved Edge Template
3.2. Coding Design
3.3. Fitness Function Design
3.4. Genetic Operators Design
3.5. Fuzzy Sets and Fuzzy Membership Functions
3.6. Sobel Filter Design
4. Experimental Results and Analysis
5. Conclusion
ACKNOWLEDGEMENTS
References