An edge detection technique using genetic algorithm-based optimization

Pattern Recognition

Volumn 27, Issue 9, 1994, Pages 1159-1180

  Bhandarkar, Suchendra M ^a   Zhang, Yiqing ^a   Potter, Walter D ^a  

^a UNIVERSITY OF GEORGIA   (United States)

Author keywords

Cost minimization; Edge detection; Genetic algorithm

Indexed keywords

ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; DATA STRUCTURES; IMAGE ANALYSIS; IMAGE QUALITY; MATHEMATICAL OPERATORS; OPTIMIZATION; ROBUSTNESS (CONTROL SYSTEMS);

COST MINIMIZATION; EDGE DETECTION; GENETIC ALGORITHMS;

COMPUTER VISION;

EID: 0028496754     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/0031-3203(94)90003-5     Document Type: Article

References (26)

1. On edge detection
2. A study of edge detection algorithms
3. Optimum edge detection filter
4. A computational approach to edge detection
5. Discontinuity detection and thresholding-a stochastic approach
6. An optimal linear operator for step edge detection
7. Theory of edge detection
8. Residual analysis for feature detection
9. Digital step edges from zero crossing of second directional derivatives
10. On detecting edges
11. Scale space and edge detection using anisotropic diffusion
12. Adaptive smoothing: a general tool for early vision
13. The contour extraction problem with biomedical applications
14. On the optimal detection of curves in noisy pictures
15. An application of heuristic search to edge and contour detection
16. A comparative cost function approach to edge detection
17. A cost minimization approach to edge detection using simulated annealing
18. Anisotropic edge detection using mean field annealing
19. Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images
20. Optimization by simulated annealing
21. Genetic Algorithms and Simulated annealing: an overview
22. Probabilistic Solution of Ill-Posed Problems in Computational Vision
23. A genetic algorithms tutorial
24. Improving the reliability of heuristic multiple fault diagnosis via the EC-based genetic algorithm