AN EDGE-DETECTION TECHNIQUE USING GENETIC ALGORITHM-BASED OPTIMIZATION

In this paper we present a genetic algorithm-based optimization technique for edge detection. The problem of edge detection is formulated as one of choosing a minimum cost edge configuration. The edge configurations are viewed as two-dimensional chromosomes with fitness values inversely proportional to their costs. The design of the crossover and the mutation operators in the context of the two-dimensional chromosomal representation is described. The knowledge-augmented mutation operator which exploits knowledge of the local edge structure is shown to result in rapid convergence. The incorporation of meta-level operators and strategies such as the elitism strategy, the engineered conditioning operator and adaptation of mutation and crossover rates in the context of edge detection are discussed and are shown to improve the convergence rate. The genetic algorithm with various combinations of meta-level operators is tested on synthetic and natural images. The performance of the genetic algorithm-based cost minimization technique is compared both qualitatively and quantitatively with local search-based and simulated annealing-based cost minimization approaches. The genetic algorithm-based technique is shown to perform very well in terms of robustness to noise, rate of convergence and quality of the final edge image.