Fast Covariance Matching With Fuzzy Genetic Algorithm

The exiting covariance matching method is not suited for real-time applications due to its demand for exhaustive search. Aiming at this problem, we developed a novel approach based on fuzzy genetic algorithm (GA) to boost the computing efficiency of covariance matching. The approach employs GA in searching for optimal solution in a large image region. To avoid premature convergence or local optimum which often occur in traditional GAs, we use a fuzzy inference system to adaptively estimate the crossover and mutation probabilities to gain convergence in a much higher speed than using a conventional GA. Experimental results show that the proposed approach can significantly improve the processing speed of covariance matching, while keeping the matching results almost unchanged. The runtime performance of the proposed approach is faster than its counterparts using exhaustive search with eight times and more.