Study On Image Matching Based On Hybrid Genetic Algorithm

Image matching is an important area in image processing, and it is also one of the difficulties. Image matching is the foundation of computer vision applications, such as camera calibration,three-dimensional reconstruction,intelligent monitoring and motion analysis. There are mainly two kinds of image matching methods, one is feature-based and the other is grayscale-based. The feature-based method uses the physical feature for image matching, such as edges, skeleton lines and corners. The computational complexity of this method is low, but it is only suitable for simple images with significant geometric features. The grayscale-based method performs high in accuracy, but the computational complexity of this method is high. The grayscale-based method usually uses the template matching method, and the conventional template matching method is inefficient and time-consuming, the sequential similarity detection algorithm (SSDA) is more efficient, but still not meet the requirements of real-time. Genetic algorithm (GA) is an evolutionary algorithm for global optimization search with the implicit parallelism. GA is based on schema theorem and building block hypothesis. GA provides a general model to solve the complex optimization problems. The speed of image matching is enhanced because of the global search ability and the implicit parallelism of GA. The global search ability of GA determines its faster speed of the search process from the random value to the sub-optimal value, and its weaker local search ability leads to the inefficiency from the sub-optimal value to optimal value, and the accuracy of GA is not high because of its randomness.This article introduces a hybrid genetic algorithm by analyzing the disadvantage of the standard genetic algorithm. The hybrid genetic algorithm combines GA and hill-climbing algorithm in an efficient way and performs fast both in global search and local search. The algorithm also uses the rough match and exact match in different match stage, thus the matching speed is further improved. The article also studied the situation when the image was rotated, and proposed an anti-rotation image matching algorithm. The algorithm uses the histogram future of the ring and circle area in the template image and referenced image, and uses the hybrid genetic algorithm in the search process, thus the matching speed of rotated image is enhanced greatly.