Abstract
Image matching is an important topic in image processing. Matching technology plays an important role in and is the basis for image understanding. In order to solve the shortcomings of slow image matching and low matching accuracy, a matching method based on improved genetic algorithm is proposed. The main improvement of the algorithm is the use of self-identifying crossover operators for crossover operations to avoid premature population maturity. According to the characteristics of the image data, new intersection and mutation operators are defined by the new coding method. The sampling method is used to initialize the population method, introduce an evolution strategy, reduce the number of iterations, and effectively reduce the amount of calculation. The experimental results show that the algorithm can guarantee the matching accuracy and that the calculation time is much shorter than that of the original algorithm. In addition, the image matching calculation time per frame of the algorithm is basically unchanged, which is convenient for engineering applications.
Highlights
Image matching is an important subject in image processing
When the seeding is far away from the local optimum area, the initialization effect is not as good as the uniform seeding effect, plus the calculation amount, the number of iterations is insufficient, and the population does not converge or only converges to the local optimum. erefore, this paper proposes an effective genetic algorithm initialization strategy—a comprehensive strategy for controlling various initial methods of population spacing. e basic idea is to form a set of total M x N seed points (N is the population size) obtained by the M initialization method
For the same matching effect, the speed of the genetic algorithm is more than an order of magnitude higher than that of the SSDA algorithm, and the matching time is basically constant
Summary
Image matching is an important subject in image processing. It has broad application prospects in computer vision, moving target tracking and recognition, motion compensation in sequence image compression, and medical image processing. Matching technology plays an important role in understanding images. Image correlation matching tracking techniques are the basic means by which optoelectronic imaging systems track moving and stationary surfaces. Image matching technology has become more and more prominent in the field of image processing. It has been widely used in many fields and has high research value and application value [2, 3]. Using the super parallel processing capabilities of FPGAs, some image matching algorithms can be used [4, 5].
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