Monocular vision-based ternary image calibration method

Abstract

The invention discloses a monocular vision-based ternary image calibration method. The method comprises the steps of performing further adjustment on a genetic algorithm, and enabling the adaptive genetic algorithm to have relatively high universality in each time period of population evolution by correcting a hybridization probability Pc and a mutation probability Pm; secondly, on the basis of referring to a radial basis neural network algorithm, improving a radial basis neural network by applying the improved genetic algorithm, thereby further reducing a fault classification problem training error of the radial basis neural network algorithm and realizing better training convergence; and finally, reconstructing an image through a super-resolution image reconstruction mathematic processing method, thereby remarkably improving the definition of the obtained low-resolution image.

Description

technical field [0001] The invention relates to the technical field of three-dimensional image processing, in particular to a three-dimensional image calibration method based on monocular vision. Background technique [0002] The traditional genetic algorithm is also called the standard genetic algorithm (SGA). When applied to the optimal segmentation of a three-dimensional image, its advantage is that it can mutate and optimize the target gray level even under complex backgrounds. This method also has some disadvantages, such as: the fixed crossover rate Pc and mutation rate Pm used in the crossover and mutation links can easily lead to too fast convergence of the population, falling into convergence, and insufficient stability. SGA does not make enough use of the feedback information in the system. In the later stage of the optimization process, a large number of unnecessary redundant iterations are required. The efficiency of finding an accurate solution is relatively low...

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Problems solved by technology

This method also has some disadvantages, such as: the fixed crossover rate Pc and mutation rate Pm used in the crossover and mutation links can easily lead to too fast convergence of the population, falling into convergence, and insufficient stability

SGA does not make enough use of the feedback information in the system. In the later stage of the optimization process, a large number of unnecessary redundant iterations are required. The efficiency of finding an accurate solution is relatively low, and it is difficult to maintain strong robustness under the premise of a faster convergence speed.

Applied in the field of three-dimensional image processing technology, the processed three-dimensional image has insufficient clarity

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