Application of machine vision based on genetic algorithm in image painting style method and image processing optimization

Abstract

In recent years, the research on the non photorealistic painting of watercolor painting through computer algorithms has made rapid progress, and its speci c application has also been increasingly valued. This paper proposes a new machine vision method based on genetic algorithm, and applies it to the research of oil painting style and image processing optimization. First of all, it provides a stroke layout method based on machine vision important areas. It analyzes the key areas through face, visual focus analysis and other technologies, and realizes the starting point, angle calculation, accuracy optimization and image quality optimization of pen painting based on the data of this area, thus realizing the function of distinguishing the primary and secondary of oil painting. Secondly, the machine vision method based on genetic algorithm can explore the style characteristics of any oil painting graphics according to the characteristics of oil painting, and create artistic images through this method. The experimental resultsshow that the machine vision method based on genetic algorithm not only has achieved good results in the practical application of oil painting style, but also can optimize the image processing.

Genetic Algorithm Based on Operator Optimization in Illustration Art Design

“…The main idea behind Genetic Algorithm Based on Operator Optimization is to develop and optimize genetic operators like crossover and mutation to better meet the needs of illustration art design [7]. These operators play an important role in the evolution of solutions within a genetic algorithm by emulating the mechanisms of genetic recombination and mutation found in natural evolution [8].…”