Abstract
In order to effectively solve the problems of unreasonable design and low evaluation level in the process of fractal art pattern design and evaluation, a CAD fractal art pattern design and evaluation is proposed based on RBF network algorithm. Through the corresponding sequence sorting of RBF network algorithm, the image surface with the same CAD fractal art pattern design and evaluation attributes is divided into two parts. The main shape in the algorithm is mainly the node of feature tree. According to the later stage of image features, using high-level design basic knowledge and evaluation methods, RBF network algorithm is introduced into the process of original image design modification. Finally, the experimental results show that the algorithm used in this paper has certain advantages in CAD fractal art pattern design and evaluation.
Highlights
Basic Concepts and Characteristics of Fractal Art GraphicsFractal is a kind of art graphics with different rules, fragmented and irregular [6,7,8]
According to CAD fractal art pattern to spread the essence of culture and users’ cognition of culture, this paper describes the design of CAD fractal art pattern from the perspective of extracting cultural resource graphics and designs and evaluates CAD fractal art pattern
Fractal is a kind of art graphics with different rules, fragmented and irregular [6,7,8]
Summary
Fractal is a kind of art graphics with different rules, fragmented and irregular [6,7,8]. It is mainly a way with irregular geometric form and complex graphic structure. E fractal art graphics composed of different rules constitute the objective world. Is paper presents a CAD fractal art pattern design method based on RBF network algorithm. E RBF network algorithm is used for preprocessing, and the superpixel block is treated as a node to improve the color space, reduce the background area during repetition, ensure the accuracy, and greatly reduce the time consumption during the repetition of the algorithm Screen the test images to reduce the difference between the original image and the compressed image, so as to improve the tester’s overall subjective feeling ability. e RBF network algorithm is used for preprocessing, and the superpixel block is treated as a node to improve the color space, reduce the background area during repetition, ensure the accuracy, and greatly reduce the time consumption during the repetition of the algorithm
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