Art Visual Image Transmission Method Based on Cartesian Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{DBLP:journals/sp/Zhao21b,
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author = "Jing Zhao",
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title = "Art Visual Image Transmission Method Based on
Cartesian Genetic Programming",
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journal = "Scientific Programming",
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volume = "2021",
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pages = "4628563:1--4628563:10",
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year = "2021",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
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ISSN = "1058-9244",
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URL = "
https://downloads.hindawi.com/journals/sp/2021/4628563.pdf",
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URL = "
https://doi.org/10.1155/2021/4628563",
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DOI = "
doi:10.1155/2021/4628563",
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timestamp = "Thu, 17 Feb 2022 00:00:00 +0100",
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biburl = "https://dblp.org/rec/journals/sp/Zhao21b.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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size = "10 pages",
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abstract = "Because most of the traditional artistic visual image
communication methods use the form of modeling and
calculation, there are some problems such as long image
processing time, low success rate of image visual
communication, and poor visual effect. An artistic
visual image communication method based on Cartesian
genetic programming is proposed. The visual expression
sensitivity difference method is introduced to process
the image data, the neural network is used to identify
the characteristics of the artistic visual image, the
midpoint displacement method is used to remove the
folds of the artistic visual image, and the processed
image is formed under the above three links. The
Cartesian genetic programming algorithm is used to
encode the preprocessed image, improve the fitness
function, select the algorithm to improve the
operation, design the image rendering platform, input
the processed image to the platform, and complete the
artistic visual image transmission. The analysis of the
experimental results shows that the image processing
time of this method is short, the success rate of
visual communication is high, and the image visual
effect is good, which can obtain the image processing
results satisfactory to users.",
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notes = "sp@hindawi.com",
- }
Genetic Programming entries for
Jing Zhao
Citations
