<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To understand the deep learning model, the author proposed the research of interactive texture replacement of cartoon characters. For image segmentation, if you want to fill a cartoon without any texture in detail, or replace the unsatisfied texture area, first, we need to separate the filled or replaced area from the cartoon. For this reason, the traditional image segmentation algorithm has been carefully studied and analyzed, and the author chooses the Graphcut texture synthesis algorithm, the algorithm is parallelized and improved, and the innovative point of lighting customization is proposed based on the original algorithm, which can affect the synthesis effect according to the input lighting image. In terms of timeliness and synthesis effect, the Graphcut algorithm has been improved. Experimental results show that the algorithm can maintain the brightness distribution of the original cartoon and the practicability and efficiency of the algorithm proposed by the author.</p>
</abstract>

To understand the deep learning model, the author proposed the research of interactive texture replacement of cartoon characters. For image segmentation, if you want to fill a cartoon without any texture in detail, or replace the unsatisfied texture area, first, we need to separate the filled or replaced area from the cartoon. For this reason, the traditional image segmentation algorithm has been carefully studied and analyzed, and the author chooses the Graphcut texture synthesis algorithm, the algorithm is parallelized and improved, and the innovative point of lighting customization is proposed based on the original algorithm, which can affect the synthesis effect according to the input lighting image. In terms of timeliness and synthesis effect, the Graphcut algorithm has been improved. Experimental results show that the algorithm can maintain the brightness distribution of the original cartoon and the practicability and efficiency of the algorithm proposed by the author.

Interactive texture replacement of cartoon characters based on deep learning model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

To understand the deep learning model, the author proposed the research of interactive texture replacement of cartoon characters. For image segmentation, if...

Interactive texture replacement of cartoon characters based on deep learning model

Data publikacji: 07 lip 2023

Zakres stron: 3023 - 3032

Otrzymano: 01 sie 2022

Przyjęty: 13 sty 2023

DOI: https://doi.org/10.2478/amns.2023.2.00018

Słowa kluczowe
Deep learning, Cartoon characters, Interactive texture

© 2023 Anqiang Zhao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Interactive texture replacement of cartoon characters based on deep learning model

Data publikacji: 07 lip 2023

Zakres stron: 3023 - 3032

Otrzymano: 01 sie 2022

Przyjęty: 13 sty 2023

DOI: https://doi.org/10.2478/amns.2023.2.00018

Słowa kluczoweDeep learning, Cartoon characters, Interactive texture

© 2023 Anqiang Zhao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Deep learning, Cartoon characters, Interactive texture