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

<p>In order to make a deeper research on the existing animation character recognition technology and improve the existing role intelligent analysis technology, semantic ontology and Poisson equation are combined to apply to the animation character recognition and role intelligent analysis technology. For the three-dimensional model, the mapping relationship between semantic tags and local geometric features is extracted to form an intelligent recognition ontology. In the recognition process, support vector machine (SVM) and local geometric features are used to identify semantic tags, and the recognition analysis is carried out according to the semantic tag driving level. Ensure the consistency of animation character model recognition level. In view of the equal perimeter of the recognition boundary under attitude change, the isoline is defined by Poisson equation. This optimization method makes the recognition boundary smooth and consistent under the change of attitude. In the experimental part, various animation character models under different postures are verified and analyzed, and the consistent hierarchical recognition effect is obtained. Compared with the existing methods, the proposed recognition ontology can solve the problem of adaptive selection of optimization parameters of different models and improve the recognition quality.</p>
</abstract>

In order to make a deeper research on the existing animation character recognition technology and improve the existing role intelligent analysis technology, semantic ontology and Poisson equation are combined to apply to the animation character recognition and role intelligent analysis technology. For the three-dimensional model, the mapping relationship between semantic tags and local geometric features is extracted to form an intelligent recognition ontology. In the recognition process, support vector machine (SVM) and local geometric features are used to identify semantic tags, and the recognition analysis is carried out according to the semantic tag driving level. Ensure the consistency of animation character model recognition level. In view of the equal perimeter of the recognition boundary under attitude change, the isoline is defined by Poisson equation. This optimization method makes the recognition boundary smooth and consistent under the change of attitude. In the experimental part, various animation character models under different postures are verified and analyzed, and the consistent hierarchical recognition effect is obtained. Compared with the existing methods, the proposed recognition ontology can solve the problem of adaptive selection of optimization parameters of different models and improve the recognition quality.

Animation character recognition and character intelligence analysis based on semantic ontology and Poisson equation

Applied Mathematics and Nonlinear Sciences

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

In order to make a deeper research on the existing animation character recognition technology and improve the existing role intelligent analysis technology,...

Animation character recognition and character intelligence analysis based on semantic ontology and Poisson equation

Publié en ligne: 15 juil. 2022

Pages: 1487 - 1498

Reçu: 15 mars 2022

Accepté: 20 mai 2022

DOI: https://doi.org/10.2478/amns.2022.2.0137

Mots clés
Semantic ontology, Poisson equation, comic, Character recognition, Role intelligence analysis

© 2023 Zirui Yang, published by Sciendo

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

Animation character recognition and character intelligence analysis based on semantic ontology and Poisson equation

Publié en ligne: 15 juil. 2022

Pages: 1487 - 1498

Reçu: 15 mars 2022

Accepté: 20 mai 2022

DOI: https://doi.org/10.2478/amns.2022.2.0137

Mots clésSemantic ontology, Poisson equation, comic, Character recognition, Role intelligence analysis

© 2023 Zirui Yang, published by Sciendo

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

Mots clés
Semantic ontology, Poisson equation, comic, Character recognition, Role intelligence analysis