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

Abstract

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.