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Special issue on cognitive-inspired computing and applications

With the advent of the information age and advancement of digital technology, film and television tourism is developing rapidly under the joint action of the film industry and tourism industry, and has become a crucial form of cultural and entertainment consumption for individuals to pursue a better life in the new age. This study designs three experiments from the perspectives of identity conversion, motivation transfer, and demand change to conduct an empirical study on the mediating role of empathy for further exploring the internal mechanism of film-induced tourism in film and television tourism. The findings suggest that the three mediation hypotheses are all valid, indicating that film-induced tourism involves identity conversion from audiences to visitors, motivation transfer from watching to traveling, and demand change from interest to expectation through emotional media.

https://www.frontiersin.org/articles/10.3389/fpsyg.2022.900998/pdf

The Mediating Role of Empathy in the Internal Mechanism of Film-Induced Tourism

We conduct an in-depth study on the model construction of deep neural networks and design a model of painter's psychology and anime creation style to realize the study of the relationship between painter's psychology and anime creation style based on deep neural networks. This paper proposes an animation creation psychology classification algorithm that integrates human cognitive deep network structure optimization. The algorithm analyzes the connection between different convolutional layer features and animation characteristics through animation creation style CNN feature visualization. It interactively uses the knowledge of animation creation psychology expression techniques to optimize the network structure. This paper proposes a scene animation network based on spectral difference perception style. By analyzing the characteristics and differences in the spectrum between realistic and anime domain images, the generator is guided to learn the mapping relationships better to fit the style distribution of anime domain images. This paper uses a fully convolutional structure; the network is more lightweight and supports image inputs of arbitrary size, which can keep the semantic system of the background unchanged while highly deforming the five facial features, moving toward the goal of human-scene fusion for the animation task.

A Study on the Relationship between Painter's Psychology and Anime Creation Style Based on a Deep Neural Network

Deep neural network (DNN) has gone through more than forty years from the simulation stage to the embryonic stage, to the conception and the initial formation of the theory, until further simple applications. Firstly, deep convolution neural network is used to extract features, and then the extracted features are coded by Gaussian aggregation. Finally, the encoded features are input into the full connection layer to classify the image. The experiment in this paper focuses on the two-dimensional facial expression animation technology under DNN. This experiment mainly introduces the two important steps of image conversion, spatial mapping and resampling technology, as well as the detailed definition of the MPEG-4 standard. By using the feature points obtained in the experiment as the feature points set in the face definition parameters, in the deformation algorithm based on Delaunay triangulation, the inverse mapping technology and the quadratic linear interpolation technology are combined with the face and combined with facial animation. The parameter step can realize the conversion of facial expressions, and the generated facial expressions are more natural and delicate.The experimental data show that the facial animation parameters (FAP) as a complete set of basic facial movements can recognize the most subtle facial expressions under DNN. Change, different FAP combinations can form different facial expressions. The experimental results show that the 76 facial feature points located in the experiment have been tested for grid generation. When the segmentation threshold is larger, the merge method is closer to the sub-point insertion method; otherwise, it is closer to the divide and conquer method. When the threshold is 20, the algorithm belongs to the divide and conquer method; when the threshold is 80, the algorithm belongs to the point insertion method. Through research, this article finds that the innovation of DNN on the visual performance of animated films can provide more help for the production of sophisticated animation works, and can continuously improve the visual performance and cultural connotation of animated films.

Ping Xu

Papers

Innovative research on the visual performance of image two-dimensional animation film based on deep neural network