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(Retracted) Intelligent resources based on active vision sensing strategy in the evolution of food aesthetics system

主动视觉 计算机科学 过程(计算) 机器视觉 人工智能 计算机视觉 对象(语法) 资源(消歧) 美女 人机交互 美学 计算机网络 哲学 操作系统
作者
Fazheng Chen,Yuemin Fan
出处
期刊:Journal of Electronic Imaging [SPIE]
卷期号:31 (06)
标识
DOI:10.1117/1.jei.31.6.062010
摘要

The active vision research platform allows the computer to recognize the local information around one or more images, even if the device can observe the geometric information of the objects in the area. We analyze and study the evolution of the diet aesthetics system based on the intelligent resources of active vision sensing strategies, modeling, and analysis of images with vision sensors and aim to combine the advanced vision research technology of active vision with the main body of food that is most closely related to the development of human society as the research object. The discussion makes this process more consistent with the human development process. This article is mainly divided into two parts to expand the narrative. The first part is the theoretical basis, including the construction of the active vision system, imaging applications, and these theories. The second is the specific role it plays in the promotion of dietary aesthetics as the basis for the development of intelligent resources. The article spends a lot of space on the analysis of active vision imaging. The content of this part can make the sensing strategy of active vision clearer. The experimental results show that the application of intelligent resource active visual sensing can make the sound characteristics in food aesthetics more vivid and more real. Vividness is the main indicator to reflect the beauty of food; among them, the maximum value of vividness before application is 5.97, the maximum value after application is 8.18, and even the minimum is 6.66, higher than the maximum value before smart resource application. The active visual sensing strategy of intelligent resources also has a huge impact on the aesthetics of the diet. The overall evaluation value of the audience has reached 8.41 after big data analysis.

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