In recent years, the rapid development of artificial intelligence and computer vision technology has played a vital role in the advancement of medicine. The application of motion recognition technology based on virtual reality to the process of medical rescue and training is an important development direction at present. Gesture recognition needs to consider the dynamic relationship between the front and back of the hand, target detection, and space‐time transformation, but its application in medical surgery is challenging. This paper introduces the development of gesture recognition technology and equipment, introduces the target detection, processing, feature extraction, motion estimation and motion recognition technology in virtual reality‐based surgical motion recognition, discusses the current mainstream virtual reality‐based surgical motion recognition development, and makes a developing prospect.
CNN (convolutional neural network) is a classical research method of Deep Learning. It can obtain the characteristics of a picture through the information transmission between convolution layers and pooling layers, and generate some output (such as image classification) after final processing. Since the end of the 20th century, scholars have proposed various convolutional neural networks, which have their own characteristics. In this paper, we choose LeNet, AlexNet, VGG, ResNet and GoogLeNet to complete the cat and dog recognition task on kaggle, so as to identify and explore the performance of different networks in different situations. The results show that these classical algorithms have generally become more and more advanced over time, but this cognition is not completely correct. For example, when the number of samples is limited, the more advanced ResNet did not perform as well as the relatively primitive VGG network. Such characteristics help us choose the right algorithm in different situations and guide us refine the algorithm in the future.
In the context of the explosive growth of virtual reality technology, the author analyzes the degree of attention, development trend and hotspot distribution of VR technology in recent years through research on VR‐related market research and large‐scale project applications. Specifically, the author investigates and summarizes the history, development and latest progress of VR technology, and lists the latest achievements of VR technology in the field of industrial applications. The development prospects in the fields of patriotic education, smart medical care, and smart social networking are prospected and discussed.
VR content is a key link in building a VR ecosystem, but the extreme lack of high-quality content has become the core shortcoming restricting the development of the VR industry, so in the medium and long term, the VR industry will shift from hardware technology upgrades to high-quality content-oriented, and is expected to usher in a new round of growth driven by business model innovation and content explosion. With 3D reconstruction, users can experience virtual scenes visually and audibly. The development of 3D reconstruction technology will bring great changes to existing players, and also greatly promote the rapid development of metaverse content Through continuous algorithm improvement, 3D reconstruction continues to be applied to all aspects of life.
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