![]() Virtual reality immersion (VRI) is an advanced computer-generated technology that reduces subjective reports of pain in procedural medical treatment. The emergence of panoramic stitching technology makes image-based VR technology more interactive. ![]() Therefore, when working on the research of Lingnan cultural heritage panoramic display technology based on image mosaic, this paper needs to explore and give a reasonable solution by itself. As far as image stitching technology is concerned, it is a complete process from image collection to stitching, but the current academic research is mostly aimed at a specific step in the process, and there is no standardized and efficient complete solution. Lingnan culture refers to the culture of the Lingnan region of China, covering academic research, literature, painting, calligraphy, music, opera, crafts, architecture, gardens, folklore, religion, food, language, overseas Chinese culture, and many other contents. Although the commonly used image registration algorithms represented by the SIFT algorithm are relatively mature and widely used, they need to be further optimized and improved due to the large amount of calculation, the relatively long operation time, and the relatively general accuracy of the algorithm. Image registration is the core step of image splicing, and its quality and efficiency are the key to determining the effect of image splicing. This research will promote the development of Lingnan cultural heritage. The highest accuracy of SIFT is 82.22%, and the lowest recognition time is 0.01 s. This article uses a Java Applet-based approach to realize virtual roaming of viewing panoramic images of Lingnan cultural heritage in IE browser. ![]() In this paper, the number of pixels in the first row of the overlapping area is used to determine the candidate stitching line column, and the best stitching line position should be determined in consideration of the smallest color difference in the stitching area and the most similar texture on both sides. In order to generate each component classifier, we determine the overlap area of the two images according to the matched SIFT feature points and determine the best stitching line during the implementation of stitching. ![]() We use automatic machine learning models to train the visual feature set and use the bagging method to generate different training subsets. For each Lingnan cultural heritage training image, we first perform image segmentation to obtain multiple regions and extract the visual features of each region. In this study, cylindrical projection is used to construct the panorama of Lingnan cultural heritage. Image preprocessing mainly includes image denoising and image projection transformation. In order to make Lingnan cultural heritage panoramic images have better visual effects, it is necessary to preprocess the images before image registration and fusion. In order to effectively make up for the impact of the insufficiency of the collection process on the quality of the final panoramic image of Lingnan cultural heritage, it is necessary to minimize the irregular rotation of the camera and collect images according to the overlapping area between adjacent images of appropriate size. This research mainly discusses the intelligent mosaic method of virtual reality Lingnan cultural heritage panorama based on automatic machine learning. ![]() With the increasing expansion of virtual reality application fields and the complexity of application content, the demand for real-time rendering of realistic graphics has increased sharply. ![]()
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