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. This research mainly discusses the intelligent mosaic method of virtual reality Lingnan cultural heritage panorama based on automatic machine learning. 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. 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. Image preprocessing mainly includes image denoising and image projection transformation. In this study, cylindrical projection is used to construct the panorama of Lingnan cultural heritage. For each Lingnan cultural heritage training image, we first perform image segmentation to obtain multiple regions and extract the visual features of each region. We use automatic machine learning models to train the visual feature set and use the bagging method to generate different training subsets. 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. 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. This article uses a Java Applet-based approach to realize virtual roaming of viewing panoramic images of Lingnan cultural heritage in IE browser. The highest accuracy of SIFT is 82.22%, and the lowest recognition time is 0.01 s. This research will promote the development of Lingnan cultural heritage.

中文翻译：

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基于自动机器学习的虚拟现实岭南文化遗产全景智能拼接方法

随着虚拟现实应用领域的不断扩大和应用内容的复杂化，对逼真图形实时渲染的需求急剧增加。本研究主要探讨基于自动机器学习的虚拟现实岭南文化遗产全景智能拼接方法。为了有效弥补采集过程的不足对岭南文化遗产最终全景影像质量的影响，需要尽量减少相机的不规则旋转，并根据相邻区域之间的重叠区域进行采集。适当大小的图像。为了使岭南文化遗产全景图像具有更好的视觉效果，需要在图像配准和融合之前对图像进行预处理。图像预处理主要包括图像去噪和图像投影变换。本研究采用圆柱投影构建岭南文化遗产全景。对于每个岭南文化遗产训练图像，我们首先进行图像分割以获得多个区域并提取每个区域的视觉特征。我们使用自动机器学习模型来训练视觉特征集，并使用装袋方法生成不同的训练子集。为了生成每个分量分类器，我们根据匹配的SIFT特征点确定两幅图像的重叠区域，并在拼接执行过程中确定最佳拼接线。本文采用重叠区域第一行的像素数来确定候选拼接线列，最佳拼接线位置应考虑拼接区域的最小色差和两侧最相似的纹理来确定。本文采用基于Java Applet的方法，实现了在IE浏览器中查看岭南文化遗产全景图的虚拟漫游。SIFT 的最高准确率为 82.22%，最低识别时间为 0.01 s。这项研究将促进岭南文化遗产的发展。最低识别时间为0.01 s。这项研究将促进岭南文化遗产的发展。最低识别时间为0.01 s。这项研究将促进岭南文化遗产的发展。