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14 February 2022 Based on improved unsuperpoint image stitching method
Rui Liu, Ming Lu, Xianke He, Zuguo Chen
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Proceedings Volume 12161, 4th International Conference on Informatics Engineering & Information Science (ICIEIS2021); 121610Y (2022) https://doi.org/10.1117/12.2627213
Event: 4th International Conference on Informatics Engineering and Information Science, 2021, Tianjin, China
Abstract
When the traditional ORB algorithm is applied in the field of image mosaic, it is susceptible to the interference of light factors and cannot extract high-quality features, which leads to the inaccuracy of the perspective transformation matrix and the problem of poor mosaic effect. In response to this problem, this paper proposes an improved Unsuperpoint feature extraction network to apply to image stitching. By designing the backbone network of the unsupervised model, the points of interest and the descriptor loss function are optimized to improve the efficiency of the network and the accuracy of feature point extraction. Compared with the traditional ORB algorithm, the image stitching algorithm in this paper reduces the image matching time by half and increases the matching accuracy by 7%.The images stitched by this method avoid the phenomenon of cracks and black lines at the joints, and the image transitions are natural and high in definition.
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Rui Liu, Ming Lu, Xianke He, and Zuguo Chen "Based on improved unsuperpoint image stitching method", Proc. SPIE 12161, 4th International Conference on Informatics Engineering & Information Science (ICIEIS2021), 121610Y (14 February 2022); https://doi.org/10.1117/12.2627213
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KEYWORDS
Feature extraction
Convolution
Detection and tracking algorithms
Image enhancement
Convolutional neural networks
Image processing
Network architectures
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