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Your search keyword '"Wu, Fengjiao"' showing total 9 results
Start Over Author "Wu, Fengjiao" Publisher springer science and business media llc
9 results on '"Wu, Fengjiao"'

2. Additive margin cosine loss for image registration

Author

Wu Fengjiao, Yang Yunfan, Sun Shouyu, Zijiang Luo, Xin Yang, Ma Yuandong, and Bin Xu

Subjects
Matching (graph theory), Computer science, business.industry, Feature vector, Deep learning, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image registration, 020207 software engineering, 02 engineering and technology, Computer Graphics and Computer-Aided Design, Convolutional neural network, Position (vector), Feature (computer vision), Computer Science::Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software
Abstract

In view of the multi-scale changes and the influence of light and angle in the image matching process, it is quite difficult to realize intelligent image registration by using convolutional neural network. The existing image matching algorithm has the following problems in the application process: the existing shallow feature extraction model has lost a lot of effective feature information and low recognition accuracy. Meanwhile, the image registration method based on deep learning is not robust and accurate enough. Therefore, an image registration method based on additive edge cosine loss was proposed in this paper. In the twin network architecture, cosine loss was used to convert Euclidean space into angular space, which eliminated the influence of characteristic intensity and improved the accuracy of registration. The matching cost was directly calculated by the included angle of two vectors in the embedded space, where the size of the angle edge could be quantitatively adjusted through parameter $$m$$ . We further derived a specific $$m$$ to quantitatively adjust the loss. In addition, anti-rotation attention mechanism was added to the network to enhance the ability of feature information extraction and adjust the position information of feature vectors to reduce the mismatching caused by image rotation.

Published
2021

4. miR-196b-5p-mediated downregulation of FAS promotes NSCLC progression by activating IL6-STAT3 signaling

Author

Huang, Xiangjie, primary, Xiao, Sisi, additional, Zhu, Xinping, additional, Yu, Yun, additional, Cao, Meng, additional, Zhang, Xiaodong, additional, Li, Shaotang, additional, Zhu, Wangyu, additional, Wu, Fengjiao, additional, Zheng, Xiaohui, additional, Jin, Libo, additional, Xie, Congying, additional, Huang, Xiaoying, additional, Zou, Peng, additional, Li, Xiaokun, additional, and Cui, Ri, additional

Published
2020
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