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Detection of embryo eggs based on tensor depth calculation model
Author(s): Min Zhang; Wei Wang
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Paper Abstract

With the improvement of people's living standards, poultry has become accustomed to appearing on the table. However, the emergence of the bird flu virus not only harms the lives of poultry but also harms human health. The vaccine and epidemic prevention method is the main prevention and control method for the bird flu epidemic. The quality of bird flu vaccine is directly related to the safety of poultry and human life. The culture of bird flu vaccine is mainly through the inoculation of chicken embryos with avian influenza strains and inactivated by the proliferation and cultivation of strains in the embryonated eggs of the strains. Therefore, the detection of embryogenesis activity of strains is an important part of the proliferation and culture of avian influenza strains. For some traditional detection methods such as artificial eggthinning, there are shortcomings such as visual fatigue, low detection efficiency, and subjective factors that are easily detected by human eyes. This paper proposes a tensor depth calculation model, which extends the data from the vector space to the tensor space, which can better reflect the underlying relevance of the data. The activity detection of embryonated eggs was performed on real data sets. Comparison with convolutional neural network on vector space can get better recognition rate of this algorithm.

Paper Details

Date Published: 29 October 2018
PDF: 6 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083621 (29 October 2018); doi: 10.1117/12.2513864
Show Author Affiliations
Min Zhang, Tianjin Normal Univ. (China)
Wei Wang, Tianjin Normal Univ. (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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