Detection of embryo eggs based on tensor depth calculation model

Min Zhang; Wei Wang

doi:10.1117/12.2513864

29 October 2018 Detection of embryo eggs based on tensor depth calculation model

Min Zhang, Wei Wang

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 1083621 (2018) https://doi.org/10.1117/12.2513864
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

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.

Citation Download Citation

Min Zhang and Wei Wang "Detection of embryo eggs based on tensor depth calculation model", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083621 (29 October 2018); https://doi.org/10.1117/12.2513864

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KEYWORDS

Data modeling

Convolutional neural networks

Detection and tracking algorithms

Evolutionary algorithms

Convolution

Image sensors

Neural networks

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