Building Systems for Object Recognition by Multichannel Sensing Systems Based on Neural Networks and Fractal Signatures

Akinshin, N. S.; Потапов, А. А.; Bystrov, R.P.; Есиков, О. В.; Чернышков, А. И.

doi:10.1134/s1064226920060017

Cited by 3 publications

(1 citation statement)

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“…This requires the government to create a clear Internet environment for hundreds of millions of people with a high sense of political responsibility and mission and build a good Internet Ecology for realizing the Chinese dream of the great rejuvenation of the Chinese nation. Therefore, strengthening network public opinion monitoring work should be an important part of government work [2].…”

Section: Introductionmentioning

confidence: 99%

Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network

Liu

Zhang²

2022

Advances in Multimedia

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With the rapid development of the Internet, network media, as a new form of information dissemination, has penetrated into people’s daily life. In recent years, with the rapid transformation of Chinese social structure and the rise of self-media platforms, various social contradictions have been highlighted in the form of online public opinion. Especially on online multimedia platforms, the spread of online public opinion is more rapid, which can easily lead to social hotspots. In order to effectively supervise the public opinion information on the Internet, it is necessary to identify the target of the information on the multimedia platform and effectively screen the information, so as to control the network public opinion in the development stage. Aiming at the above problems, we propose a multitarget retrieval method based on a convolutional neural network, which uses multitarget detection algorithm to locate multitarget regions and extract regional features and uses cosine distance as a similarity measure for multitarget recognition. In view of the slow feature extraction speed of VGG model, a lightweight mobile network model is proposed to replace the original VGG model on the mobile phone to reduce the retrieval time and realize the recognition of specific targets on the multimedia platform, and it is applied to the verification of image recognition on the multimedia platform. The results show that the algorithm proposed in this paper has great advantages in multitarget recognition tasks.

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Section: Introductionmentioning

confidence: 99%

Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network

Liu

Zhang²

2022

Advances in Multimedia

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Imitation camouflage synthesis based on shallow neural network

Xiuxia,

Pin,

Shuaibin

2023

Multimedia Systems

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Deep learning technology has been widely used in the military field, which have achieved great success. The traditional method for painting camouflage either using the background information or the artificial pattern. None of the traditional methods can both consider the background information and camouflage rules. In this paper, A new automatic camouflage generation framework is proposed. A method for generating camouflage pattern is designed. The imitation camouflage pattern is synthesized from the features of both background and artificial pattern. In our method, the texture feature of both background and traditional pattern patches are extracted from the feature maps of shallow neural network (SNN). Based on the feature maps, statistic information of second order differential and mean subtracted contrast normalized coefficients for texture and color is extracted. By iterating to optimize the imitation camouflage to be generated, the statistical information of the imitation camouflage can approximate the characteristic statistical information of the background and pattern. The new generated camouflage pattern can contain the color and texture information of background; besides, it can maintain the traditional patch camouflage criteria. Our approach makes camouflage painting more flexible and allows the target to better infuse into the background. And our method is designed for the preparation of painting camouflage.

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Convolutional Neural Network Training Optimization for Low Point Density Image Recognition

Ziyadinov

Kurochkin

Терешонок

2021

J. Commun. Technol. Electron.

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Building Systems for Object Recognition by Multichannel Sensing Systems Based on Neural Networks and Fractal Signatures

Cited by 3 publications

References 0 publications

Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network

Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network

Imitation camouflage synthesis based on shallow neural network

Convolutional Neural Network Training Optimization for Low Point Density Image Recognition

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