Multi-resolution transmission image registration based on “Terrace Compression Method” and normalized mutual information

Li, Gang; Ye, Yaping; Zhou, Mei; Shang, Hua; Yang, Yuhui; Ma, Shuangshuang

doi:10.1016/j.chemolab.2022.104529

Cited by 7 publications

(1 citation statement)

References 35 publications

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“…The results of these analyses are presented in an intuitive visualization that provides the administrator with a holistic view of the cultural relics collection. By analyzing the correlation between different attributes, administrators can better grasp the characteristics and trends of relics collection and offer strong support for future collection strategies and exhibition planning [22][23][24].…”

Section: Design Of the Digital Retrieval System Of The Art Museummentioning

confidence: 99%

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Lin,

Hu,

Cheng

et al. 2024

PLoS ONE

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This study aims to develop a digital retrieval system for art museums to solve the problems of inaccurate information and low retrieval efficiency in the digital management of cultural heritage. By introducing an improved Genetic Algorithm (GA), digital management and access efficiency are enhanced, to bring substantial optimization and innovation to the digital management of cultural heritage. Based on the collection of art museums, this study first integrates the collection’s images, texts, and metadata with multi-source intelligent information to achieve a more accurate and comprehensive description of digital content. Second, a GA is introduced, and a GA 2 Convolutional Neural Network (GA2CNN) optimization model combining domain knowledge is proposed. Moreover, the convergence speed of traditional GA is improved to adapt to the characteristics of cultural heritage data. Lastly, the Convolutional Neural Network (CNN), GA, and GA2CNN are compared to verify the proposed system’s superiority. The results show that in all models, the sample output results’ actual value is 2.62, which represents the real data observation results. For sample number 5, compared with the actual value of 2.62, the predicted values of the GA2CNN and GA models are 2.6177 and 2.6313, and their errors are 0.0023 and 0.0113. The CNN model’s predicted value is 2.6237, with an error of 0.0037. It can be found that the network fitting accuracy after optimization of the GA2CNN model is high, and the predicted value is very close to the actual value. The digital retrieval system integrated with the GA2CNN model has a good performance in enhancing retrieval efficiency and accuracy. This study provides technical support for the digital organization and display of cultural heritage and offers valuable references for innovative exploration of museum information management in the digital era.

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Section: Design Of the Digital Retrieval System Of The Art Museummentioning

confidence: 99%

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Lin,

Hu,

Cheng

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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Method for enhancing transmission image of breast obtained in visible and near-infrared bands

Fan

et al. 2023

Biomedical Signal Processing and Control

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Threshold method to improve the speed of “Registration-Frame accumulation” and its application in breast multispectral transmission images

Song

2023

Infrared Physics & Technology

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Multi-resolution transmission image registration based on “Terrace Compression Method” and normalized mutual information

Cited by 7 publications

References 35 publications

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Method for enhancing transmission image of breast obtained in visible and near-infrared bands

Threshold method to improve the speed of “Registration-Frame accumulation” and its application in breast multispectral transmission images

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