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Digital Image Recognition Based on Improved Cognitive Neural Network
This paper presents an innovative cognitive neural network method application in digital image recognition. The following conclusion can be drawn. Each point of the graph is transformed, and the original color of the transformed new coordinates is given to the point. If after all the points have tra...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
De Gruyter
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6487799/ https://www.ncbi.nlm.nih.gov/pubmed/31098322 http://dx.doi.org/10.1515/tnsci-2019-0021 |
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author | Liu, Yuxi |
author_facet | Liu, Yuxi |
author_sort | Liu, Yuxi |
collection | PubMed |
description | This paper presents an innovative cognitive neural network method application in digital image recognition. The following conclusion can be drawn. Each point of the graph is transformed, and the original color of the transformed new coordinates is given to the point. If after all the points have transformed, if there is a point and no point has converted to this point, the point is not given a color. Then this point will form a hole or a stripe, and the color is the color of the point initialization. The innovative method can effectively separate the digital image recognition signal from the mixed signal and maintain the waveform of the source signal with high accuracy, thus laying the foundation for the next step of recognition. |
format | Online Article Text |
id | pubmed-6487799 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | De Gruyter |
record_format | MEDLINE/PubMed |
spelling | pubmed-64877992019-05-16 Digital Image Recognition Based on Improved Cognitive Neural Network Liu, Yuxi Transl Neurosci Special Issue: Adult Neurogenesis and Neurological Disorders This paper presents an innovative cognitive neural network method application in digital image recognition. The following conclusion can be drawn. Each point of the graph is transformed, and the original color of the transformed new coordinates is given to the point. If after all the points have transformed, if there is a point and no point has converted to this point, the point is not given a color. Then this point will form a hole or a stripe, and the color is the color of the point initialization. The innovative method can effectively separate the digital image recognition signal from the mixed signal and maintain the waveform of the source signal with high accuracy, thus laying the foundation for the next step of recognition. De Gruyter 2019-04-23 /pmc/articles/PMC6487799/ /pubmed/31098322 http://dx.doi.org/10.1515/tnsci-2019-0021 Text en © 2019 Yuxi Liu, published by De Gruyter http://creativecommons.org/licenses/by/4.0 This work is licensed under the Creative Commons Attribution 4.0 Public License. |
spellingShingle | Special Issue: Adult Neurogenesis and Neurological Disorders Liu, Yuxi Digital Image Recognition Based on Improved Cognitive Neural Network |
title | Digital Image Recognition Based on Improved Cognitive Neural Network |
title_full | Digital Image Recognition Based on Improved Cognitive Neural Network |
title_fullStr | Digital Image Recognition Based on Improved Cognitive Neural Network |
title_full_unstemmed | Digital Image Recognition Based on Improved Cognitive Neural Network |
title_short | Digital Image Recognition Based on Improved Cognitive Neural Network |
title_sort | digital image recognition based on improved cognitive neural network |
topic | Special Issue: Adult Neurogenesis and Neurological Disorders |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6487799/ https://www.ncbi.nlm.nih.gov/pubmed/31098322 http://dx.doi.org/10.1515/tnsci-2019-0021 |
work_keys_str_mv | AT liuyuxi digitalimagerecognitionbasedonimprovedcognitiveneuralnetwork |