Development and research of deep neural network fusion computer vision technology
Deep learning (DL) has revolutionized advanced digital picture processing, enabling significant advancements in computer vision (CV). However, it is important to note that older CV techniques, developed prior to the emergence of DL, still hold value and relevance. Particularly in the realm of more c...
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| Format: | Article |
| Language: | English |
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De Gruyter
2023-10-01
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| Series: | Journal of Intelligent Systems |
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| Online Access: | https://doi.org/10.1515/jisys-2022-0264 |
| _version_ | 1797349332265992192 |
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| author | Wang Jiangtao |
| author_facet | Wang Jiangtao |
| author_sort | Wang Jiangtao |
| collection | DOAJ |
| description | Deep learning (DL) has revolutionized advanced digital picture processing, enabling significant advancements in computer vision (CV). However, it is important to note that older CV techniques, developed prior to the emergence of DL, still hold value and relevance. Particularly in the realm of more complex, three-dimensional (3D) data such as video and 3D models, CV and multimedia retrieval remain at the forefront of technological advancements. We provide critical insights into the progress made in developing higher-dimensional qualities through the application of DL, and also discuss the advantages and strategies employed in DL. With the widespread use of 3D sensor data and 3D modeling, the analysis and representation of the world in three dimensions have become commonplace. This progress has been facilitated by the development of additional sensors, driven by advancements in areas such as 3D gaming and self-driving vehicles. These advancements have enabled researchers to create feature description models that surpass traditional two-dimensional approaches. This study reveals the current state of advanced digital picture processing, highlighting the role of DL in pushing the boundaries of CV and multimedia retrieval in handling complex, 3D data. |
| first_indexed | 2024-03-08T12:28:48Z |
| format | Article |
| id | doaj.art-f0d64c9045f540968b18a215fc35c00f |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-03-08T12:28:48Z |
| publishDate | 2023-10-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-f0d64c9045f540968b18a215fc35c00f2024-01-22T07:04:35ZengDe GruyterJournal of Intelligent Systems2191-026X2023-10-013211079295510.1515/jisys-2022-0264Development and research of deep neural network fusion computer vision technologyWang Jiangtao0School of Network Communication, Zhejiang Yuexiu University, Shaoxing, 312000, ChinaDeep learning (DL) has revolutionized advanced digital picture processing, enabling significant advancements in computer vision (CV). However, it is important to note that older CV techniques, developed prior to the emergence of DL, still hold value and relevance. Particularly in the realm of more complex, three-dimensional (3D) data such as video and 3D models, CV and multimedia retrieval remain at the forefront of technological advancements. We provide critical insights into the progress made in developing higher-dimensional qualities through the application of DL, and also discuss the advantages and strategies employed in DL. With the widespread use of 3D sensor data and 3D modeling, the analysis and representation of the world in three dimensions have become commonplace. This progress has been facilitated by the development of additional sensors, driven by advancements in areas such as 3D gaming and self-driving vehicles. These advancements have enabled researchers to create feature description models that surpass traditional two-dimensional approaches. This study reveals the current state of advanced digital picture processing, highlighting the role of DL in pushing the boundaries of CV and multimedia retrieval in handling complex, 3D data.https://doi.org/10.1515/jisys-2022-0264deep neural networkcomputer vision technology3d |
| spellingShingle | Wang Jiangtao Development and research of deep neural network fusion computer vision technology Journal of Intelligent Systems deep neural network computer vision technology 3d |
| title | Development and research of deep neural network fusion computer vision technology |
| title_full | Development and research of deep neural network fusion computer vision technology |
| title_fullStr | Development and research of deep neural network fusion computer vision technology |
| title_full_unstemmed | Development and research of deep neural network fusion computer vision technology |
| title_short | Development and research of deep neural network fusion computer vision technology |
| title_sort | development and research of deep neural network fusion computer vision technology |
| topic | deep neural network computer vision technology 3d |
| url | https://doi.org/10.1515/jisys-2022-0264 |
| work_keys_str_mv | AT wangjiangtao developmentandresearchofdeepneuralnetworkfusioncomputervisiontechnology |