Algorithm of Painting Style Transformation Based on Depth Neural Network
Lecture Notes in Electrical Engineering, ISSN: 1876-1119, Vol: 1031 LNEE, Page: 1658-1663
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
With the enrichment of material life, people’s demand for spiritual and cultural life is becoming stronger and stronger. As an important part of spiritual culture, artistic creation and entertainment consumption have also received more and more attention. The painting style conversion algorithm based on depth neural network is a technology that uses depth information to convert an image into another image. The algorithm is suitable for images with high contrast and sharp edges. It is also suitable for images with low contrast and blurred edges, but compared with other algorithms, it takes more time to complete this process. The painting style conversion algorithm based on depth neural network is a technology that uses depth information to convert an image into another image. The algorithm is suitable for images with high contrast and sharp edges. It is also suitable for images with low contrast and blurred edges. This algorithm is used to convert the sample image into a new image of Xin’an painting style.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85163277278&origin=inward; http://dx.doi.org/10.1007/978-981-99-1428-9_221; https://link.springer.com/10.1007/978-981-99-1428-9_221; https://dx.doi.org/10.1007/978-981-99-1428-9_221; https://link.springer.com/chapter/10.1007/978-981-99-1428-9_221
Springer Science and Business Media LLC
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