Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Weichun Liu"'
Autor:
Sijie Jin, Muhammad Tahir, Fuqing Huang, Tianwei Wang, Huangkeyi Li, Weixiong Shi, Yayong Liu, Weichun Liu, Jin Zhong
Publikováno v:
Frontiers in Microbiology, Vol 15 (2024)
Promoting the availability of silage with a high protein content on farms can lead to profitable and sustainable ruminant production systems. Whole plant soybean (Glycine max L. Merrill, WPS) is a promising high-protein forage material for silage pro
Externí odkaz:
https://doaj.org/article/c85c071cbf8342a5a3424839776ec415
Publikováno v:
IEEE Access, Vol 10, Pp 128293-128303 (2022)
Due to the uneven distribution and large scale change of sheep in the pasture, it is not conducive to the counting and statistics of sheep in animal husbandry. The traditional target counting algorithm has low counting accuracy in the field of animal
Externí odkaz:
https://doaj.org/article/0c8a1a92b922474eae709528c189b0a9
Autor:
Weichun Liu, Chenglin Zhao
Publikováno v:
IEEE Access, Vol 9, Pp 47216-47229 (2021)
With the widespread application of machine learning and deep learning, image recognition has been continuously developed. However, there are still huge challenges in the use of machine learning and deep learning. The tuning processes of algorithms ar
Externí odkaz:
https://doaj.org/article/d3f452994ba248c6886b3d9c1498fea5
Publikováno v:
IEEE Access, Vol 7, Pp 84658-84667 (2019)
Recently, convolutional regression networks have drawn great attention in the tracking community. Convolutional regression trackers formulate the regression network as one convolutional layer and take advantages of end-to-end learning. However, exist
Externí odkaz:
https://doaj.org/article/4dabc4c1bec241bcb0645b8905663336
Publikováno v:
Applied Sciences, Vol 9, Iss 10, p 1975 (2019)
The accuracy of 3D viewpoint and shape estimation from 2D images has been greatly improved by machine learning, especially deep learning technology such as the convolution neural network (CNN). However, current methods are always valid only for one s
Externí odkaz:
https://doaj.org/article/fa972293044e46efb12e59d91a1b8812
Publikováno v:
IEEE Sensors Journal. 23:4118-4128
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc79ac3a02c589b51567842b75862fd8
https://doi.org/10.1109/jsen.2023.3234091
https://doi.org/10.1109/jsen.2023.3234091
Publikováno v:
Journal of Materials Science: Materials in Electronics. 33:23116-23127
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a351e55d4c802f959a857bb6b858555
https://doi.org/10.1007/s10854-022-09077-w
https://doi.org/10.1007/s10854-022-09077-w
Autor:
Chenglin Zhao, Weichun Liu
Publikováno v:
IEEE Access, Vol 9, Pp 47216-47229 (2021)
With the widespread application of machine learning and deep learning, image recognition has been continuously developed. However, there are still huge challenges in the use of machine learning and deep learning. The tuning processes of algorithms ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f6626b2dab0b4762116c1edda6796d7
https://doi.org/10.1109/access.2021.3062366
https://doi.org/10.1109/access.2021.3062366
Publikováno v:
BioMed research international. 2022
The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6f4a3158268c9c0b5715aad8f723b25
https://pubmed.ncbi.nlm.nih.gov/35898680
https://pubmed.ncbi.nlm.nih.gov/35898680
Publikováno v:
IEEE Sensors Journal. 20:4496-4503
Discriminative Correlation Filter (DCF) based trackers achieve superior performance with continuous conceptual improvement in the tracking field. By now, traditional DCF based trackers suppose that the tracked target is rigid and could be represented
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::549e37c50d6d8c6749d8c7e03e349b0d
https://doi.org/10.1109/jsen.2020.2964019
https://doi.org/10.1109/jsen.2020.2964019