Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Cengsi Zhong"'
Publikováno v:
IEEE Access, Vol 7, Pp 42826-42835 (2019)
Coronary heart disease is one of the most serious health problems in the world nowadays. By segmenting and examining the coronary arteries in medical images, we can find artery stenosis and plaque, which are the main causes of this certain disease. S
Externí odkaz:
https://doaj.org/article/15aea70bb19c4ab996becd0943d30da0
Publikováno v:
IEEE Access, Vol 7, Pp 82206-82217 (2019)
Inferring the three-dimensional structure of objects from monocular images has far-reaching applications in the field of 3D perception. In this paper, we propose a self-supervised network (SSL-Net) to generate 3D point clouds from a single RGB image,
Externí odkaz:
https://doaj.org/article/1c86fd99ba3c483fbc1c2f9db28e1585
Autor:
Huilin Tong, Xianhua Tang, Ziran Wei, Bo Huang, Cengsi Zhong, Pan Liang, Yinglin Wang, Yongbin Gao, Qingping Cai
Publikováno v:
Applied Intelligence. 51:7196-7207
The estimation of the vascular direction of celiac trunk plays an important role in the resection of gastric cancer as it can help doctors to plan the specific gastrectomy. The traditional manual estimation is the current clinical standard but time-c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89c1f2fd6782bff736b292618e24db0b
https://doi.org/10.1007/s10489-021-02221-9
https://doi.org/10.1007/s10489-021-02221-9
Publikováno v:
Applied Intelligence. 51:2063-2076
The 3D point clouds is an important type of geometric data structure, and the analysis of 3D point clouds based on deep learning is a very challenging task due to the disorder and irregularity. In existing research, RS-CNN provides an effective and p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c81dcafe5ac8567daf0c83c8b2e6bcb5
https://doi.org/10.1007/s10489-020-02004-8
https://doi.org/10.1007/s10489-020-02004-8
Publikováno v:
Pattern Recognition Letters. 133:327-333
3D scene parsing has always been a hot topic and point clouds are efficient data format to represent scenes. The semantic segmentation of point clouds is critical to the 3D scene, which is a challenging problem due to the unordered structure of point
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02b9206cc3dc15a5db1c8696cd577d71
https://doi.org/10.1016/j.patrec.2020.03.021
https://doi.org/10.1016/j.patrec.2020.03.021
Publikováno v:
Signal Processing: Image Communication. 78:284-292
Depth estimation is a fundamental task for 3D scene perception. Unsupervised learning is a prevalent method for depth estimation due to its generalization ability, and it requires no extra ground truth of depth for training. The typical pipeline for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::232574cc4cf903abbbddbb7b66923309
https://doi.org/10.1016/j.image.2019.07.007
https://doi.org/10.1016/j.image.2019.07.007
Publikováno v:
Wuhan University Journal of Natural Sciences. 24:369-375
3D object detection is one of the most challenging research tasks in computer vision. In order to solve the problem of template information dependency of 3D object proposal in the method of 3D object detection based on 2.5D information, we proposed a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02fe97a8984327952bd07babe85bdca2
https://doi.org/10.1007/s11859-019-1408-4
https://doi.org/10.1007/s11859-019-1408-4
Publikováno v:
IEEE Access, Vol 7, Pp 42826-42835 (2019)
Coronary heart disease is one of the most serious health problems in the world nowadays. By segmenting and examining the coronary arteries in medical images, we can find artery stenosis and plaque, which are the main causes of this certain disease. S
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58bc438a533d596b77cd01be4067d648
https://doi.org/10.1109/access.2019.2908039
https://doi.org/10.1109/access.2019.2908039
Publikováno v:
IEEE Access, Vol 7, Pp 82206-82217 (2019)
Inferring the three-dimensional structure of objects from monocular images has far-reaching applications in the field of 3D perception. In this paper, we propose a self-supervised network (SSL-Net) to generate 3D point clouds from a single RGB image,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7a317e4b749f3191bbeec50d93f5894
https://doi.org/10.1109/access.2019.2923842
https://doi.org/10.1109/access.2019.2923842