Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Xiaoshui Huang"'
Autor:
Ali Haidar, Matthew Field, Vikneswary Batumalai, Kirrily Cloak, Daniel Al Mouiee, Phillip Chlap, Xiaoshui Huang, Vicky Chin, Farhannah Aly, Martin Carolan, Jonathan Sykes, Shalini K. Vinod, Geoffrey P. Delaney, Lois Holloway
Publikováno v:
Cancers, Vol 15, Iss 3, p 564 (2023)
In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nome
Externí odkaz:
https://doaj.org/article/430c3788a87046cc902fac4dec5f1fa4
Publikováno v:
IEEE Transactions on Visualization and Computer Graphics. :1-12
Publikováno v:
IEEE Robotics and Automation Letters. 7:12585-12592
Rejecting correspondence outliers enables to boost the correspondence quality, which is a critical step in achieving high point cloud registration accuracy. The current state-of-the-art correspondence outlier rejection methods only utilize the struct
Publikováno v:
IEEE Robotics and Automation Letters. 7:12323-12330
The existing state-of-the-art point descriptor relies on structure information only, which omit the texture information. However, texture information is crucial for our humans to distinguish a scene part. Moreover, the current learning-based point de
Publikováno v:
IEEE Robotics and Automation Letters. 7:7028-7035
Sampling noise and density variation widely exist in the point cloud acquisition process, leading to few accurate point-to-point correspondences. Since they rely on point-to-point correspondence search, existing state-of-the-art point cloud registrat
Publikováno v:
IEEE Transactions on Multimedia. 24:3506-3519
The studies of previous decades have shown that the quality of depth maps can be significantly lifted by introducing the guidance from intensity images describing the same scenes. With the rising of deep convolutional neural network, the performance
Autor:
Xiaoshui Huang, Yangfu Wang, Sheng Li, Guofeng Mei, Zongyi Xu, Yucheng Wang, Jian Zhang, Mohammed Bennamoun
Real-world point cloud registration is challenging because of large outliers in correspondence search. The mixture variations, such as partial overlap, noise and cross sources, are the root cause of these large outliers. Existing methods face challen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d76bdb8997396f5e6e0843a3c1318788
https://hdl.handle.net/10453/169595
https://hdl.handle.net/10453/169595
The annotation for large-scale point clouds is still time-consuming and unavailable for many complex real-world tasks. Point cloud pre-training is a promising direction to auto-extract features without labeled data. Therefore, this paper proposes a g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbd3bd8aeab0dcce2ef37fff9b242b90
https://hdl.handle.net/10453/169685
https://hdl.handle.net/10453/169685
Most existing correspondence-free registration methods suffer from performance degradation in partial overlapped point clouds. To solve the partial overlapped point cloud registration, this paper proposes, SegReg, a soft Segmentationbased corresponde
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f54407f77d3059a43e712b9b270ff314
https://hdl.handle.net/10453/169765
https://hdl.handle.net/10453/169765
Autor:
Ali Haidar, Matthew Field, Vikneswary Batumalai, Kirrily Cloak, Daniel Al Mouiee, Phillip Chlap, Xiaoshui Huang, Vicky Chin, Farhannah Aly, Martin Carolan, Jonathan Sykes, Shalini K. Vinod, Geoffrey P. Delaney, Lois Holloway
Publikováno v:
Cancers; Volume 15; Issue 3; Pages: 564
In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nome
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7903c2fb84a070f5f229d4949ad2b0c6
https://doi.org/10.1101/2022.10.14.22280859
https://doi.org/10.1101/2022.10.14.22280859