Yuping Duan
Tianjin University
41 Papers
123 Citations
Yuping Duan is an academic researcher from Tianjin University. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 12, co-authored 38 publications. Previous affiliations of Yuping Duan include Institute for Infocomm Research Singapore & Nanyang Technological University.
Chat about Author
Papers
Total Variation--Based Phase Retrieval for Poisson Noise Removal
TL;DR: A variational model for phase retrieval based on a total variation regularization as an image prior and maximum a posteriori estimation of a Poisson noise model is proposed, referred to as “TV-PoiPR” and an efficient numerical algorithm based on an alternating direction method of multipliers is proposed and established.
96
Liver tumor detection and segmentation using kernel-based extreme learning machine
Weimin Huang,Ning Li,Ziping Lin,Guang-Bin Huang,Weiwei Zong,Jiayin Zhou,Yuping Duan +6 more
- 03 Jul 2013
TL;DR: In automatic liver tumor detection, it is proposed and shown that ELM can be trained as a one-class classifier with only healthy liver samples in training and results in a method of tumor detection based on novelty detection.
89
Volume Preserved Mass–Spring Model with Novel Constraints for Soft Tissue Deformation
Yuping Duan,Weimin Huang,Huibin Chang,Wenyu Chen,Jiayin Zhou,Soo-Kng Teo,Yi Su,Chee-Kong Chui,Stephen Chang +8 more
TL;DR: A stable and accurate method for animating mass-spring systems in real time and an implementation in a virtual reality environment for laparoscopic cholecystectomy demonstrates that the proposed MSM is capable to simulate large deformation and preserve volume in real-time calculations.
76
The $L_{0}$ Regularized Mumford–Shah Model for Bias Correction and Segmentation of Medical Images
TL;DR: A new variant of the Mumford-Shah model for simultaneous bias correction and segmentation of images with intensity inhomogeneity is proposed, which derives a new data fidelity using the local intensity properties to allow the bias field to be influenced by its neighborhood.
57
Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation.
Weimin Huang,Yongzhong Yang,Zhiping Lin,Guang-Bin Huang,Jiayin Zhou,Yuping Duan,Wei Xiong +6 more
- 06 Nov 2014
TL;DR: In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM.
49