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Weimin Huang
Researcher at Nanyang Technological University
Publications - 639
Citations - 17757
Weimin Huang is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 59, co-authored 419 publications receiving 15262 citations. Previous affiliations of Weimin Huang include Shenyang Jianzhu University & Ohio State University.
Papers
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Proceedings ArticleDOI
Coronary Artery Segmentation by Deep Learning Neural Networks on Computed Tomographic Coronary Angiographic Images
Weimin Huang,Lu Huang,Zhiping Lin,Su Huang,Yanling Chi,Jiayin Zhou,Jun-Mei Zhang,Ru San Tan,Liang Zhong +8 more
TL;DR: This paper investigates coronary artery lumen segmentation using 3D U-net convolutional neural networks, and tests its utility with multiple datasets on two settings, showing that the proposed deep learning approach outperformed other methods, evaluated by the Dice coefficients.
Proceedings ArticleDOI
Tracking a Variable Number of Human Groups in Video Using Probability Hypothesis Density
TL;DR: This work applies a multi-target recursive Bayes filter, the probability hypothesis density (PHD) filter, to a visual tracking problem: tracking a variable number of human groups in video.
Journal ArticleDOI
Formation of micro protrusion arrays atop shape memory polymer
TL;DR: In this paper, two simple approaches, namely laser heating and indentation, are proposed to produce micro-sized protrusion arrays, which can be used for shape memory polymers (SMPs).
Journal ArticleDOI
An Efficient Sequential Approach to Tracking Multiple Objects Through Crowds for Real-Time Intelligent CCTV Systems
TL;DR: A novel 2.5-D approach to real-time multiobject tracking in crowds is proposed, formulated as a maximum a posteriori estimation problem and is approximated through an assignment step and a location step.
Journal ArticleDOI
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.