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Yongtian Wang
Researcher at Beijing Institute of Technology
Publications - 358
Citations - 4216
Yongtian Wang is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Augmented reality & Holographic display. The author has an hindex of 27, co-authored 357 publications receiving 3010 citations. Previous affiliations of Yongtian Wang include Beijing Film Academy.
Papers
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Journal ArticleDOI
Hybrid constraint optimization for 3D subcutaneous vein reconstruction by near-infrared images.
TL;DR: The experimental results demonstrate that the veins captured from the left and right views can be accurately matched through the proposed HCO matching algorithm to solve the matching failure problems caused by the incomplete segmentation of vein structures captured from different views.
Proceedings ArticleDOI
Design of freeform nonsymmetric three-mirror systems using Gaussian radial basis functions freeform surfaces
TL;DR: A novel and high-accuracy surface fitting algorithm of Gaussian radial basis functions is proposed for the freeform surface fitting of freeform unobscured three-mirror system and a design example is demonstrated.
Journal ArticleDOI
A new method to accelerate depth extraction for aperture-coded camera
TL;DR: An improved algorithm in which the image is firstly segmented and then the small image regions are sampled for deconvolution and depth judgment is proposed, which can greatly reduce time consumption and save computer memory.
Proceedings ArticleDOI
Freeform lens design for laser diode beam shaping
TL;DR: In this paper, a simple and efficient method is provided to iteratively construct freeform surfaces for achieving difficult laser beam shaping tasks, which can precisely control the LD beam and is a very difficult inverse problem.
Journal ArticleDOI
Unbiased groupwise registration for shape prediction of foot scans
Jianjun Zhu,Xiuxing Wang,Shaodong Ma,Jingfan Fan,Jingfan Fan,Shuang Song,Xiao Ma,Danni Ai,Hong Song,Yurong Jiang,Yongtian Wang,Jian Yang +11 more
TL;DR: A graph-based groupwise shape registration algorithm for building statistical shape model (SSM) is proposed, which has been successfully applied to shape prediction of foot scans, and can obtain robust shape correspondences and SSM capability with respect to model generalization, specificity, and compactness.