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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.

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Journal ArticleDOI

Accurate measurement of granary stockpile volume based on fast registration of multi-station scans

TL;DR: The convex hull indexed Gaussian mixture model is introduced for accurate point cloud registration of the granary and has the potential to be utilized in intelligent granary management in the future because it is fully automatic.
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Distortion Correction and Geometric Calibration for X-Ray Angiography System

TL;DR: Based on the knowledge that distortion is the result of pixel movement, a distortion correction method is developed by analyzing the principle of distortion change with imaging orientation, which can be used to correct the imaging distortion of a conventional X-ray angiography (XRA) system with an image intensifier as discussed by the authors.
Journal ArticleDOI

Deep feature regression (DFR) for 3D vessel segmentation.

TL;DR: A deep feature regression (DFR) method based on a convolutional regression network (CRN) and a stable point clustering mechanism that evaluates the reliability of the CRN estimation through iterative regression of vessel parameters is proposed.
Journal ArticleDOI

Multiresolution Cube Propagation for 3-D Ultrasound Image Reconstruction

TL;DR: A novel method for3-D freehand ultrasound reconstruction, which can accurately restore 3-D volumes from 2-D B-scans through cube propagation and which is robust and can achieve quality results in a short execution time relative to current reconstruction techniques is proposed.
Proceedings ArticleDOI

A novel graph cuts based liver segmentation method

TL;DR: Experimental results show that the developed method is very effective for the segmentation of liver from CT images and greatly reduces the complexity of the commonly used graph cuts methods, which can obtain the hard constrains automatically.