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Xin Yang
Researcher at Shanghai Jiao Tong University
Publications - 68
Citations - 682
Xin Yang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Active contour model & Image segmentation. The author has an hindex of 14, co-authored 66 publications receiving 651 citations.
Papers
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Vascular Active Contour for Vessel Tree Segmentation
TL;DR: A region competition-based active contour model exploiting the Gaussian mixture model, which mainly segments thick vessels, is introduced and is used to extract the liver and lung vessel tree as well as the coronary artery from high-resolution volumetric computed tomography images.
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Multi-object detection and tracking by stereo vision
TL;DR: A stereo vision system is constructed in this model to overcome the problems of illumination variation, shadow interference, and object occlusion, and a kernel-based clustering algorithm is proposed to group the projected points according to their height values and locations on the plane.
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Region competition based active contour for medical object extraction
TL;DR: A probabilistic and level set model for three-dimensional medical object extraction is proposed, which is called region competition based active contour, and is fast, convergent, adapted to a broad range of medical objects and produces satisfactory results.
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Active contour model driven by local histogram fitting energy
TL;DR: The method belongs to a nonparametric local region based active contour, and it can segment the regions whose distribution is hard to be predefined, and Experimental results show desirable performances of the method.
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Rotation Invariant Texture Descriptor Using Local Shearlet-Based Energy Histograms
Jiangping He,Hongwei Ji,Xin Yang +2 more
TL;DR: This letter presents a rotation invariant descriptor based on the shearlet transform for texture classification that has comparable classification accuracies on the Outex, Brodatz and CUReT texture databases and shows strong robustness on the databases containing additive noise.