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Shihui Ying
Researcher at Shanghai University
Publications - 96
Citations - 1808
Shihui Ying is an academic researcher from Shanghai University. The author has contributed to research in topics: Computer science & Iterative closest point. The author has an hindex of 17, co-authored 64 publications receiving 1232 citations. Previous affiliations of Shihui Ying include University of North Carolina at Chapel Hill & Xi'an Jiaotong University.
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Journal ArticleDOI
Hierarchical Unbiased Graph Shrinkage (HUGS): A Novel Groupwise Registration for Large Data Set
TL;DR: The proposed groupwise registration method using a graph to model the distribution of all image data sitting on the image manifold can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph.
Journal ArticleDOI
Projective parameter transfer based sparse multiple empirical kernel learning Machine for diagnosis of brain disease
TL;DR: A novel projective model (PM) based sparse MEKLM(PM-SMEKLM) algorithm to learn a cross-domain transformation by PM in way of the parameter-based TL, and then apply it to the neuroimaging-based CAD for brain diseases.
Journal ArticleDOI
LieTrICP: An improvement of trimmed iterative closest point algorithm
TL;DR: This algorithm is termed as LieTrICP, as it combines the advantages of the Trimmed Iterative Closest Point algorithm and Lie group representation and gives a unified Lie group framework for point set registration, which can be extended to more complicated transformations and high dimensional problems.
Journal ArticleDOI
Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.
TL;DR: The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR.
Proceedings ArticleDOI
ICP with Bounded Scale for Registration of M-D Point Sets
TL;DR: A novel approach named the iterative closest point with bounded scale (ICPBS) algorithm which integrates a scale with boundaries into the traditional ICP algorithm, and yields more satisfying robust results than thetraditional ICP method.