Y
Yanxi Liu
Researcher at Pennsylvania State University
Publications - 144
Citations - 6785
Yanxi Liu is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Symmetry (geometry) & Symmetry group. The author has an hindex of 40, co-authored 126 publications receiving 6474 citations. Previous affiliations of Yanxi Liu include University of Massachusetts Amherst & Beihang University.
Papers
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Journal ArticleDOI
Online selection of discriminative tracking features
TL;DR: This paper presents an online feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance, and notes susceptibility of the variance ratio feature selection method to distraction by spatially correlated background clutter.
Journal ArticleDOI
Near-regular texture analysis and manipulation
TL;DR: This work develops a multi-modal framework where each deformation field is subject to analysis, synthesis and manipulation and is able to construct simple parametric models to faithfully synthesize the appearance of a near-regular texture and purposefully control its regularity.
Journal ArticleDOI
A computational model for periodic pattern perception based on frieze and wallpaper groups
TL;DR: A set of computer algorithms are developed that "understand" a given periodic pattern by automatically finding its underlying lattice, identifying its symmetry group, and extracting its representative motifs.
Book
Computational Symmetry in Computer Vision and Computer Graphics
TL;DR: Recognizing the fundamental relevance and group theory of symmetry has the potential to play an important role in computational sciences.
Journal ArticleDOI
Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment.
Owen Carmichael,Howard A. Aizenstein,Simon W. Davis,James T. Becker,Paul M. Thompson,Carolyn C. Meltzer,Yanxi Liu +6 more
TL;DR: Assessment of public-domain automated methodologies for MRI-based segmentation of the hippocampus in elderly subjects with Alzheimer's disease and mild cognitive impairment shows that a variety of methodological factors must be carefully considered to insure that automated methods perform well in practice.