X
Xiaoxing Li
Researcher at General Electric
Publications - 15
Citations - 464
Xiaoxing Li is an academic researcher from General Electric. The author has contributed to research in topics: Image segmentation & Facial recognition system. The author has an hindex of 9, co-authored 15 publications receiving 448 citations. Previous affiliations of Xiaoxing Li include Virginia Tech & Simon Fraser University.
Papers
More filters
Journal ArticleDOI
Watershed segmentation using prior shape and appearance knowledge
Ghassan Hamarneh,Xiaoxing Li +1 more
TL;DR: This work proposes a novel method for enhancing watershed segmentation by utilizing prior shape and appearance knowledge, which iteratively aligns a shape histogram with the result of an improved k-means clustering algorithm of the watershed segments.
Proceedings ArticleDOI
Expression-insensitive 3D face recognition using sparse representation
Xiaoxing Li,Tao Jia,Hao Zhang +2 more
TL;DR: This work presents a face recognition method based on sparse representation for recognizing 3D face meshes under expressions using low-level geometric features and shows that by choosing higher-ranked features, the recognition rates approach those for neutral faces, without requiring an extensive set of reference faces for each individual.
Proceedings ArticleDOI
Expression-Invariant Face Recognition with Expression Classification
Xiaoxing Li,Greg Mori,Hao Zhang +2 more
TL;DR: By studying face geometry, this work is able to determine which type of facial expression has been carried out, thus building an expression classifier which is capable of recognizing faces with different expressions.
Proceedings ArticleDOI
Adapting Geometric Attributes for Expression-Invariant 3D Face Recognition
Xiaoxing Li,Hao Zhang +1 more
TL;DR: The use of multiple intrinsic geometric attributes, including angles, geodesic distances, and curvatures, for 3D face recognition, where each face is represented by a triangle mesh, preprocessed to possess a uniform connectivity is investigated.
Journal ArticleDOI
Registration of Images With Varying Topology Using Embedded Maps
TL;DR: The efficacy of the proposed REM method for registration of brain MRI with severe topological differences is demonstrated as multiple sets of experiments are conducted on magnetic resonance imaging (MRI) with lesions from OASIS and ADNI datasets.