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Albert C. S. Chung
Researcher at Hong Kong University of Science and Technology
Publications - 190
Citations - 4915
Albert C. S. Chung is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Image registration & Image segmentation. The author has an hindex of 31, co-authored 182 publications receiving 4178 citations. Previous affiliations of Albert C. S. Chung include University of Oxford.
Papers
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Journal ArticleDOI
Dominant Local Binary Patterns for Texture Classification
TL;DR: The proposed features are robust to image rotation, less sensitive to histogram equalization and noise, and achieves the highest classification accuracy in various texture databases and image conditions.
Book ChapterDOI
Three Dimensional Curvilinear Structure Detection Using Optimally Oriented Flux
Max W. K. Law,Albert C. S. Chung +1 more
TL;DR: In this article, the authors proposed a novel curvilinear structure detector, called Optimally Oriented Flux (OOF), which finds an optimal axis on which image gradients are projected in order to compute the image gradient flux.
Proceedings ArticleDOI
Facial Expression Recognition using Advanced Local Binary Patterns, Tsallis Entropies and Global Appearance Features
TL;DR: This paper proposes a novel facial expression recognition approach based on two sets of features extracted from the face images: texture features and global appearance features, obtained by using the extended local binary patterns in both intensity and gradient maps.
Proceedings ArticleDOI
Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks
TL;DR: A novel, efficient unsupervised symmetric image registration method which maximizes the similarity between images within the space of diffeomorphic maps and estimates both forward and inverse transformations simultaneously.
Book ChapterDOI
Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks
TL;DR: A deep Laplacian Pyramid Image Registration Network is proposed, which can solve the image registration optimization problem in a coarse-to-fine fashion within the space of diffeomorphic maps.