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Chunmei Qing
Researcher at South China University of Technology
Publications - 63
Citations - 3621
Chunmei Qing is an academic researcher from South China University of Technology. The author has contributed to research in topics: Feature extraction & Computer science. The author has an hindex of 13, co-authored 57 publications receiving 2557 citations. Previous affiliations of Chunmei Qing include University of Lincoln.
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DehazeNet: An End-to-End System for Single Image Haze Removal
TL;DR: DehazeNet as discussed by the authors adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing.
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DehazeNet: An End-to-End System for Single Image Haze Removal
TL;DR: This paper proposes a trainable end-to-end system called DehazeNet, for medium transmission estimation, which takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model.
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Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
Jaime Zabalza,Jinchang Ren,Jiangbin Zheng,Huimin Zhao,Chunmei Qing,Zhijing Yang,Peijun Du,Stephen Marshall +7 more
TL;DR: Segmented SAE (S-SAE) is proposed by confronting the original features into smaller data segments, which are separately processed by different smaller SAEs, which has resulted in reduced complexity but improved efficacy of data abstraction and accuracy of data classification.
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Interpretable emotion recognition using EEG signals
TL;DR: The results support the point that emotions are progressively activated throughout the experiment, and the weighting coefficients based on the correlation coefficient and the entropy coefficient can effectively improve the EEG-based emotion recognition accuracy.
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Hierarchical Lifelong Learning by Sharing Representations and Integrating Hypothesis
TL;DR: Experiments show that the proposed novel hierarchical lifelong learning algorithm (HLLA) method outperforms many other recent LML algorithms, especially when dealing with higher dimensional, lower correlation, and fewer labeled data problems.