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Chenqiang Gao
Researcher at Chongqing University of Posts and Telecommunications
Publications - 107
Citations - 2870
Chenqiang Gao is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 17, co-authored 94 publications receiving 1834 citations. Previous affiliations of Chenqiang Gao include Carnegie Mellon University & Huazhong University of Science and Technology.
Papers
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Journal ArticleDOI
Infrared Patch-Image Model for Small Target Detection in a Single Image
TL;DR: Extensive synthetic and real data experiments show that the proposed small target detection method not only works more stably for different target sizes and signal-to-clutter ratio values, but also has better detection performance compared with conventional baseline methods.
Proceedings ArticleDOI
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
TL;DR: DecDecideNet as discussed by the authors proposes an end-to-end crowd counting framework, which can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions.
Proceedings ArticleDOI
Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising
TL;DR: This paper proposes an effective MSI denoising approach by combinatorially considering two intrinsic characteristics underlying an MSI: the nonlocal similarity over space and the global correlation across spectrum.
Journal ArticleDOI
Image Classification by Cross-Media Active Learning With Privileged Information
TL;DR: A novel cross-media active learning algorithm is proposed to reduce the effort on labeling images for training, and train classifiers on both visual features and privileged information, and measure the uncertainty of unlabeled data by exploiting the learned classifiers and slacking function.
Journal ArticleDOI
Infrared small-dim target detection based on Markov random field guided noise modeling
TL;DR: This work treats the small-dim targets as a special sparse noise component of the complex background noise and adopt Mixture of Gaussians (MoG) with Markov random field (MRF) with MRF to model the small target detection problem.