H
Heesung Kwon
Researcher at United States Army Research Laboratory
Publications - 146
Citations - 3776
Heesung Kwon is an academic researcher from United States Army Research Laboratory. The author has contributed to research in topics: Object detection & Kernel embedding of distributions. The author has an hindex of 23, co-authored 134 publications receiving 2746 citations. Previous affiliations of Heesung Kwon include University at Buffalo & University of Massachusetts Amherst.
Papers
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Journal ArticleDOI
Kernel adaptive subspace detector for hyperspectral imagery
Heesung Kwon,Nasser M. Nasrabadi +1 more
TL;DR: Experimental results show that the proposed kernel-based ASD outperforms the conventional ASD and a nonlinear anomaly detector so called the kernel RX-algorithm.
Proceedings ArticleDOI
Human-autonomy sensor fusion for rapid object detection
Ryan M. Robinson,Hyungtae Lee,Michael J. McCourt,Amar R. Marathe,Heesung Kwon,Chau Ton,William D. Nothwang +6 more
TL;DR: Fusion of human electroencephalography and button-press responses from rapid serial visual presentation experiments are fused with outputs from trained object detection algorithms and it is demonstrated that fusion of these human classifiers with computer-vision-based detectors improves object detection accuracy.
Journal ArticleDOI
Sparse kernel learning-based feature selection for anomaly detection
TL;DR: A novel framework of sparse kernel learning for support vector data description (SVDD) based anomaly detection is presented and it is shown that the proposed method can provide improved performance over the current state-of-the-art techniques.
Proceedings ArticleDOI
Dual-window-based anomaly detection for hyperspectral imagery
TL;DR: In this article, the authors proposed adaptive anomaly detectors that find materials whose spectral characteristics are substantially different from those of the neighboring materials by generating subspace projection vectors onto which the IWR and OWR vectors are projected.
Proceedings ArticleDOI
Hyperspectral Target Detection Using Kernel Spectral Matched Filter
Heesung Kwon,Nasser M. Nasrabadi +1 more
TL;DR: A non-linear matched filter is introduced for target detection in hyperspectral imagery which is implemented by using the ideas in kernel-based learning theory and simulation results are shown to outperform the linear version.