J
Junhui Hou
Researcher at City University of Hong Kong
Publications - 236
Citations - 6392
Junhui Hou is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 27, co-authored 192 publications receiving 2712 citations. Previous affiliations of Junhui Hou include Northwestern Polytechnical University & Southeast University.
Papers
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Superpixel-Guided Discriminative Low-Rank Representation of Hyperspectral Images for Classification
TL;DR: SP-DLRR as discussed by the authors is composed of two modules, i.e., the classification-guided superpixel segmentation and the discriminative low-rank representation, which are iteratively conducted.
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Learning hyperspectral images from RGB images via a coarse-to-fine CNN
TL;DR: In this paper, a spectral super-resolution network (SSR-Net) was proposed to learn the transformation model between RGB images and HSIs from training data, including a band prediction network and a refinement network.
Posted Content
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection
TL;DR: This paper presents an end-to-end collaborative aggregation-and-distribution network (CoADNet) to capture both salient and repetitive visual patterns from multiple images, and develops a group consistency preserving decoder tailored for the CoSOD task.
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Deep Spatial-angular Regularization for Light Field Imaging, Denoising, and Super-resolution
TL;DR: Guo et al. as discussed by the authors proposed a deep learning-based framework for the reconstruction of high-quality LFs from acquisitions via learned coded apertures, which incorporates the measurement observation into the deep learning framework elegantly to avoid relying entirely on data-driven priors for LF reconstruction.
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Scalable and Compact Representation for Motion Capture Data Using Tensor Decomposition
TL;DR: Experimental results demonstrate that the proposed scheme significantly outperforms existing algorithms in terms of scalability and storage requirement.