H
Houqiang Li
Researcher at University of Science and Technology of China
Publications - 612
Citations - 17591
Houqiang Li is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Computer science & Motion compensation. The author has an hindex of 57, co-authored 520 publications receiving 12325 citations. Previous affiliations of Houqiang Li include China University of Science and Technology & Nanjing Medical University.
Papers
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Journal ArticleDOI
MCFD: A Hardware-Efficient Noniterative Multicue Fusion Demosaicing Algorithm
TL;DR: The proposed demosaicing algorithm has no learning stage, no iteration operation, a small line buffer and a limited number of parameters, so it can easily be applied to hardware platforms and achieve both high PSNR/SSIM and visual perceptual quality compared to previous state-of-the-art methods.
Proceedings ArticleDOI
Video-Based Compression for Plenoptic Point Clouds
TL;DR: Wang et al. as mentioned in this paper proposed a block-based padding method to unify the unoccupied attribute pixels from different views to reduce their bit cost, based on the observation that these videos from multiple views have very high correlations, they proposed encoding them using multiview high efficiency video coding.
Book ChapterDOI
Scalable Bag of Selected Deep Features for Visual Instance Retrieval
TL;DR: A novel Bag- of-Deep-Visual-Words (BoDVW) model for instance retrieval that achieves respectable performance in comparison to other state-of-the-art methods and is proved to be more effective and efficient on large scale datasets.
Journal ArticleDOI
Improving Person Re-identification with Iterative Impression Aggregation
TL;DR: This work proposes a simple attentional aggregation formulation to instantiate the problem of person re-identification (re-ID), where the representation of a query image is iteratively updated with new information from the candidates in the gallery, and demonstrates competitive performance on standard benchmarks including CUHK03, Market-1501 and DukeMTMC.
Proceedings ArticleDOI
Learning Fine-Grained Motion Embedding for Landscape Animation
TL;DR: Li et al. as mentioned in this paper proposed a fine-grained motion embedding for Landscape animation, which consists of two parts: a motion encoder which embeds time-lapse motion in a finegrained way and a motion generator which generates realistic motion to animate input images.