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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.

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Book ChapterDOI

The sixth visual object tracking VOT2018 challenge results

Matej Kristan, +158 more
TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Proceedings ArticleDOI

Jointly Modeling Embedding and Translation to Bridge Video and Language

TL;DR: Liu et al. as discussed by the authors presented a unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visualsemantic embedding.
Proceedings ArticleDOI

The Visual Object Tracking VOT2017 Challenge Results

Matej Kristan, +104 more
TL;DR: The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative; results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years.
Posted Content

Jointly Modeling Embedding and Translation to Bridge Video and Language

TL;DR: A novel unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visual- semantic embedding and outperforms several state-of-the-art techniques in predicting Subject-Verb-Object (SVO) triplets.
Journal ArticleDOI

Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy

TL;DR: A novel marker-controlled watershed based on mathematical morphology is proposed, which can effectively segment clustered cells with less oversegmentation and design a tracking method based on modified mean shift algorithm, in which several kernels with adaptive scale, shape, and direction are designed.