H
Hong Cheng
Researcher at University of Electronic Science and Technology of China
Publications - 198
Citations - 4526
Hong Cheng is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Exoskeleton & Object detection. The author has an hindex of 33, co-authored 192 publications receiving 3452 citations. Previous affiliations of Hong Cheng include Carnegie Mellon University & Xi'an Jiaotong University.
Papers
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Journal ArticleDOI
Survey on 3D Hand Gesture Recognition
Hong Cheng,Lu Yang,Zicheng Liu +2 more
TL;DR: This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors, and reviews the commercial depth sensors and public data sets that are widely used in this field.
Book ChapterDOI
Contour Knowledge Transfer for Salient Object Detection
TL;DR: A contour-to-saliency transferring method to automatically generate salient object masks which can be used to train the saliency branch from outputs of the contour branch, and introduces a novel alternating training pipeline to gradually update the network parameters.
Journal ArticleDOI
Springrobot: a prototype autonomous vehicle and its algorithms for lane detection
Qing Li,Nanning Zheng,Hong Cheng +2 more
TL;DR: Experimental results in different road scene and a comparison with other methods have proven the validity of the proposed method, and the architecture and strategy for the system are briefly described.
Proceedings ArticleDOI
Sparsity induced similarity measure for label propagation
Hong Cheng,Zicheng Liu,Jie Yang +2 more
TL;DR: This paper presents a novel technique to measure the similarities among data points by decomposing each data point as an L1 sparse linear combination of the rest of the data points, and shows that the proposed Sparsity Induced Similarity (SIS) measure significantly improves label propagation performance.
Journal ArticleDOI
Sparse representation and learning in visual recognition: Theory and applications
TL;DR: A survey of some recent work on sparse representation, learning and modeling with emphasis on visual recognition, and the applications of sparse theory to various visual recognition tasks are introduced.