activity
20182021
most cited3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model

17 citations · 31 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2021★ 1 cited

Projection-free Graph-based Classifier Learning using Gershgorin Disc Perfect Alignment

Cheng Yang, Gene Cheung, Guangtao Zhai

In semi-supervised graph-based binary classifier learning, a subset of known labels are used to infer unknown labels, assuming that the label signal is smo…

cs.CV2020

Identification of deep breath while moving forward based on multiple body regions and graph signal analysis

Yunlu Wang, Cheng Yang, Menghan Hu +4

This paper presents an unobtrusive solution that can automatically identify deep breath when a person is walking past the global depth camera. Existing non-contact breath assessmen…

eess.IV2020

Joint Demosaicking / Rectification of Fisheye Camera Images using Multi-color Graph Laplacian Regularization

Fengbo Lan, Cheng Yang, Gene Cheung +1

To compose a 360 image from a rig with multiple fisheye cameras, a conventional processing pipeline first performs demosaicking on each fisheye camera's Bayer-patterned grid, then…

cs.LG2020

Signed Graph Metric Learning via Gershgorin Disc Perfect Alignment

Cheng Yang, Gene Cheung, Wei Hu

Given a convex and differentiable objective $Q(\M)$ for a real symmetric matrix $\M$ in the positive definite (PD) cone -- used to compute Mahalanobis distances -- we propose a fas…

cs.RO2018★ 1 cited

Avalon: Building an Operating System for Robotcenter

Yuan Xu, Zhiyuan Yan, Sa Wang +3

This paper envisions a scenario that hundreds of heterogeneous robots form a robotcenter which can be shared by multiple users and used like a single powerful robot to perform comp…

eess.SP2018★ 12 cited

Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation

Chinthaka Dinesh, Gene Cheung, Ivan V. Bajic +1

Acquired 3D point cloud data, whether from active sensors directly or from stereo-matching algorithms indirectly, typically contain non-negligible noise. To address the point cloud…