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Shuangjun Liu
Northeastern University
7Publications
3H-index
16Citations
Publications 7
Newest
#1Shuangjun Liu (NU: Northeastern University)H-Index: 3
#2Sarah Ostadabbas (NU: Northeastern University)H-Index: 10
Human in-bed pose estimation has huge practical values in medical and healthcare applications yet still mainly relies on expensive pressure mapping (PM) solutions. In this paper, we introduce our novel physics inspired vision-based approach that addresses the challenging issues associated with the in-bed pose estimation problem including monitoring a fully covered person in complete darkness. We reformulated this problem using our proposed Under the Cover Imaging via Thermal Diffusion (UCITD) me...
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#2Shuangjun LiuH-Index: 3
Last.Swastik KarH-Index: 28
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Despite its ability to draw precise inferences from large and complex data sets, the use of data analytics in the field of condensed matter and materials sciences—where vast quantities of complex metrology data are regularly generated—has remained surprisingly limited. Specifically, such approaches could dramatically reduce the engineering complexities of devices that directly exploit the physical properties of materials. Here, we present a cyber-physical system for accurately estimating the wav...
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#2Shuangjun LiuH-Index: 3
Last.Swastik KarH-Index: 28
view all 4 authors...
Despite its ability to draw precise inferences from large and complex datasets, the use of data analytics in the field of condensed matter and materials sciences -- where vast quantities of complex metrology data are regularly generated -- has remained surprisingly limited. Specifically, such approaches could dramatically reduce the engineering complexities of devices that directly exploit the physical properties of materials. Here, we present a cyber-physical system for accurately estimating th...
Sep 8, 2018 in ECCV (European Conference on Computer Vision)
#1Shuangjun Liu (NU: Northeastern University)H-Index: 3
#2Sarah Ostadabbas (NU: Northeastern University)H-Index: 10
Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned. However, most successful frameworks share a common procedure which performs an image-to-image translation with pose of figures in the image untouched. When the objective is reposing a figure in an image while preserving the rest of the image, the state-of-the-art mainly assumes a single rigid body with s...
5 CitationsSource
Sep 8, 2018 in ECCV (European Conference on Computer Vision)
#1Shuangjun Liu (NU: Northeastern University)H-Index: 3
#2Sarah Ostadabbas (NU: Northeastern University)H-Index: 10
Deep learning approaches have been rapidly adopted across a wide range of fields because of their accuracy and flexibility, but require large labeled training datasets. This presents a fundamental problem for applications with limited, expensive, or private data (i.e. small data), such as human pose and behavior estimation/tracking which could be highly personalized. In this paper, we present a semi-supervised data augmentation approach that can synthesize large scale labeled training datasets u...
1 CitationsSource
Although human pose estimation for various computer vision (CV) applications has been studied extensively in the last few decades, yet in-bed pose estimation using camera-based vision methods has been ignored by the CV community because it is assumed to be identical to the general purpose pose estimation methods. However, in-bed pose estimation has its own specialized aspects and comes with specific challenges including the notable differences in lighting conditions throughout a day and also hav...
4 CitationsSource
Oct 1, 2017 in ICCV (International Conference on Computer Vision)
#1Shuangjun Liu (NU: Northeastern University)H-Index: 3
#2Sarah Ostadabbas (NU: Northeastern University)H-Index: 10
Tracking human sleeping postures over time provides critical information to biomedical research including studies on sleeping behaviors and bedsore prevention. In this paper, we introduce a vision-based tracking system for pervasive yet unobtrusive long-term monitoring of in-bed postures in different environments. Once trained, our system generates an in-bed posture tracking history (iPoTH) report by applying a hierarchical inference model on the top view videos collected from any regular off-th...
3 CitationsSource
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