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Your search keyword '"Ma, Yue"' showing total 414 results
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414 results on '"Ma, Yue"'

15. Enhancing Nonlinear Interferometers for Imaging with Undetected Photons: Seeding and High-Gain

Author

Pearce, Emma, primary, Flórez, Jefferson, additional, Gemmell, Nathan R., additional, Karzazil, Mariame Abire, additional, Ma, Yue, additional, Bressanini, Gabriele, additional, Battle, Ronan A., additional, Murray, Robert T., additional, Kim, Myungshik S., additional, Phillips, Chris C., additional, Oulton, Rupert F., additional, and Clark, Alex S., additional

Published
2023
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27. Hierarchical Graph Convolutional Networks for Action Quality Assessment

Author

Zhou, Kanglei, Ma, Yue, Shum, Hubert P. H., and Liang, Xiaohui

Abstract

Action quality assessment (AQA) automatically evaluates how well humans perform actions in a given video, a technique widely used in fields such as rehabilitation medicine, athletic competitions, and specific skills assessment. However, existing works that uniformly divide the video sequence into small clips of equal length suffer from intra-clip confusion and inter-clip incoherence, hindering the further development of AQA. To address this issue, we propose a hierarchical graph convolutional network (GCN). First, semantic information confusion is corrected through clip refinement, generating the ‘shot’ as the basic action unit. We then construct a scene graph by combining several consecutive shots into meaningful scenes to capture local dynamics. These scenes can be viewed as different procedures of a given action, providing valuable assessment cues. The video-level representation is finally extracted via sequential action aggregation among scenes to regress the predicted score distribution, enhancing discriminative features and improving assessment performance. Experiments on the AQA-7, MTLAQA, and JIGSAWS datasets demonstrate the superiority of the proposed hierarchical GCN over state-of-the-art methods.

Published
2023

40. Coastal Bathymetry Determined From Water Waves Observed by Airborne Lidars: A Case Study Near Ganquan Island, South China Sea.

Author

Zhang, Ting, Yang, Jian, Ma, Yue, Xu, Nan, Zhou, Hui, and Wang, Xiao Hua

Abstract

Passive multispectral imaging and active bathymetric lidar make a great achievement for bathymetry in optically shallow waters. However, due to the attenuation of the water column precludes deep penetration of the light, accurately obtaining the underwater topography in turbid waters through remote sensing techniques, both passive and active, is still a challenging task. Airborne lidars can obtain water surface topography with high accuracy and resolution, which can further be used to derive the water depth based on wave theory. In this study, an “indirect” method to determine water depth is proposed using airborne lidar measured water surface points. As the wavelength and wave direction can be accurately tracked from the water surface topography, a $20\times20$ m underwater topography near Ganquan Island, South China Sea, is generated with a root mean square error (RMSE) of 0.91 m and a mean absolute percent error (MAPE) of 7.1%. The basic theory of deriving water depths is totally different from airborne lidar bathymetry (ALB), i.e., this method is independent of water clarity and can be used in turbid waters or even works with near infrared airborne lidar that can only obtain water surface points. [ABSTRACT FROM AUTHOR]

Published
2023
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41. Examining the Consistency of Lidar Attenuation Coefficient K lidar From ICESat-2 and Diffuse Attenuation Coefficient K d From MODIS.

Author

Yang, Jian, Zheng, Huiying, Ma, Yue, Zhao, Pufan, Zhou, Hui, Li, Song, and Wang, Xiao Hua

Abstract

The new generation photon-counting lidar on Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can obtain the subsurface optical properties of sea waters. Recent studies highlight the applications of deriving the lidar attenuation coefficient $K_{\mathrm {lidar}}$ and then substituting $K_{\mathrm {lidar}}$ into the bio-optical model to obtain more information of sea waters. As commonly used bio-optical models are built for the diffuse attenuation coefficient $K_{d}$ that are traditionally derived by passive ocean color sensors, whether $K_{\mathrm {lidar}}$ derived from ICESat-2 can be directly used as $K_{d}$ is a fundamental question. Given that $K_{d}$ is an apparent optical property (AOP) in the water column rather than an inherent optical property (IOP), $K_{d}$ is closely related to the zenith angle of the incident light. The zenith angle of the incident light of the sunlight is normally tens of degrees for ocean color sensors, while the maximum laser off-nadir angle is ~1.5° for the ICESat-2 lidar. To demonstrate this issue, we select hundreds of ground tracks of ICESat-2 in both open ocean and coastal sea waters and compare the derived $K_{\mathrm {lidar}}$ with their corresponding Moderate Resolution Imaging Spectroradiometer (MODIS)-derived $K_{d}$. The results indicate that the corrected results of $1.2\times K_{\mathrm {lidar}}$ , instead of the direct results of $K_{\mathrm {lidar}}$ , are more consistent with MODIS $K_{d}$. This study is of great significance to the better fusion of active lidar data and passive optical data in ocean observations. [ABSTRACT FROM AUTHOR]

Published
2023
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