Han YANG

I am an undergraduate student and research assistant in BNU-HKBU University United International College. During that, I worked in Key Laboratory for Artificial Intelligence and Multi-Modal Data Processing of Department of Education of Guangdong Province, working with Prof. Amy ZHANG.

My research interest lies in Medical Robotics, 3D Computer Vision including 3D scene understanding, 3D reconstruction, and Object detection.

I am researching in Mediacal Robotics and Machine Vision, if you are interseted, please contact me via email!

news

Sep 23, 2022	I am going to join Prof. ZHANG zhuoran's research group at CUHK(SZ) as a Ph.D student!
Aug 25, 2022	Our work on Cervical Cancer Classification using Convolutional Neural Network(CNN) is accepted to ICBSP 2022.
Aug 21, 2022	I won the national third prize of China University Student Computer Design Competition at Nanjing 2022.
Aug 16, 2022	Our work on implicit neural field representation and rendering of Wind Turbine scenes using Neural Radiance Fields(NeRF) is accepted to TEPEN 2022.

selected publications

Full publication list can be found on Google Scholar.
^*equal contribution; ^♯corresponding author.

2022

HQNet: An Efficient Convolutional Neural Network for Cervical Cancer Classification

Han YANG, and Teoh Teik Toe,

IEEE on International Conference on Biomedical Imaging, Signal Processing(ICBSP) 2022
(Oral Presentation)

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With the rapid development of artificial intelligence, cancer cell classification recognition is also gradually intelligent. Although the classification and recognition methods for some common cancers are mature, Cervical Cancer, as a common cancer among middle-aged women, is often neglected in diagnosis because of the low incidence of malignant tumors. In recent years, the incidence of Cervical Cancer has been increasing year by year and has also gained attention. In this paper, an efficient deformable convolutional neural network system(HQNet) was constructed to identify Cervical Cancer cells with different degrees of development.
WTBNeRF: Wind Turbine Blade 3D Reconstruction by Neural Radiance Fields

Han YANG^*, Linchuan TANG^*, Hui MA, Rongfeng DENG, and Hui ZHANG

The Efficiency and Performance Engineering Network(TEPEN) 2022

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The Neural radiation field for scene rendering is gradually be- coming a widely used method in novel view synthesis task. With the gradual popularization of wind turbines, the requirement for integrity detection of wind turbines operating in natural or extreme environments is gradually increasing. In order to better complete the state detection of wind turbines, rendering real wind turbine scenes and 3D reconstruction of wind turbines become crucial. For the original neural radiation field method, the scene or object with a large number of features or complex textures is required. But for wind turbines, the surface is too smooth and texture-free, which creates blurring and ghosting in the scene. The method we use, called WTBNeRF is a network dedicated to wind turbine scene rendering and 3D reconstruction of wind turbines. We use conical truncated cone rays for more detailed coverage of individual pixel ranges than single pixel-centered rays, effectively reducing jaggedness and blur- ring in smooth, low-texture wind turbine scenes. At the same time, get- ting accurate camera pose for low-texture objects and scenes is also a challenge. We use a pre-trained camera pose estimation neural radiation field network to predict the camera pose of the data set, which reduces the requirement of knowing the real camera parameters of the data in advance. Moreover, we simplify the network structure in the network design, so that the network training time is also significantly improved, and the speed is about 10 times faster than NeRF [1] for the multi-scale wind turbine dataset we produced.