![Xin Zhao](https://profiles-cardiff.imgix.net/attachments/3466?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Miss Xin Zhao
Teaching Associate
Overview
I am a first year PhD student at COMSC department.
The interests of my research area are how Image quality assessment (IQA) could evaluate, monitor and optimise of modern imaging systems.
Teaching Support (Lab demonstration) on module: Database System
Publication
2023
- Zhao, X. et al. 2023. CUDAS: Distortion-aware saliency benchmark. IEEE Access 11, pp. 58025-58036. (10.1109/ACCESS.2023.3283344)
2022
- Dong, Z., Wu, X., Zhao, X., Zhang, F. and Liu, H. 2022. Identifying pitfalls in the evaluation of saliency models for videos. Presented at: IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), Nafplio, Greece, 26-29 June 2022Proceedings of IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE, (10.1109/IVMSP54334.2022.9816306)
2021
- Lou, J., Zhao, X., Young, P., White, R. and Liu, H. 2021. Study of saccadic eye movements in diagnostic imaging. Presented at: 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, 19-22 September 20212021 IEEE International Conference on Image Processing (ICIP). IEEE pp. 1474-1478., (10.1109/ICIP42928.2021.9506017)
- Guo, P., Zhao, X., Zeng, D. and Liu, H. 2021. A metric for quantifying image quality induced saliency variation. Presented at: 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, 19-22 September 2021.
- Guo, P., Zhao, X., Wu, D., Zhang, L., Zeng, D. and Liu, H. 2021. Convex optimization method for quantifying image quality induced saliency variation. IEEE Access 9, pp. 111533-111543. (10.1109/ACCESS.2021.3102465)
2020
- Zhao, X., Lin, H., Guo, P., Saupe, D. and Liu, H. 2020. Deep learning vs. traditional algorithms for saliency prediction of distorted images. Presented at: 27th IEEE International Conference on Image Processing (ICIP 2020), United Arab Emirates, 25-28 October 2020.
Cynadleddau
- Dong, Z., Wu, X., Zhao, X., Zhang, F. and Liu, H. 2022. Identifying pitfalls in the evaluation of saliency models for videos. Presented at: IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), Nafplio, Greece, 26-29 June 2022Proceedings of IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE, (10.1109/IVMSP54334.2022.9816306)
- Lou, J., Zhao, X., Young, P., White, R. and Liu, H. 2021. Study of saccadic eye movements in diagnostic imaging. Presented at: 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, 19-22 September 20212021 IEEE International Conference on Image Processing (ICIP). IEEE pp. 1474-1478., (10.1109/ICIP42928.2021.9506017)
- Guo, P., Zhao, X., Zeng, D. and Liu, H. 2021. A metric for quantifying image quality induced saliency variation. Presented at: 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, 19-22 September 2021.
- Zhao, X., Lin, H., Guo, P., Saupe, D. and Liu, H. 2020. Deep learning vs. traditional algorithms for saliency prediction of distorted images. Presented at: 27th IEEE International Conference on Image Processing (ICIP 2020), United Arab Emirates, 25-28 October 2020.
Erthyglau
- Zhao, X. et al. 2023. CUDAS: Distortion-aware saliency benchmark. IEEE Access 11, pp. 58025-58036. (10.1109/ACCESS.2023.3283344)
- Guo, P., Zhao, X., Wu, D., Zhang, L., Zeng, D. and Liu, H. 2021. Convex optimization method for quantifying image quality induced saliency variation. IEEE Access 9, pp. 111533-111543. (10.1109/ACCESS.2021.3102465)
Research
Proposed Research Topic: EYE ATTENTIVE PREDICTION FROM THE MEDICAL PROFESSIONALS IN THE MEDICAL IMAGE
The proposed research aims to have a better understanding of eye movements in the context of diagnostic radiology, identify distinctive viewing strategies of image readers at different levels of expertise and develop computational methods to automatically and dynamically aid image readers to render optimum diagnostic decisions and trainees to achieve best training outcomes.
Teaching
Teaching Support (Lab demonstration) on the module:
Database System.
Data Processing and Visualisation.
Supervisions: Year 2- Group Project