![Xin Zhao BSc, PhD](https://profiles-cardiff.imgix.net/attachments/7166?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Dr Xin Zhao
(she/her)
BSc, PhD
Teams and roles for Xin Zhao
Teaching Associate
Overview
COMSC
The interests of my research area are how Image quality assessment (IQA) could evaluate, monitor and optimise modern imaging systems.
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. et al. 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 2022. Proceedings of IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE. (10.1109/IVMSP54334.2022.9816306)
2021
- Guo, P. et al., 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. Proceedings of the IEEE International Conference on Image Processing. IEEE. , pp.1459-1463. (10.1109/ICIP42928.2021.9506154)
- Lou, J. et al. 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 2021. 2021 IEEE International Conference on Image Processing (ICIP). IEEE. , pp.1474-1478. (10.1109/ICIP42928.2021.9506017)
- Guo, P. et al., 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. et al. 2020. Deep learning vs. traditional algorithms for saliency prediction of distorted images. Presented at: 27th IEEE International Conference on Image Processing (ICIP 2020) Abu Dhabi, United Arab Emirates 25-28 October 2020. Proceedings of the International Conference on Image Processing. IEEE. , pp.156-160. (10.1109/ICIP40778.2020.9191203)
Articles
- Zhao, X. et al. 2023. CUDAS: Distortion-aware saliency benchmark. IEEE Access 11 , pp.58025-58036. (10.1109/ACCESS.2023.3283344)
- Guo, P. et al., 2021. Convex optimization method for quantifying image quality induced saliency variation. IEEE Access 9 , pp.111533-111543. (10.1109/ACCESS.2021.3102465)
Conferences
- Dong, Z. et al. 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 2022. Proceedings of IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE. (10.1109/IVMSP54334.2022.9816306)
- Guo, P. et al., 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. Proceedings of the IEEE International Conference on Image Processing. IEEE. , pp.1459-1463. (10.1109/ICIP42928.2021.9506154)
- Lou, J. et al. 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 2021. 2021 IEEE International Conference on Image Processing (ICIP). IEEE. , pp.1474-1478. (10.1109/ICIP42928.2021.9506017)
- Zhao, X. et al. 2020. Deep learning vs. traditional algorithms for saliency prediction of distorted images. Presented at: 27th IEEE International Conference on Image Processing (ICIP 2020) Abu Dhabi, United Arab Emirates 25-28 October 2020. Proceedings of the International Conference on Image Processing. IEEE. , pp.156-160. (10.1109/ICIP40778.2020.9191203)
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
Currently Supporting:
CM1101 Computational Thinking
CM1102 Web Application
CM2203 Informatics
CM2305 Group projects supervision