![Xin Zhao](https://profiles-cardiff.imgix.net/attachments/7166?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Xin Zhao
(hi/ei)
Timau a rolau for Xin Zhao
Cydymaith Addysgu
Myfyriwr ymchwil
Trosolwyg
PhD yn adran COMSC.
Diddordebau fy maes ymchwil yw sut y gallai asesiad ansawdd delwedd (IQA) werthuso, monitro a gwneud y gorau o systemau delweddu modern.
Cyhoeddiad
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.
Articles
- 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)
Conferences
- 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.
Ymchwil
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.
Addysgu
Cymorth Addysgu (Arddangos labordy) ar y modiwl:
Meddwl Cyfrifiannol CM1101
CM1102 Webapplication