![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
(hi/ei)
BSc, PhD
Timau a rolau for Xin Zhao
Cydymaith Addysgu
Trosolwyg
COMSC
Diddordebau fy maes ymchwil yw sut y gallai asesu 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. 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)
Cynadleddau
- 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)
Erthyglau
- 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)
Ymchwil
Pwnc Ymchwil Arfaethedig: RHAGFYNEGIAD SYLWGAR LLYGAID GAN Y GWEITHWYR PROFFESIYNOL MEDDYGOL YN Y DDELWEDD FEDDYGOL
Nod yr ymchwil arfaethedig yw cael gwell dealltwriaeth o symudiadau'r llygaid yng nghyd-destun radioleg ddiagnostig, nodi strategaethau gwylio nodedig darllenwyr delweddau ar wahanol lefelau arbenigedd a datblygu dulliau cyfrifiadurol i gynorthwyo darllenwyr delweddau yn awtomatig ac yn ddeinamig i wneud y penderfyniadau diagnostig gorau posibl a hyfforddeion i gyflawni'r canlyniadau hyfforddi gorau.
Addysgu
Ar hyn o bryd yn cefnogi:
CM1101 Meddwl Cyfrifiadurol
CM1102 Cais Gwe
CM2203 Gwybodeg
CM2305 Goruchwylio prosiectau grŵp