Dr Osman Tursun
Faculty of Engineering,
School of Electrical Engineering & Robotics
Biography
Dr Tursun is a Postdoctoral Research Fellow in Computer Vision and Machine Learning based on the Signal Processing, Artificial Intelligence and Vision Technologies (SAIVT) research group at Queensland University of Technology (QUT). Dr Osman Tursun completed his PhD in the Computer Vision field at QUT. His research focuses on large-scale image searching, semantic object detection and segmentation and natural language processing.Dr Tursun is also an Advanced Queensland Industry Research Fellow. In 2023, he received a $240,000 fellowship to undertake a project titled AI-enabled Fragmentation and Ore Intelligence through Machine Vision. This project aims to develop advanced machine vision techniques to create the first AI-enabled real-time fragmentation analysis mining software platform with industry partner Orica Digital Solutions.
Personal details
Positions
- Research Fellow
Faculty of Engineering,
School of Electrical Engineering & Robotics
Keywords
Large-scale Image Retrieval, Image Editing, Semantic Segmentation, Natural Language Processing
Research field
Artificial Intelligence and Image Processing
Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2008
Qualifications
- Doctor of Philospohy (Queensland University of Technology)
Teaching
EGH444 Digital Signals and Image Processing
CAB320 Artificial Intelligence
CAB420 Machine Learning
EGH404 Research in Engineering Practice
Publications
- Tursun, O., Denman, S., Sridharan, S., Goan, E. & Fookes, C. (2022). An efficient framework for zero-shot sketch-based image retrieval. Pattern Recognition, 126. https://eprints.qut.edu.au/227841
- Tursun, O., Denman, S., Sridharan, S. & Fookes, C. (2022). Learning test-time augmentation for content-based image retrieval. Computer Vision and Image Understanding, 222. https://eprints.qut.edu.au/232859
- Tursun, O., Denman, S., Sridharan, S. & Fookes, C. (2021). Learning Regional Attention Over Multi-Resolution Deep Convolutional Features For Trademark Retrieval. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), 2393–2397. https://eprints.qut.edu.au/212860
- Tursun, O., Denman, S., Zeng, R., Sivapalan, S., Sridharan, S. & Fookes, C. (2020). MTRNet++: One-stage mask-based scene text eraser. Computer Vision and Image Understanding, 201. https://eprints.qut.edu.au/207912
- Tursun, O., Denman, S., Sivapalan, S., Sridharan, S., Fookes, C. & Mau, S. (2022). Component-based Attention for Large-scale Trademark Retrieval. IEEE Transactions on Information Forensics and Security, 17, 2350–2363. https://eprints.qut.edu.au/136009
- Tursun, O., Zeng, R., Denman, S., Sivapalan, S., Sridharan, S. & Fookes, C. (2019). MTRNet: A Generic Scene Text Eraser. Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019, 39–44. https://eprints.qut.edu.au/180464
QUT ePrints
For more publications by Osman, explore their research in QUT ePrints (our digital repository).
Supervision
Looking for a postgraduate research supervisor?
I am currently accepting research students for Honours, Masters and PhD study.
You can browse existing student topics offered by QUT or propose your own topic.