Dr Qingde Li has been a lecturer in computer science at the University of Hull since 2001. Previously he was Professor and Deputy Head of Mathematics and Computer Science at Anhui Normal University, China.
Dr Li's research findings have been published in the world's most prestigious graphics journals including ACM Transactions on Graphics, IEEE Transactions on Visualization and Computer Graphics, and IEEE Transactions on Medical Imaging.
Dr Li@Google scholar: https://scholar.google.co.uk/citations?user=abSAMUkAAAAJ&hl=en
Towards additive manufacturing oriented geometric modeling using implicit functions
Li, Q., Hong, Q., Qi, Q., Ma, X., Han, X., & Tian, J. (2018). Towards additive manufacturing oriented geometric modeling using implicit functions. Visual Computing for Industry, Biomedicine, and Art, 1(1), https://doi.org/10.1186/s42492-018-0009-y
A Survey of the Methods on Fingerprint Orientation Field Estimation
Bian, W., Xu, D., Li, Q., Cheng, Y., Jie, B., & Ding, X. (2019). A Survey of the Methods on Fingerprint Orientation Field Estimation. IEEE access : practical innovations, open solutions, 7, 32644-32663. https://doi.org/10.1109/access.2019.2903601
High precision implicit modeling for patient-specific coronary arteries
Hong, Q., Li, Q., Wang, B., Liu, K., & Qi, Q. (2019). High precision implicit modeling for patient-specific coronary arteries. IEEE access : practical innovations, open solutions, 7, 72020-72029. https://doi.org/10.1109/access.2019.2920113
Prior knowledge-based deep learning method for indoor object recognition and application
Ding, X., Luo, Y., Li, Q., Cheng, Y., Cai, G., Munnoch, R., …Wang, B. (2018). Prior knowledge-based deep learning method for indoor object recognition and application. Systems Science and Control Engineering, 6(1), 249-257. https://doi.org/10.1080/21642583.2018.1482477
Dr Li's current research interests are in the areas of computer graphics, visual and tangible computing, as well as their applications in mixed reality environment , including
(1). High precision geometric reconstruction of vascular and neural structures.
(2). Deep learning on shape understanding and geometric information processing.
(3). Tactile modelling and haptic effects programming.
(4). Environmental geometry-aware wearable devices.
Dr Li welcomes applications in the areas of visual and tangible computing, deep learning in geometric information processing and scientific simulation, cloud-based collaborative geometric design in mixed reality environment, as well as developing environmental geometry-aware wearable devices.