Doctor Jing Wang PhD, BEng
Lecturer
Summary
I am engaged in research and teaching in the field of computer science and software engineering.
About
I gained my BEng degree in machine and electronic technology from the Xidian University, China, in 2006.
After graduation, I was appointed as a software engineer and carried out development work on Computer Vision (CV)-based quality control systems. These included assembly line monitoring and industrial robotic controls.
In 2008, I became a postgraduate student in the University of Huddersfield and gained my PhD degree at 2012. I then became a research fellow and carried out independent research on image processing, analysing and understanding. I joined Sheffield Hallam University in 2017.
My interests are focused on the real-world applications of computer vision systems, and I have published more than 20 journal and conference papers in fields related to this.
I am a member of British Machine Vision Association (BMVA) and British Computer Society (BCS).
I also served as chair and editor for International Conference on Automation and Computing.
Publications
Journal articles
Marchang, J., Wang, J., Otebolaku, A., Enamamu, T., Porter, D., & Sanders, B. (2019). Multidimensional: User with File Content and Server’s status based Authentication for Secure File Operations in Cloud. Current Trends in Computer Sciences & Applications (CTCSA), 1 (5), 108-118. http://doi.org/10.32474/CTCSA.2019.01.000121
Ho, Y., Lugea, J., McIntyre, D., Wang, J., & Xu, Z. (2018). Projecting (un)certainty : a text-world analysis of three statements from the Meredith Kercher murder case. English Text Construction, 11 (2), 285-316. http://doi.org/10.1075/etc.00012.ho
Ho, Y., Lugea, J., McIntyre, D., Xu, Z., & Wang, J. (2018). Text-world annotation and visualization for crime narrative reconstruction. Digital Scholarship in the Humanities. http://doi.org/10.1093/llc/fqy044
Hao, Y., Xu, Z.-.J., Liu, Y., Wang, J., & Fan, J.-.L. (2018). Effective crowd anomaly detection through spatio-temporal texture analysis. International Journal of Automation and Computing. http://doi.org/10.1007/s11633-018-1141-z
Xu, Y., Lu, L., Xu, Z., He, J., Wang, J., Huang, J., & Lu, J. (2018). Towards Intelligent Crowd Behavior Understanding through the STFD DescriptorExploration. Sensing and Imaging. http://doi.org/10.1007/s11220-018-0201-3
Zhang, C., Xu, Y., Xu, Z., He, J., Wang, J., & Adu, J. (2018). A fuzzy neural network based dynamic data allocationmodel on heterogeneous multi-GPUs for large-scalecomputations. International Journal of Automation and Computing, 1-13. http://doi.org/10.1007/s11633-018-1120-4
Wang, J., & Xu, Z. (2016). Spatio-temporal Texture Modelling for Real-time Crowd Anomaly Detection. Computer Vision and Image Understanding, 144, 177-187. http://doi.org/10.1016/j.cviu.2015.08.010
Al-Obaidi, K., Wang, J., & Hossain, M. (n.d.). Data-driven Urbanism: Image Processing Techniques for Urban Analytics. .
Conference papers
Han, P., Wang, D., Yang, X., Liu, Y., Li, D., Xu, Z., & Wang, J. (2019). An improved adaptive correction algorithm for non-uniform illumination panoramic image. Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019, 258-262. http://doi.org/10.1109/ICEICT.2019.8846270
Hao, Y., Xu, Z., Liu, Y., Wang, J., & Fan, J. (2018). A graphical simulator for modeling complex crowd behaviors. In 22 International Conference Information Visualisation, (pp. 6-11). IEEE: http://doi.org/10.1109/iV.2018.00012
Hao, Y., Xu, Z., Liu, Y., Wang, J., & Fan, J. (2018). Crowd Synthesis Based on Hybrid Simulation Rules for Complex Behaviour Analysis. 2018 24th International Conference on Automation and Computing (ICAC). http://doi.org/10.23919/iconac.2018.8749070
Penders, J., Wang, J., Bhowmik, D., Di Nuovo, A., Soranzo, A., Rolph, J., ... Cameron, D. (2018). Robots claiming space: gauging public reaction usingcomputer vision techniques. In Giuliani, M., Assaf, T., & Giannaccini, M.E. (Eds.) Towards autonomous robotic systems: 19th annual conference, TAROS 2018 proceedings, (pp. 468-470). Springer: http://doi.org/10.1007/978-3-319-96728-8
Yu, H., Xu, Z., Wang, J., Liu, Y., & Fan, J. (2017). An effective video processing pipeline for crowd pattern analysis. In 2017 23rd International Conference on Automation and Computing (ICAC). IEEE: http://doi.org/10.23919/IConAC.2017.8082025
Wang, J., Xu, Z., Cao, Y., & Yuanping, X. (2017). Wavelet-based Texture Model for Crowd Dynamic Analysis. In 23rd International Conference on Automation & Computing, Huddersfield, 7 September 2017 - 8 September 2017.
Hao, Y., Wang, J., Liu, Y., Xu, Z., & Fan, J. (2017). Extracting Spatio-Temporal Texture signatures for crowd abnormality detection. 2017 23rd International Conference on Automation and Computing (ICAC). http://doi.org/10.23919/iconac.2017.8082051
Mawanda, R., Keishing, S., Wang, J., & Marchang, J. (2024). Secure by Design Smart Authentication for Care Robots to Support the Elderly. In ICIT 2024. The 25th IEEE International Conference on Industrial Technology), Bristol, UK, 25 March 2024 - 27 March 2024.
Book chapters
Mazumdar, S., & Wang, J. (2018). Cyber-situation awareness: a visual analytics perspective. In Parkinson, S., Crampton, A., & Hill, R. (Eds.) Guide to vulnerability analysis for computer networks and systems : an artificial intelligence approach. Springer: http://doi.org/10.1007/978-3-319-92624-7
Wang, J., & Xu, Z. (2016). A graph theory-based online keywords model for image semantic extraction. In SAC '16 Proceedings of the 31st Annual ACM Symposium on Applied Computing. (pp. 67-72). ACM: http://doi.org/10.1145/2851613.2851633
Wang, J., Ho, Y., Xu, Z., McIntyre, D., & Lugea, J. (2016). The visualisation of cognitive structures in forensic statements. In Banissi, E., Bannatyne, M.W.M., Bouali, F., Burkhard, R., Counsell, J., Cvek, U., ... Zhang, J.J. (Eds.) 2016 20th International Conference Information Visualisation (IV). IEEE: http://doi.org/10.1109/IV.2016.60
Wang, J., & Xu, Z. (2015). Crowd anomaly detection for automated video surveillance. In 6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15). IET: http://doi.org/10.1049/ic.2015.0102
Wang, J., & Xu, Z. (2014). Bayesian inferential reasoning model for crime investigation. In Neves-Silva, R., Tshirintzis, G.A., & Uskov, V. (Eds.) Smart digital futures 2014. (pp. 59-67). IOS Press: http://doi.org/10.3233/978-1-61499-405-3-59