Dr Jing Wu
Lecturer
School of Computer Science and Informatics
- Available for postgraduate supervision
Overview
I am a lecturer in the School of Computer Science and Informatics at Cardiff University, and a member of the Visual Computing research group. I received PhD from the University of York, United Kingdom, with thesis on statistical approaches to gender classification in the surface normal domain. I received MSc and BSc in Computer Science from Nanjing University, China.
I have broad interests and research experience in computer vision. My main research interests lie in the area of 3D reconstruction, visual understanding, and their applications. My current research focus is on visual SLAM, micro-scale 3D surface imaging, 3D face modelling and processing, and looking into interdisciplinary applications in robotics, manufacturing, and health care. I am also researching into visual analysis tools for explainable machine learning algorithms in computer vision.
Biography
Education and qualifications
- 2009: PhD (Computer Science), University of York, York, UK
- 2005: MSc (Computer Science and Technology), Nanjing University, Nanjing, China
- 2002: BSc (Computer Science and Technology), Nanjing University, Nanjing, China
Career overview
- 2016 - present: Lecturer, School of Computer Science and Informatics, Cardiff University
- 2009 - 2016: Postdoctoral Research Associate, School of Computer Science and Informatics, Cardiff University
Honours and awards
- TAROS Best Paper Award, 2018
- BMVC Best Security Paper Prize, 2007
-
Overseas Research Students Awards Scheme (ORSAS), UK, 2005 - 2008
Professional memberships
- Member of ACM, IEEE, BMVA
- HEA Fellowship
Committees and reviewing
- Programme Committee Member: CGVC (2018/2019/2020), BMVC (2019/2020), WACV (2021)
- Grant reviewer: EPSRC
- Journal and Conference reviewer: CVPR, Computer Graphics Forum, Pattern Recognition, Pattern Recognition Letters, Neurocomputing, The Visual Computer, etc.
Publications
2020
- Cao, K.et al. 2020. Analyzing the noise robustness of deep neural networks. IEEE Transactions on Visualization and Computer Graphics, pp. 1. (10.1109/TVCG.2020.2969185)
- Xiang, S.et al. 2020. Interactive correction of mislabeled training data. Presented at: IEEE VIS 2019, Vancouver, BC, Canada, 20-25 October 20192019 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, (10.1109/VAST47406.2019.8986943)
- Xie, Q.et al. 2020. MLCVNet: multi-level context VoteNet for 3D object detection. Presented at: Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, Washington, USA, 16-18 June 2020.
2019
- Pan, Y.et al. 2019. InSocialNet: Interactive visual analytics for role-event videos. Computational Visual Media 5(4), pp. 375-390. (10.1007/s41095-019-0157-9)
- Wang, X.et al. 2019. Discriminative features matter: Multi-layer bilinear pooling for camera localization. Presented at: BMVC 2019: 30th British Machine Vision Conference, Cardiff, UK, 9-12 September, 2019.
- Zhang, Y.et al. 2019. Computer-assisted relief modelling: a comprehensive survey. Presented at: Eurographics Conference 2019 STARs, Genova, Italy, 6-10 May 2019.
2018
- Wu, J. and Ji, Z. 2018. Seeing the unseen: Locating objects from reflections. Presented at: 19th Towards Autonomous Robotic Systems (TAROS) Conference, Bristol, UK, 25- 27 July 2018.
- Liu, S.et al. 2018. Visual diagnosis of tree boosting methods. IEEE Transactions on Visualization and Computer Graphics 24(1), pp. 163-173. (10.1109/TVCG.2017.2744378)
2016
- Wu, J.et al. 2016. Improving shape from shading with interactive Tabu search. Journal of Computer Science and Technology 31(3), pp. 488-500. (10.1007/s11390-016-1639-1)
2014
- Wu, J.et al. 2014. Use of non-photorealistic rendering and photometric stereo in making bas-reliefs from photographs. Graphical Models 76(4), pp. 202-213. (10.1016/j.gmod.2014.02.002)
2013
- Wu, J.et al. 2013. Making bas-reliefs from photographs of human faces. Computer-Aided Design 45(3), pp. 671-682. (10.1016/j.cad.2012.11.002)
2012
- Zhang, L., Hancock, E. R. and Wu, J. 2012. Estimating surface characteristics and extracting features from polarisation. Presented at: Joint IAPR International Workshop, SSPR&SPR 2012, Hiroshima, Japan, November 7-9, 2012 Presented at Gimel'farb, G. et al. eds.Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2012, Hiroshima, Japan, November 7-9, 2012, Proceedings.. Lecture Notes in Computer Science Vol. 7626. Springer Berlin Heidelberg: Springer pp. 400-408., (10.1007/978-3-642-34166-3_44)
2011
- Wu, J., Smith, W. A. P. and Hancock, E. R. 2011. Gender discriminating models from facial surface normals. Pattern Recognition 44(12), pp. 2871-2886. (10.1016/j.patcog.2011.04.013)
- Kawulok, M., Wu, J. and Hancock, E. R. 2011. Supervised relevance maps for increasing the distinctiveness of facial images. Pattern Recognition 44(4), pp. 929-939. (10.1016/j.patcog.2010.10.010)
- Zhang, Z., Hancock, E. R. and Wu, J. 2011. An information theoretic approach to gender feature selection. Presented at: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain, 6-13 Nov 20112011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). Piscataway, N.J.: IEEE pp. 1425-1431., (10.1109/ICCVW.2011.6130418)
2010
- Wu, J., Smith, W. A. P. and Hancock, E. R. 2010. Facial gender classification using shape-from-shading. Image and Vision Computing 28(6), pp. 1039-1048. (10.1016/j.imavis.2009.09.003)
- Wu, J., Smith, W. A. P. and Hancock, E. R. 2010. Semi-supervised feature selection for gender classification. Presented at: 9th Asian Conference on Computer Vision, Xi’an, China, 23-27 September 2009 Presented at Zha, H., Taniguchi, R. and Maybank, S. eds.Computer Vision – ACCV 2009: 9th Asian Conference on Computer Vision, Xi’an, September 23-27, 2009, Revised Selected Papers, Part II, Vol. II. Lecture Notes in Computer Science Vol. 5995. Berlin Heidelberg: Springer pp. 23-33., (10.1007/978-3-642-12304-7_3)
2009
- Wu, J.et al. 2009. Extracting gender discriminating features from facial needle-maps. Presented at: 2009 IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, 7-10 Nov 20092009 IEEE International Conference on Image Processing: Proceedings. Piscataway, NJ: IEEE pp. 2449-2452., (10.1109/ICIP.2009.5414129)
2008
- Wu, J., Smith, W. A. P. and Hancock, E. R. 2008. Supervised principal geodesic analysis on facial surface normals for gender classification. Presented at: Structural, Syntactic, and Statistical Pattern Recognition Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, 4-6 December 2008 Presented at da Vitoria Lobo, N. et al. eds.Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings. Lecture Notes in Computer Science Vol. 5342. Berlin Heidelberg: Springer pp. 664-673., (10.1007/978-3-540-89689-0_70)
- Wu, J., Smith, W. A. P. and Hancock, E. 2008. Gender classification based on facial surface normals. Presented at: 19th International Conference on Pattern Recognition (ICPR 2008), Tampa, Florida, 8-11 December 200819th International Conference on Pattern Recognition (ICPR 2008). Piscataway, NJ: IEEE pp. 1-4., (10.1109/ICPR.2008.4761056)
- Wu, J., Smith, W. and Hancock, E. 2008. Gender classification based on facial surface normals. Presented at: International Conference on Pattern Recognition, Tampa, FL., 8-11 Dec 200819th International Conference on Pattern Recognition : (ICPR 2008) ; Tampa, Florida, USA 8-11 December 2008.. Piscataway, N.J.: IEEE pp. 1-4., (10.1109/ICPR.2008.4761056)
- Wu, J., Smith, W. A. P. and Hancock, E. R. 2008. Facial gender classification using shape from shading and weighted principal geodesic analysis. Presented at: 5th International Conference, ICIAR 2008, Póvoa de Varzim, Portugal, 25-27 June 2008 Presented at Campilho, A. and Kamel, M. eds.Image Analysis and Recognition: 5th International Conference, ICIAR 2008, Póvoa de Varzim, Portugal, June 25-27, 2008. Proceedings.. Lecture Notes in Computer Science Vol. 5112. Berlin Heidelberg: Springer pp. 925-934., (10.1007/978-3-540-69812-8_92)
- Wu, J., Smith, W. A. P. and Hancock, E. R. 2008. Weighted principal geodesic analysis for facial gender classification. Presented at: 12th Iberoamericann Congress on Pattern Recognition, CIARP 2007, Valparaiso, Chile, 13-16 November 2007 Presented at Rueda, L., Mery, D. and Kittler, J. eds.Progress in Pattern Recognition, Image Analysis and Applications: 12th Iberoamericann Congress on Pattern Recognition, CIARP 2007, Valparaiso, Chile, November 13-16, 2007. Proceedings.. Lecture Notes in Computer Science Vol. 4756. Berlin Heidelberg: Springer pp. 331-339., (10.1007/978-3-540-76725-1_35)
2007
- Wu, J., Smith, W. A. and Hancock, E. R. 2007. Gender classification using shape from shading. Presented at: BMVC 2007, Warwick, UK, 10-13 September 2007 Presented at Rajpoot, N. M. and Bhalerao, M. L. eds.Proceedings of the British Machine Conference. BMVA Press pp. 50.1-50.10., (10.5244/C.21.50)
- Dickens, M. P.et al. 2007. Face recognition using principal geodesic analysis and manifold learning. Presented at: Third Iberian Conference, IbPRIA 2007, Girona, Spain, 6-8 June 2007 Presented at Martí, J. ed.Pattern Recognition and Image Analysis: Third Iberian Conference, IbPRIA 2007, Girona, Spain, June 6-8, 2007, Proceedings, Part I, Vol. 4477. Lecture Notes in Computer Science Vol. 4477. Berlin Heidelberg: Springer pp. 426-434., (10.1007/978-3-540-72847-4_55)
- Wu, J., Smith, W. A. P. and Hancock, E. R. 2007. Learning mixture models for gender classification based on facial surface normals. Presented at: Third Iberian Conference, IbPRIA 2007, Girona, Spain, 6-8 June 2007 Presented at Marti, J. et al. eds.Pattern Recognition and Image Analysis: Third Iberian Conference, IbPRIA 2007, Girona, Spain, June 6-8, 2007, Proceedings, Part I, Vol. 4477. Lecture Notes in Computer Science Vol. 4477. Springer Berlin Heidelberg pp. 39-46., (10.1007/978-3-540-72847-4_7)
2006
- Wu, J., Smith, W. A. P. and Hancock, E. 2006. Gender classification using principal geodesic analysis and gaussian mixture models. Lecture Notes in Computer Science 4225, pp. 58-67. (10.1007/11892755_6)
Teaching
2016/17-2020/21, I am involved in teaching:
- CM1101 Computational Thinking
- CM1103 Problem Solving with Python
- CM3203 One Semester Individual Project
- CMT400 Dissertation
2015/16, I am involved in teaching:
- CM1103 Problem Solving with Python
- CMT202 Distributed and Cloud Computing
I have broad interests and research experience in computer vision. My main research interests lie in the area of 3D reconstruction, visual understanding, and their applications. To reconstruct 3D information from 2D is a low-level vision problem, and assists many mid- and high-level vision tasks, such as segmentation, tracking, recognition, etc. and also has applications in computer graphics. I am working / have worked on a number of projects in this regard, including:
- Reflection Aware Visual Simultaneous Localization and Mapping (RA-vSLAM), Main Supervisor, EPSRC DTP. Oct 2020 - Mar 2024
- Study of style transfer based and 3D assisted caricature generation techniques, PI, Travel fund to visit Nanjing University. June 2020 - June 2022.
- Learning-based and 3D assisted caricaturization of multi-modal portraits, PI, Travel fund to visit Nanjing University. Apr 2018 - Apr 2020.
- Pushing the boundary of vision-based 3D surface imaging, CoI, Renishaw and Cardiff University Strategic Fund. Apr 2018 - Mar 2019.
- Realistic SFS with user interaction, which developed an interactive framework to tackle intrinsic problems of SFS, and combined heuristic search to improve the intuitiveness of user interaction. It is an EPSRC project. July 2013 - July 2016.
- Generating bas-reliefs from face photographs, which developed a framework combining techniques of machine learning, SFS, photometric stereo, and non-photorealistic rendering etc. to tackle the problem of bas-relief generation from a single input face image. It is part of a RIVIC (The Welsh Research Institute of Visual Computing) scientific programme - Vision-based Geometric Modelling. Sep 2009 - June 2013.
- SFS in facial gender classification, which proposed a shape-from-shading (SFS) based representation that can implicitly encode both the 2D texture and the 3D shape information to improve facial gender classification using either information alone. Oct 2005 - Sep 2009.
Supervision
I am interested in supervising PhD students in the areas of:
- Visual SLAM
- Shape from shading
- 3D face modelling
- Visualization
