Education

• Ph.D. University of Waterloo, 2014

Memberships

• Senior Member IEEE, The Institute of Electrical and Electronics Engineers, 2017

• Full Membership Sigma Xi, The Scientific Research Honor Society, 2022

• Member iBEST, Institute for Biomedical Engineering, Science and Technology, 2021

Work Experience

• Associate Professor, Toronto Metropolitan University, 2020 - present

• Associate/Assistant Professor, University of Kansas, 2014 - 2020

Research Interests

• Computational Vision: Structure from motion, stereo vision, visual measurement

• Image Analysis: Image classification, object detection, image matching, image translation

• Autonomous Systems: Robot navigation, multisensor data fusion, simultaneous localization & mapping

• Artificial Intelligence: Deep learning, machine learning, visual attention, semantic segmentation, scene classification

Courses

• CP 8315. Special Doctoral Topics ‐ AI & Robotics (Toronto Metropolitan University)

• CP 8207. Special Topics: Core Computer Science (Toronto Metropolitan University)

• CP 8307. Introduction to Computer Vision (Toronto Metropolitan University)

• CPS 843. Introduction to Computer Vision (Toronto Metropolitan University)

• CPS 109. Computer Science I (Toronto Metropolitan University)

• CPS 621. Introduction to Multimedia Systems (Toronto Metropolitan University)

• EECS 740. Digital Image Processing (University of Kansas)

• EECS 741. Computer Vision (University of Kansas)

• EECS 444. Control Systems (University of Kansas)

Books

• Wang, G. & Wu, J. Guide to Three Dimensional Structure and Motion Factorization, Springer-Verlag, ISBN: 978-0-85729-045-8, 2011. Buy it from Springer and Amazon.

• Wang, G. (ed.). (2016). Recent Advances in Robotic Systems. InTech. ISBN: 978-953-51-2570-9, 2016. Buy it from Amazon.

Selected Publications

• Li, K., Zhang, Z., Zhong, C., & Wang, G. (2022). Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. pdf. source code.

• Xu, W., Long, C., Wang, R., & Wang, G. (2021). DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer. IEEE/CVF International Conference on Computer Vision (ICCV), oral (acceptance rate: 3%). pdf. code.

• Xu, W., & Wang, G. (2021). A Domain Gap Aware Generative Adversarial Network for Multi-domain Image Translation. IEEE Trans. on Image Processing, 2021. (impact factor: 10.856)

• Xia, S., Xu, S., Wang, R., Li, J., & Wang, G. (2021). Building instance mapping from ALS point clouds aided by polygonal maps. IEEE Trans. on Geoscience and Remote Sensing, https://doi.org/10.1109/TGRS.2021.3087159. 2021. (impact factor: 5.855)

• Ma, W., Tu, X., Luo, B., & Wang, G. (2021). Semantic clustering based deduction learning for image recognition and classification. Pattern Recognition, vol.124, pdf. code. (impact factor: 7.74)

• Cen, F., Zhao, X., Li, W., & Wang, G. (2021). Deep feature augmentation for occluded image classification. Pattern Recognition, https://doi.org/10.1016/j.patcog.2020.107737. 2021. (impact factor: 7.74)

• Sajid, U., Sajid, H., Wang, H., & Wang, G. (2020). ZoomCount: A zooming mechanism for crowd counting in static images . IEEE Transactions on Circuits and Systems for Video Technology. DOI: 10.1109/TCSVT.2020.2978717. 2020.

• Ma, W., Wu, Y, Cen, F., & Wang, G. (2020). MDFN: Multi-scale deep feature learning network for object detection.Pattern Recognition. vol.100. 2020.

• Cen F., & Wang, G. (2019). Boosting occluded image classification via subspace decomposition based estimation of deep features. IEEE Transactions on Cybernetics, DOI:  10.1109/TCYB.2019.2931067. 2019.

• Sui, Y., Zhang Z., Wang, G., Tang, Y., & Zhang, L., (2019). Exploiting the anisotropy of correlation filter learning for visual tracking. International Journal of Computer Vision (IJCV), doi.org/10.1007/s11263-019-01156-6, 2019.

• Sui, Y., Wang, G., & Zhang, L., (2019). Joint correlation filtering for visual tracking. IEEE Transactions on Circuits and Systems for Video Technology, DOI: 10.1109/TCSVT.2018.2888573. 2019.

• Sui, Y., Wang, G., & Zhang, L., (2019). Sparse subspace clustering via low-rank structure propagation. Pattern Recognition. vol. 95, pp. 261-271, 2019.

• Xu, W., Keshmiri, S., & Wang, G. (2019). Adversarially approximated autoencoder for image generation and manipulation. IEEE Transactions on Multimedia. DOI:  10.1109/TMM.2019.2898777. 2019

• Xu, W., Keshmiri, S., & Wang, G., (2019). Toward learning a unified many-to-many mapping for diverse image translation. Pattern Recognition. vol. 93, pp. 570-580, 2019.

• Sui, Y., Tang, Y., Zhang, L.,Wang, G., (2018). Visual tracking via subspace learning: A discriminative approach. International Journal of Computer Vision (IJCV), vol. 126 (5), pp.515-536, 2018.

• Huo, J., Wu, J., Cao, J., & Wang, G. (2018). Supervoxel based method for multi-atlas segmentation of brain MR images. NeuroImage, vol.175, pp.201-214 2018.

• He, L., Wang, G., & Hu, Z. (2018). Learning depth from single images with deep neural network embedding focal length,.IEEE Transactions on Image Processing, vol.27(9), pp. 4676 - 4689, 2018

• Sui, Y., Wang, G., Zhang, L., &Yang, M.X. (2018). Exploiting spatial-temporal locality of tracking via structured dictionary learning. IEEE Transactions on Image Processing, vol.27(3), pp.1282-1296, 2018.

• Bharati, S., Wu, Y., Sui, Y., Padgett, C., & Wang, G. (2018). Real-time obstacle detection and tracking for sense-and-avoid mechanism in UAVs. IEEE Transactions on Intelligent Vehicles, DOI: 10.1109/TIV.2018.2804166, 2018.

• Zhang,, Z., Wu, Y., & Wang, G. (2018). BPGrad: Towards global optimality in deep learning via branch and pruning. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

• Sui, Y., Wang, G., Zhang, L.(2017). Correlation filter learning toward peak strength for visual tracking. IEEE Transactions on Cybernetics, vol. 48 (4) pp.1290-1303. 2018.

• Wu, F., Zhang, M., Wang, G., & Hu, Z. (2016). Triangulation and metric of lines based on geometric error. Computer Vision and Image Understanding, vol.145, pp.111-127, 2016.

• Sui, Y., Wang, G., Tang, Y., Zhang, L. (2016). Tracking completion, European Conference on Computer Vision (ECCV), 2016.

• Sui, Y., Zhang, Z., Wang, G., Tang, Y., Zhang, L. (2016). Real-time visual tracking: promoting the robustness of correlation filter learning, European Conference on Computer Vision (ECCV), 2016.

• Wang, G., Zelek, J., & Wu, J. (2012). Structure and motion recovery based on spatial-and-temporal-weighted factorization. IEEE Trans. on Circuits and Systems for Video Technology (T-CSVT), 22(11), 1590-1603, 2012.

• Zhang, W., Wu, J., & Wang, G. (2012). Tracking and pairing vehicle headlight in night scenes. IEEE Trans. on Intelligent Transportation Systems, 13(1), 140-153, 2012.

• Wang, G., & Wu, J. (2010). Quasi-perspective projection model: Theory and application to structure and motion factorization from uncalibrated image sequences. International Journal of Computer Vision (IJCV), 87(3), 213-234, 2010.

• Wang, G., & Wu, J. (2010). The quasi-perspective model: Geometric properties and 3D reconstruction. Pattern Recognition, 43, (5), 1932-1942, 2010.

• Zhang, W., Wu, J., & Wang, G. (2010). An adaptive computational model for salient object detection. IEEE Trans. on Multimedia, 12 (4), 300-316, 2010.

• Wang, G., & Wu, J. (2009). Perspective 3D Euclidean reconstruction with varying camera parameters. IEEE Trans. on Circuits and Systems for Video Technology, 19 (12), 1793-1803, 2009.

• Wang, G., & Wu, J. (2008). Stratification approach for 3D Euclidean reconstruction of nonrigid objects from uncalibrated image sequences. IEEE Trans. on Systems, Man, and Cybernetics: Part B, 38 (1), 90-101, 2008.

• Wang, G., & Wu, J. (2008). Quasi-perspective projection with applications to 3D factorization from uncalibrated image sequences. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2008.

A full list can be found at Publications.

Contact Us

Computer Vision and Intelligence Systems Laboratory
George Vari Engineering and Computing Centre
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Toronto, Ontario
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