Visual Computing and Image Processing Lab
Oklahoma State University
Imaging, Processing, Inferencing and Learning

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Publications


Scene understanding

  • X. Chen and G. Fan, "Indoor Camera Pose Estimation from Room Layouts and Image Outer Corners", IEEE Transactions on Multimedia, to appear.
  • E. Roberts, G. Fan and X. Chen, "In-Lab Development of a Mobile Interface for Cognitive Assistive Technology to Support Instrumental Activities of Daily Living in Dementia Homecare", Journal of Aging and Environment, Published online: Dec. 20, 2021.
  • X. Chen and G. Fan, "Indoor Camera Pose Estimation from Room Layouts and Image Outer Corners", accepted by IEEE Transactions on Multimedia, to appear.
  • L. Guo and G. Fan, “Holistic Indoor Scene Understanding by Context-supported Instance Segmentation”, Multimedia Tools and Applications (Special Issue on Depth-Related Processing and Applications in Visual Systems), Vol. 81, 35751-35773, 2022.
  • M. Yazdanpour, G. Fan an W. Sheng, "ManhattanFusion: Online Dense Reconstruction of Indoor Scenes from Depth Sequences", IEEE Trans. Visualization and Computer Graphics, Vol. 28, pp. 2668-2681, July 2022.
  • L. Yu and G. Fan, "DrsNet: Dual-resolution Semantic Segmentation with Rare Class-Oriented Superpixel", Multimedia Tools and Applications, in press.
  • A. Siddiqua and G. Fan, "Semantics-enhanced supervised deep autoencoder for depth image-based 3D model retrieval", Pattern Recognition Letters, Vol. 125, July 2019, pp806-812.
  • M. Yazdanpour, G. Fan, W. Sheng, "Online Manhattan Keyframe-based Dense Reconstruction from Indoor Depth Sequences", IEEE Conference on Visual Communications and Image Processing (VCIP), Dec. 2019.
  • A. Siddiqua and G. Fan, "Asymmetric supervised deep autoencoder for depth image based 3D model retrieval", IEEE Conference on Visual Communications and Image Processing (VCIP), Dec. 2019.
  • A. Siddiqua and G. Fan, "Semantics-enhanced supervised deep autoencoder for depth image-based 3D model retrieval", Pattern Recognition Letters, Vol. 125, July 2019, pp 806-812.
  • Z. Peng, J. Wu, G. Fan, "CCDA: a concise corner detection algorithm", Machine Vision and Applications, Vol. 30, pp 1029-1040.
  • L. Guo, G. Fan and W. Sheng, "Creating 3D Bounding Boxe Hypotheses from Deep Network Score-Maps", IEEE International Conference on Image Processing (ICIP), Sept. 2019.
  • M. Yazdanpour, G. Fan and W. Sheng, "Online Reconstruction of Indoor Scenes with Local Manhattan Frame Growing", IEEE Workshop on Perception Beyond Visible Spectrum (PBVS), in conjunction with CVPR2019, June 16, 2019 (best paper award).
  • L. Guo, G. Fan and W. Sheng, "Dual Graphical Models for Relational Modeling of Indoor Object Categories", IEEE Workshop on Perception Beyond Visible Spectrum (PBVS), in conjunction with CVPR2019, June 16, 2019.
  • L.Yu and G. Fan, "Edge-Aware Integration Model for Semantic Labeling of Rare Classe", IEEE International Conference on Image Processing, Sept. 2017.
  • L. Yu and G. Fan, "Rare Class Oriented Scene Labeling Using CNN Incorporated Label Transfer", International Symposium on Visual Computing, Las Vegas, Dec. 12-14, 2016.

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Computational imaging

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 Tracking and recognition

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Segmentation and recognition

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  Human motion estimation

  • L. Zhou, N. Lannan, and G. Fan, "A Review of Depth-based Human Motion Capture Enhancement: Past, Present, and Future", IEEE Journal of Biomedical and Health Informatics, to appear.
  • L. Zhou, N. Lannan, and G. Fan, "Joint Optimization of Kinematics and Anthropometrics for Human Motion Denoising", IEEE Sensors Journal, Vol. 22, Issue 5, Page(s): 4386 - 4399, 2022.
  • N. Lannan, L. Zhou and G. Fan, "Human Motion Enhancement via Tobit Kalman Filter-Assisted Autoencoder", IEEE Access, Vol. 10, Page(s): 29233 - 29251, 2022.
  • N. Lannan, L. Zhou, G. Fan, J. Hausselle, "Human Motion Enhancement Using Nonlinear Kalman Filter Assisted Convolutional Autoencoders", IEEE International Conference on Bioinformatics and Bioengineering, Oct. 26-28, 2020.
  • L. Zhou, N. Lannan, G. Fan, J. Hausselle, "A Hybrid Approach to Human Motion Enhancement under Kinematic and Anthropometric Constraints", IEEE International Conference on Bioinformatics and Bioengineering, Oct. 26-28, 2020.
  • D. Liang, G. Fan, G. Lin, W. Chen, X. Pan, and H. Zhu, "Three-Stream Convolutional Neural Network with Multi-task and Ensemble Learning for 3D Action Recognition", IEEE Workshop on Perception Beyond Visible Spectrum (PBVS), in conjunction with CVPR2019, June 16, 2019.
  • N. Lannan and G. Fan, "Filter Guided Manifold Optimization in the Autoencoder Latent Space", IEEE Workshop on Perception Beyond Visible Spectrum (PBVS), in conjunction with CVPR2019, June 16, 2019.
  • S. Ge and G. Fan, "Topology-aware non-rigid point set registration via global–local topology preservation", Issue 4, Machine Vision and Applications, 2019.
  • X. Zhang, M. Ding and G. Fan, "Video-based Human Walking Estimation by Using Joint Gait and Pose Manifolds", IEEE Trans. Circuits and Systems for Video Technology, Vol. 27, Issue 7, 2017, pp1540 - 1554.
  • M. Ding and G. Fan, "Articulated and Generalized Gaussian Kernel Correlation for Human Pose Estimation", IEEE Trans. Image Processing, Vol. 25, No. 2, pages-776 - 789, Feb. 2016. 
  • S. Ge and G. Fan, "Articulated Non-Rigid Point Set Registration for Human Pose Estimation from 3D Sensors", Sensors (Physical Sensors), June 2015.
  • M. Ding and G. Fan, "Articulated Gaussian Kernel Correlation for Human Pose Estimation", in Proc. of the Workshop on Perception beyond Visible Spectrum (PBVS) in conjunction with CVPR2015, Boston, June 2015.
  • M. Ding and G. Fan, "Multi-Layer Joint Gait-Pose Manifolds for Human Gait Motion Modeling", IEEE Trans. Cybernetics, Vol. 45, Number 11, Nov. 2015.
  • S. Ge and G. Fan, "Non-rigid Articulated Point Set Registration with Local Structure Preservation", in Proc. of the Workshop on Perception beyond Visible Spectrum (PBVS) in conjunction with CVPR2015, Boston, June 2015.
  • S. Ge and G. Fan, "Sequential Non-rigid Point Registration for 3D Human Pose Tracking", in Proc. International Conference on Image Processing (ICIP2015), Quebec City, Canada, Sept., 2015.
  • M. Ding and G. Fan, "Multi-Layer Joint Gait-Pose Manifolds for Human Gait Motion Modeling", IEEE Trans. Cybernetics, Nov. 2014.
  • S. Ge and G. Fan, "Non-rigid Articulated Point Set Registration for Human Pose Estimation", in Proc. IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Beach, HI, Jan. 6-9, 2015.
  • M. Ding and G. Fan, "Generalized Sum of Gaussians for Real-Time Human Pose Tracking from a Single Depth Sensor", in Proc. IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Beach, HI, Jan. 6-9, 2015.
  • M. Ding and G. Fan, "Fast Human Pose Tracking with Single Depth Sensor using Sums of Gaussians Models", in Proc. International Symposium on Visual Computing, Las Vegas, Nevada, Dec. 8-10, 2014.
  • S. Ge, G. Fan, and M. Ding, "Non-rigid Point Set Registration with Global-Local Topology Preservation", in Proc. IEEE Workshop on Perception beyond Visible Spectrum (in conjunction with CVPR2014), June 23, 2014.
  • M. Ding and G. Fan, “Multi-Layer Joint Gait-Pose Manifold for Human Motion Modeling”, IEEE Automatic Face and Gesture Recognition (FG), April 22-26, 2013, Shanghai, China.
  • X. Zhang, G. Fan and L. Chou, "Two-layer Dual Gait Generative Models for Human Motion Estimation from a Single Camera", Image and Vision Computing, (Special issue on Machine Learning in Motion Analysis), December 2012.
  • M. Ding, G. Fan, X. Zhang, S. Ge, and L. Chou, "Structure-guided Manifold Learning for Video-based Motion Estimation", in Proc. IEEE International Conference on Image Processing (ICIP2012), Sept. 30-Oct. 3, 2012, Orlando, Florida.
  • G. Fan and X. Zhang, “Gaussian Process-based Manifold Learning for Human Motion Modeling”, Intelligent Data Analysis for Real-Life Applications: Theory and Practice, Editors: R. Magdalena, M. Martínez, J.M. Martínez, P. Escandell and J. Vila, IGI Global, to appear, 2011.
  • G. Fan, X. Zhang and M. Ding, “Gaussian Process for Human Motion Modeling: A Comparative Study”, in Proc. IEEE Workshop on Machine Learning for Signal Processing, Sept. 18-20, 2011, Beijing, China.
  • X. Zhang and G. Fan, "Joint Gait-Pose Manifold for Video-based Human Motion Estimation", in Proc. the 3rd International Workshop on Machine Learning for Vision-based Motion Analysis (MLVMA’11), in conjunction with CVPR2011, Colorado Spring, Colorado, June 25, 2011.
  • G. Fan and X. Zhang, "Video-based Human Motion Estimation by Part-whole Gait Manifold Learning", (book chapter), Machine Learning for Vision-based Motion Analysis, Editors: L. Wang, G. Zhao, L. Chen and M. Pietikaine, Springer, 2010 (to appear). 
  • X. Zhang, D. Biswas, and G. Fan, "A Software Pipeline for 3D Animation Generation using Mocap Data and Commercial Shape Models", in Proc. ACM Conference on Image and Video Retrieval, Xi'an, China, June 15-17, 2010.
  • X. Zhang and G. Fan, "Dual Gait Generative Models for Human Motion Estimation from a Single Camera", IEEE Transactions on Systems, Man, and Cybernetics: Part B,  Vol. 40, No. 4, pp1034-1049, Aug. 2010. 
  • X. Zhang, G. Fan, and L. Chou, “Two-layer Gait Generative Models for Estimating Unknown Human Gait Kinematics”, in Proc. the 2nd International Workshop on Machine Learning for Vision-based Motion Analysis (MLVMA’09), in conjunction with ICCV2009, Japan, Oct. 2009.
  • X. Zhang and G. Fan, "Dual Generative Models for Human Motion Estimation from an Uncalibrated Monocular Camera", in Proc. International Conference on Pattern Recognition (ICPR), Tampa, Florida, Dec. 2008.

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 Sports video mining

  • G. Fan and Y. Ding, “Probabilistic Graphical Models for Sports Video Mining”, Intelligent Data Analysis for Real-Life Applications: Theory and Practice, Editors: R. Magdalena, M. Martínez, J.M. Martínez, P. Escandell and J. Vila, IGI Global, 2011.
  • Y. Ding and G. Fan, “Finding the Game Flow from Sports Video”, in Proc. Joint ACM Workshop on Modeling and Representing Events (J-MRE'11), Nov. 30, 2011, Scottsdale, Arizona.
  • G. Fan and Y. Ding, "Event Detection in Sports Video based on Generative-Discriminative Models" (book chapter), Computer Vision for Multimedia Applications: Methods and Solutions, Editors: J. Wang, J. Chen and S. Jiang, IGI, 2010.  
  • Y. Ding and G. Fan, "Event Detection in Sports Video based on Generative-Discriminative Models", in Proc. the 1st ACM International Workshop on Events in Multimedia (EiMM09) in conjunction with the ACM Multimedia Conference, Oct. 2009, Beijing, China.
  • Y. Ding and G. Fan, "Sports Video Mining via Multi-channel Segmental Hidden Markov Models", IEEE Trans. on Multimedia, Vol. 11, No. 7, pp1301-1309, Nov. 2009.
  • G. Fan and Y. Ding, "Statistical Machine Learning Approaches for Sports Video Mining using Hidden Markov Models" (book chapter), Handbook of Research on Machine Learning Applications (ISBN10: 1605667668), IGI Global, 2009.
  • Y. Ding and G. Fan, "Multi-channel Segmental Hidden Markov Models for Sports Video Mining", in Proc. ACM Multimedia Conference, Oct. 27-Nov. 1 , 2008, Vancouver, Canada.
  • Y. Ding and G. Fan, "Segmental Hidden Markov Models for View-based Sport Video Analysis", in Proc. of International Workshop on Semantic Learning Applications in Multimedia (SLAM07), in conjunction with CVPR07, Minneapolis, MN, June 22, 2007.
  • Y. Ding and G. Fan, "Two-layer Generative Models for Video Mining", in Proc. of IEEE International Conference on Multimedia and Expo (ICME), Beijing, China, July 2007. 
  • Y. Ding and G. Fan, “Camera View Based American Football Video Analysis”, in IEEE Proc. International Symposium on Multimedia, San Diego, CA, Dec. 11-13.

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Video segmentation

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 Image segmentation

  • X. Song and G. Fan, "On Capturing Likelihood Disparity for Unsupervised Image Segmentation", in Proc. IEEE Statistical Signal Processing Workshop, St. Louis, MO, September 2003.
  • X. Song and G. Fan, "Unsupervised Bayesian Image Segmentation using Wavelet-domain Hidden Markov Models", in Proc. IEEE International Conference on Image Processing, Barcelona, Span, September 2003.
  • X. Song and G. Fan, "Unsupervised Image Segmentation using Wavelet-domain Hidden Markov Models", in Proc. SPIE Wavelet X, Volume 5207, San Diego, CA, August 2003.
  • L. Liu, Y. Dong, X. Song, and G. Fan, "A Entropy-based Segmentation Algorithm for Computer-Generated Document Images", in Proc. IEEE International Conference on Image Processing, Barcelona, Span, September 2003.
  • Y. Dong, L. Liu, X. Song, and G. Fan, "A New Simplified Quantization Rate-Distortion Model for Fast Document Image Segmentation", in Proc. of the 45th IEEE International Midwest Symposium on Circuits and Systems, Tulsa, OK, Aug. 2002.
  • X. Song and G. Fan, "A Study of Supervised, Semi-Supervised and Unsupervised Multiscale Bayesian Image Segmentation", in Proc. of the 45th IEEE International Midwest Symposium on Circuits and Systems, Tulsa, OK, Aug. 2002.
  • G. Fan and X. Song, "A Study of Contextual Modeling and Texture Characterization for Multiscale Bayesian Segmentation", in Proc. of the IEEE International Conference on Image Processing (ICIP2002), Rochester, NY, Sept. 2002.
  • G. Fan and X.-G. Xia, "On Context-Based Bayesian Image Segmentation: Joint Multi-context and Multiscale Approach and Wavelet-Domain Hidden Markov Models", in Proc. of the 35th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 4-7, 2001 (Invited paper).
  • G. Fan and X.-G. Xia, "A Joint Multi-context and Multiscale Approach to Bayesian Image Segmentation", IEEE Tran. on Geoscience and Remote Sensing, Vol 39, No. 12, pp2680 -2688, Dec. 2001.
  • G. Fan and X.-G. Xia, "Multiscale Texture Segmentation Using Hybrid Contextual Labeling Tree", in Proc. of the IEEE International Conference on Image Processing (ICIP2000), Vancouver, Canada, Sept. 2000.

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  Statistical image modeling

  • J. Ji, J. Wei, G. Fan, M. Bai, J. Huang, Q. Miao, "Image patch prior learning based on random neighbourhood resampling for image denoising", IET Image Processing, Vol. 14, Issue 5, pp838-844, 2019.
  • B. Xu, G. Fan, and Dan Yang, "Topic Modeling Based Image Clustering by Events in Social Media", Scientific Programming, Vol. 2016, 2016.
  • B. Xu and G. Fan, "Multimodal Topic Modeling-based Geo-annotation for Social Event Detection in Large Photo Collections", in Proc. International Conference on Image Processing (ICIP2015), Quebec City, Canada, Sept., 2015.
    G. Fan and X.-G. Xia, "Statistical Image Modeling and Processing Using Wavelet-Domain Hidden Markov Models", (book chapter) Nonlinear Signal and Image Processing: Theory, Methods, and Applications, K. E. Barner and G. R. Arce (Editors), CRC Press, 2003.
  • G. Fan and X.-G. Xia, "Wavelet-based Texture Analysis and Synthesis Using Hidden Markov Models", IEEE Trans. Circuits and Systems, Part I, Vol. 50, No. 1, pp106-120, Jan. 2003 (corrections).
  • G. Fan, "Wavelet-Domain Statistical Image Modeling and Processing", Ph.D. dissertation, University of Delaware, Summer 2001.
  • G. Fan and X.-G. Xia, "Image Denoising Using Local Contextual Hidden Markov Model in the Wavelet Domain", IEEE Signal Processing Letter, Vol. 8, No. 5, May 2001, pp125-128.
  • G. Fan and X.-G. Xia, "Improved Hidden Markov Models in the Wavelet-Domain", IEEE Trans. on Signal Processing, Vol. 49, No. 1 Jan. 2001, pp115-120.
  • G. Fan and X.-G. Xia, "Wavelet-Based Statistical Image Processing Using Hidden Markov Tree Model", in Proc. of the 2000 Conference on Information Science and Systems (CISS2000), Princeton, NJ, March, 2000, ppTA5-31-TA-5-36.
  • G. Fan and X.-G. Xia, "Wavelet-Based Image Denoising Using Hidden Markov Models", in Proc. of the IEEE International Conference on Image Processing (ICIP2000), Vancouver, Canada, Sept. 2000.
  • G. Fan and X.-G. Xia, "Texture Analysis and Synthesis Using Wavelet-Domain Hidden Markov Models", in Proc. of the 5th IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, Baltimore, MD, June 2001.
  • G. Fan and X.-G. Xia, "Maximum Likelihood Texture Analysis and Classification Using Wavelet-Domain Hidden Markov Models", in Proc. of the 34th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 29-Nov. 1, 2000.

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  Image processing

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  Retinal image processing

  • T. Chanwimaluang, G. Fan, IEEE, G. G. Yen, and S. R. Fransen, "3-D Retinal Curvature Estimation", IEEE Trans. on Information Technology in Biomedicine, Vol. 13, No. 6, pp997-1005, Nov. 2009.  

  • T. Chanwimaluang and G. Fan, "Constrained Optimization for Retinal Curvature Estimation Using an Affine Camera" in the Proc. of International Workshop on Beyond Multiview Geometry: Robust Estimation and Organization of Shapes from Multiple Cues (BMG07), in conjunction with CVPR2007, Minneapolis, Minnesota, June 22, 2007.
  • T. Chanwimaluang and G. Fan, "Affine Camera for 3D Retinal Surface Reconstruction" in the Proc. of International Symposium on Visual Computing, Lake Tahoe, Nevada, Nov. 6-8, 2006.
  • Xin Zhang and G. Fan, "Retinal Spot Lesion Detection Using Adaptive Multiscale Morphological Processing" in the Proc. of International Symposium on Visual Computing, Lake Tahoe, Nevada, Nov. 6-8, 2006.
  • T. Chanwimaluang and G. Fan, "Retinal Image Registration for NIH's ETDRS", in Lecture Notes in Computer Science, Vol. 3804, Springer,  also the Proc. of International Symposium on Visual Computing, Lake Tahoe, Nevada, Dec. 5-7, 2005.
  • T. Chanwimaluang, G. Fan, and S. Fransen, "Hybrid Retinal Image Registration", IEEE Trans. on Information Technology in Biomedicine, Vol. 10, No. 1, pp129-142, Jan. 2006. (demos)
  • A. Awawedeh and G. Fan, "Pseudo Cepstrum for Assessing Stereo Quality of Retinal Images", in Proc. of the 37th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2003.
  • T. Chanwimaluang and G. Fan, "An Efficient Algorithm for Extraction of Anatomical Structures in Retinal Images", in Proc. IEEE International Conference on Image Processing, Barcelona, Span, September 2003.
  • T. Chanwimaluang and G. Fan, "An Efficient Blood Vessel Detection Algorithm for Retinal Images using Local Entropy Thresholding", in Proc. of the 2003 IEEE International Symposium on Circuits and Systems, Bangkok, Thailand, May 25-28, 2003.

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  Remote sensing analysis

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(Acknowledgements: The template is from Interspire Free Templates, and free pictures are from 3DLuVr.com.)