M-Javad Shafiee

Research Assistant Professor
Vision & Image Processing Research Group
Department of Systems Design Engineering
University of Waterloo, Canada
Email: mjshafiee [at] uwaterloo.ca
Phone: 519-888-4567 EXT. 35342

Learn More About Me

A Brief Bio.

Mohammad Javad Shafiee, is currently Research Assistant Professor in the Department of Systems Design Engineering at University of Waterloo. He received the B.Sc. and M.Sc. degrees in Computer Science and Artificial Intelligence from Shiraz University, Shiraz, Iran, in 2008 and 2011 respectively; and the Ph.D. degree in systems design engineering from the University of Waterloo, Canada in 2017. His main research focus is on statistical learning and graphical models with random fields and deep learning approaches. His research interests include Computer Vision, Machine Learning and Biomedical Image Processing and his main focus is on Graphical Models specially Conditional Random Fields and Markov Random Fields. His PhD thesis topic is to introducing an efficient Bayesian inference with Stochastic Cliques for fully connected conditional random Fields.

See My Research & Projects

Research & Projects

RCRF

Randomly Formed Conditional Random Fields.

NeRD

Neural response divergence for saliency detection.

NeRM

Neural response mixture models for background modeling and motion detection.

Pulications

Journals (12)

  1. M.J. Shafiee, P. Siva and A. Wong, "StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity", IEEE ACCESS, Accepted.
  2. L. Xu, M.J. Shafiee, A. Wong and D. Clausi, "Fully Connected Continuous Conditional Random Field With Stochastic Cliques for Dark-Spot Detection In SAR Imagery", IEEE Journal of Selected Topics on Applied Earth Observation and Remote Sensing, 2016.
  3. A. G. Chung, F. Khalvati, M.J. Shafiee, M. A. Haider, and A. Wong, "Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework", IEEE Access Journal, 2015.
  4. S.A. Haider, A. Cameron, P. Siva , D. Lui, M.J. Shafiee, A. Boroomand, A. Wong and N. Haider, "Temporal noise reduction of fluorescence microscopy image sequences using a stochastically-connected random field model", Nature Scientific Reports, 2016.
  5. I. Ben Daya, A. Chen, M.J. Shafiee, A. Wong and J. Yeow, "Compensated Row-Column Ultrasound Imaging System Using Fisher Tippett Multilayered Conditional Random Field Model", PLoS ONE, 2015.
  6. M.J. Shafiee, Z. Azimifar and A. Wong, "A Deep-structured Conditional Random Field Model for Object Silhouette Tracking", PLoS ONE, 2015.
  7. E. Ahmadi, Z. Azimifar, M. Shams, M. Famouri and M.J. Shafiee, "Document image binarization using a discriminative structural classifier", Pattern Recognition Letter, 2015..
  8. A. Wong, M.J. Shafiee, P. Siva and XY. Wang, "A Deep-Structured Fully-Connected Random Field Model For Structured Inference", IEEE Access Journal, 2015.
  9. M.J. Shafiee, S. Haider, A. Wong, D. Lui, A. Cameron, A. Modhafar, P. Fieguth and M. Haider, "Apparent Ultra-High b-value Diffusion-Weighted Image Reconstruction via Hidden Conditional Random Fields", IEEE Transaction on Medical Imaging, 2014 .
  10. M.J. Shafiee, Z. Azimifar and P. Fieguth, "How Conditional Random Fields Learn Dynamics: An Example-Based Study'', Computer Communication \& Collaboration, 2013 .
  11. A. Wong, M.J. Shafiee and Z. Azimifar, "Statistical Conditional Sampling for Variable-Resolution Video Compression'', PLoS ONE, 2012.
  12. M. H. Setayesh, M. Kazemnejhad and M.J. Shafiee, "Genetic Algorithms in Determining Optimal Capital Structure of Firms Accepted in Tehran Stock Exchange", The Iranian Accounting and Auditing Review, 2009 [In Persian] .

Conferences (27)

  1. M.J. Shafiee , P. Sive, P. Fieguth and A. Wong, "Embedded Motion Detection via Neural Response Mixture Background Modeling", CVPR Workshop, 2016.
  2. P. Sive, M.J. Shafiee , M. Jamieson and A. Wong, "Real-time, Embedded Scene Invariant Crowd Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)", CVPR Workshop, 2016.
  3. M.J. Shafiee , P. Sive, P. Fieguth and A. Wong, "Efficient Deep Feature Learning and Extraction via StochasticNets", CVPR Workshop, 2016.
  4. A. Boroomand, E. Li, M.J. Shafiee , M. Haider, F. Khalvati and A. Wong, "Bayesian-based Compensated Magnetic Resonance Imaging", EMBC, 2016
  5. A. Karimi, M.J. Shafiee , C. Scharfenberger, I. BenDaya, S. Haider, N. Talukdar, D. Clausi and A. Wong, "Spatio-Temporal Saliency Detection Using Abstracted Fully-Connected Graphical Models", ICIP, 2016.
  6. A. G. Chung, M.J. Shafiee and A. Wong, "Random Feature MAPS Via A Layered Random Projection (LARP) Framework For Object Classification", ICIP, 2016.
  7. A.G. Chung, M.J. Shafiee and A. Wong, "Image Restoration via Deep-Structured Stochastically Fully-Connected Conditional Random Fields (DSFCRFs) for Very Low-Light Conditions", CRV, 2016
  8. M.J. Shafiee, A.G. Chung, D. Kumar, A. Wong, F. Khalvati and M.A. Haider, "Discovery Radiomics for Cancer Detection", NIPS Health-care Workshop, 2015.
  9. M.J. Shafiee, A.G.Chung, A. Wong and P. Fieguth, "Improved Fine Structure Modeling via Guided Stochastic Clique Formation in Fully Connected Conditional Random Fields ", IEEE International Conference on Image Processing, 2015, Accepted.
  10. E. Li, M.J. Shafiee, F. Kazemzadeha and A. Wong, "Sparse Reconstruction of Compressed Sensing Multi-spectral Data using Cross-Spectral Multi-layered Conditional Random Field Model'', SPIE Optical Engineering+ Applications, 2015.
  11. J. Deglint, F. Kazemzadeh, M.J. Shafiee, E. Li, I. Khodadad, S. Saini, A. Wong and D. Clausi, "Virtual Spectral Multiplexing for Applications in In-situ Imaging Microscopy of Transient Phenomena'', SPIE Optical Engineering+ Applications, 2015.
  12. F.Y. Li, M.J. Shafiee, A. Chung, B. Chwyl, F. Kazemzadeh, A. Wong and J. Zelek, "High Dynamic Range Map Estimation via Fully Connected Random Fields with Stochastic Cliques ", IEEE International Conference on Image Processing, 2015, Accepted.
  13. P. Siva, M.J. Shafiee, F. Y. Li and A. Wong, "PIRM: Fast Background Subtraction Under Sudden, Local Illumination Changes Via Probabilistic Illumination Range Modeling ", IEEE International Conference on Image Processing, 2015, Accepted.
  14. L. Xu, M.J. Shafiee, A. Wong, F. Li, L. Wang and D. Clausi, "Oil Spill Candidate Detection from SAR Imagery Using a Thresholding-Guided Stochastic Fully-Connected Conditional Random Field Model ", Computer Vision \& Pattern Recognition (CVPR) workshop, 2015.
  15. F.Y. Li, E. Li, M.J. Shafiee, A. Wong and J. Zelek, "Dense Depth Map Reconstruction from Sparse Measurements Using a Multilayer Conditional Random Field Model", Conference on Computer and Robot Vision, 2015.
  16. S. A. Haider, M.J. Shafiee, A. Chung, F. Khalvati, A. Oikonomou, A. Wong and M. Haider, " Single-click, Semi-Automatic Lung Nodule Contouring Using Hierarchical Conditional Random Fields'',International Symposium on Biomedical Imaging ISBI, 2015.
  17. A. Boroomand, M.J. Shafiee, F. Khalvati, A. Wong and M. Haider, "Noise-compensated Bias Correction of MRI via a Stochastically Fully-Connected Conditional Random Field Model'', Proc. Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), 2015.
  18. E. Li, M.J. Shafiee, A. Chung, F. Khalvati , A. Wong and M. Haider, "Enhanced Reconstruction of Compressive Sensing MRI via Cross-Domain Stochastically Fully-Connected Random Field Model'', Proc. Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), 2015.
  19. A. Boroomand, M.J. Shafiee , A. Wong and K. Bizheva, "Lateral Resolution Enhancement via Imbricated Optical Coherence Tomography in a Maximum-A-Posterior Reconstruction Framework'', the proceedings of SPIE Photonics West, 2015.
  20. M.J. Shafiee, A. Wong, P. Siva and P. Fieguth, "Efficient Bayesian Inference Using Fully Connected Conditional Random Fields With Stochastic Cliques", IEEE International Conference on Image Processing, 2014.
  21. F. Kazemzadeh, M.J. Shafiee, A. Wong and D. Clausi, "Reconstruction of Compressive Multispectral Sensing Data Using a Multilayered Conditional Random Field Approach", \textit {SPIE Optical Engineering+ Applications, 2014.
  22. A. Cameron, A. Modhafar, F. Khalvati, D. Lui, M.J. Shafiee, A. Wong and M. Haider, "Multiparametric MRI Prostate Cancer Analysis via a Hybrid Morphological-Textural Model", Engineering in Medicine and Biology Society (EMBC), 2014.
  23. A. Nazemi, M.J. Shafiee and Z. Azimifar, "On Road Vehicle Make and Model Recognition via Sparse Feature Coding", Iranian Conference on Machine Vision and Image Processing (MVIP) , 2013.
  24. M.J. Shafiee, Z. Azimifar and A. Wong, "A Novel Hierarchical Model-Based Frame Rate Up-Conversion via Spatio-temporal Conditional Random Fields'', The International Symposium of Multimedia, 2011, IEEE.
  25. M.J. Shafiee, Z. Azimifar and P. Fieguth, "Model-Based Tracking: Temporal Conditional Random Field'', The International Conference on Image Processing, 2010, IEEE.
  26. M.J. Shafiee, Z. Azimifar and P. Fieguth, "Temporal Conditional Random Fields: A Conditional State Space Predictor for Visual Tracking '', The Conference on Machine Vision and Image Processing, IEEE, 2010 .
  27. M.J. Shafiee, H. Rahmatkhah and Z. Azimifar, "License Plate Recognition in Complex Scene Based on the Neural Networks'', The 10th Iranian Student Conference on Electrical and Computer Engineering, [CD-ROM], Isfahan, Iran, 2007 [In Persian].

Under Consideration (4)

  1. M.J. Shafiee, A. Wong and P. Fieguth "Forming A Random Field via Stochastic Cliques: From Random Graphs to Fully Connected Random Fields ", Arxiv 2015
  2. M.J. Shafiee, A. Wong and P. Fieguth, "Multi-layered Stochastically Fully-Connected Conditional Random Fields For Image Segmentation", IEEE Transaction on Image Processing, Submitted.
  3. A. Boroomand, B. Tan, M.J. Shafiee, K. Bizheva and A. Wong, "Depth Compensated Spectral Domain Optical Coherence Tomography via Digital Compensation",Optice Letter , under revision
  4. C. Scharfenberger, M.J. Shafiee, A. Wong, D. Clausi and P. Fieguth, "FC2G -- Saliency Detection in Images using Fully Connected Compact Graphical Models ",Computer Vision and Image Understanding .

Awards

  • Conference Awards:

    • Magna Cum Laude Award, Imaging network Ontario symposium (IMNO), Canada, 2016.
    • Conference Travel Grant, Neural Information Processing Systems (NIPS) Workshop on Machine Learning in Healthcare, 2015.
    • Conference Travel Grant, International Conference on Image processing (ICIP), 2010.
    • Best Paper Award, Iranian Student Conference on Electrical and Computer Engineering, 2007.
  • Scholarships:

    • University of Waterloo Graduate Scholarship, University of Waterloo, Canada, Sept-Aug, 2013-2017
    • International Doctoral Student Award, University of Waterloo, Canada, Sept-Aug, 2013-2017.

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Datasets:

  • MSRA-FS: The images with fine structures on boundaries for binary image segmentation.
  • Interactive Image Segmentation: The seeds data for foreground and background interactive image segmentation.