Paul Fieguth

Professor and Department Chair

Contact InformationPaul Fieguth

Phone: 519-888-4970,519-888-4567 x43599
Location: E5 6119,E7 6338


Biography Summary

Paul Fieguth is a Professor, as well as Chair of the Systems Design Engineering department. He is also the Director of the Vision and Imaging Processing Lab at the University of Waterloo.

His main areas of research lie in multiscale statistical modelling and remote sensing. Dr. Fieguth is concentrating on the theory development and understanding of scale recursive estimation algorithms for multi-resolution stochastic processes. Such algorithms use a statistically meaningful strategy to break large estimation problems into smaller pieces, leading to vast improvements in efficiency.

While these algorithms are capable of extremely fast assimilation of data, it is difficult to determine the appropriate multiscale model for a given application of interest. The model assumes a time-invariant model underlying the measurements, and the estimates produced by the model often possess certain multiscale artifacts. Dr. Fieguth aims to address these limitations in the context of a specific data assimilation problem involving a time-varying system: the world's oceans’ surface shape. This approach has allowed him and his research team to contribute to an oceanography issue, while simultaneously providing access to an extensive data set on which to test their algorithms.

Dr. Fieguth’s work in remote sensing is conducted in collaboration with researchers in oceanography who supply physical models, observational data, and the scientific expertise to ensure that meaningful answers are being computed to meaningful problems. Past and ongoing remote sensing efforts include ocean altimetry (production of ocean-surface maps), ocean hydrography (estimation of volumetric temperature fields), acoustic tomography (estimation of large-scale oceanographic features from measurements of sound propagation), and geodesy (determining high-resolution features of the earth's gravitational field from ocean-surface features).

Research Interests

  • Signal & Image Processing
  • Societal & Environmental Systems
  • Statistical Modeling
  • Multiscale Methods
  • Statistical Estimation
  • Remote Sensing
  • Computer Vision
  • Society
  • Pattern Analysis
  • Machine Intelligence & Robotics
  • Random Processes In The Environment


  • 1995, Doctorate, PhD, Massachusetts Institute of Technology
  • 1993, Other, SM, Massachusetts Institute of Technology
  • 1991, Bachelor's, BASc, University of Waterloo


  • SYDE 332 - Introduction to Complex Systems
    • Taught in 2014, 2016, 2017, 2018
  • SYDE 770 - Selected Topics in Communication and Information Systems
    • Taught in 2015
  • SYDE 730 - Selected Topics in Societal-Environmental Systems
    • Taught in 2016
  • SYDE 672 - Statistical Image Processing
    • Taught in 2018
* Only courses taught in the past 5 years are displayed.

Selected/Recent Publications

  • Liu, Li and Lao, Songyang and Fieguth, Paul W and Guo, Yulan and Wang, Xiaogang and Pietikäinen, Matti, Median robust extended local binary pattern for texture classification, IEEE Transactions on Image Processing, 25(3), 2016, 1368 - 1381
  • Koch, Christian and Doycheva, Kristina and Kasireddy, Varun and Akinci, Burcu and Fieguth, Paul, Corrigendum to “A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure”[Advanced Engineering Informatics 29 (2)(2015) 196--210], Advanced Engineering Informatics, 30(2), 2016, 208 - 210
  • Liu, Jiange and Scott, K Andrea and Gawish, Ahmed and Fieguth, Paul, Automatic Detection of the Ice Edge in SAR Imagery Using Curvelet Transform and Active Contour, Remote Sensing, 8(6), 2016
  • Shafiee, MJ and Siva, P and Scharfenberger, C and Fieguth, P and Wong, A, NeRD: a Neural Response Divergence Approach to Visual Salience Detection, arXiv preprint arXiv:1602.01728, 2016
  • Liu, Li and Fieguth, Paul and Zhao, Guoying and Pietikäinen, Matti and Hu, Dewen, Extended local binary patterns for face recognition, Information Sciences, 358, 2016, 56 - 72