Paul Fieguth

Paul Fieguth is the Vice-Dean of the Faculty of Engineering, Professor, and Department Chair of the Department of Systems Design Engineering. He is also a co-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. In particular, Professor Fieguth concentrates on the theory development and understanding of hierarchical / 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.

Certainly a very pressing challenge is the amount of image data being collected -- satellite pictures, microscopic images, Google Streetview. There are many image processing algorithms available for regular images, such as portraits from digital cameras, however for scientific imagery, such as satellite images of a forest, microscopic pictures of a cracks in concrete, or medical images of the body from an MRI, more specialized techniques of image processing are required.

Professor Fieguth's interests are to formulate mathematical models, such as the temperature of the earth's atmosphere or the expected topology of the brain, and to combine such models with measured data. Such mathematical operations are tremendously valuable for two reasons:

First, because they allow us to infer subtle results from the data, and second, because they allow us to test whether a given mathematical model makes sense or not, a crucial step in advancing our understanding. The problem, however, is that it is very difficult to solve these equations in a computer for large two- or three-dimensional problems. His research seeks to develop efficient alternatives to modeling and algorithms, currently focusing on two specific strategies:

1. Look at the problem over a variety of scales, coupling a coarse-scale model looking at large objects with a fine-scale model looking at details and textures. Such an idea sounds very simple or intuitive, since the human visual system very much works this way, but is extremely challenging, in practice, because most mathematical models do not allow themselves to be split up.

2. Allow the model to connect not just neighbouring pixels, but also pixels further apart.

This goal seems obvious, however virtually all spatial statistical models have focused on highly local interactions. Professor Fieguth's research explores models in which every pixel is connected globally, or possibly to a randomly scattered set of pixels where the statistics of the set are a function of the underlying image.

• Signal & Image Processing
• Societal & Environmental Systems
• Statistical Modeling
• Multiscale Methods
• Remote Sensing
• Computer Vision
• Pattern Analysis
• Machine Intelligence & Robotics

• 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

• 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