Paul Fieguth, University of Waterloo
Associate Dean, Policies & Resources, Engineering
Interim Associate Dean, Outreach
Professor
Waterloo, Ontario
pfieguth@uwaterloo.ca
Office:
(519) 888-4567 ext. 33599
Expert In
Bio/Research
Paul Fieguth is the Associate Dean in the Faculty of Engineering, and Professor in the Department of Systems Design Engineering, where he is a co-director of the Vision and Imaging Processing Lab (https://uwaterloo.ca/vision-image-processing-lab/) at the University of Waterloo.
His main ...
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His main ...
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Bio/Research
Paul Fieguth is the Associate Dean in the Faculty of Engineering, and Professor in the Department of Systems Design Engineering, where he is a co-director of the Vision and Imaging Processing Lab (https://uwaterloo.ca/vision-image-processing-lab/) at the University of Waterloo.
His main areas of research lie in multiscale statistical modelling and computer vision. In particular, Professor Fieguth focuses on 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, which are tremendously valuable because they allow us to infer subtle results from the data, and because they allow us to test whether a given mathematical model makes sense or not, a crucial step in advancing our understanding.
Professor Fieguth has written three textbooks, aligned with his particular expertise:
1. Statistical Image Processing and Multidimensional Modelling, 2010
2. An Introduction to Complex Systems, 2021
3. An Introduction to Pattern Recognition and Machine Learning, 2022
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His main areas of research lie in multiscale statistical modelling and computer vision. In particular, Professor Fieguth focuses on 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, which are tremendously valuable because they allow us to infer subtle results from the data, and because they allow us to test whether a given mathematical model makes sense or not, a crucial step in advancing our understanding.
Professor Fieguth has written three textbooks, aligned with his particular expertise:
1. Statistical Image Processing and Multidimensional Modelling, 2010
2. An Introduction to Complex Systems, 2021
3. An Introduction to Pattern Recognition and Machine Learning, 2022
Click to Shrink <<