Computer vision Image analysis Medical image processing Deep learning
I joined Computing Science, University of Alberta as an Assistant Professor in 2006. I was promoted to Associate Professor in 2013.
Computer vision and deep learning; image and video analysis, medical image processing
Medical image and video analysis and general computer vision problems including segmentation, registration, object detection and classification.
An introduction to basic digital image processing theory, and the tools that make advanced image manipulation possible for ordinary users. Image processing is important in many applications: editing and processing photographs, special effects for movies, drawing animated characters starting with photographs, analyzing and enhancing images captured by the Mars Rover or the Hubble telescope, and detecting suspects from surveillance cameras. Image processing concepts are introduced using tools like Photoshop and GIMP. Exposure to simple image processing programming with Java and MATLAB. This course is preparation for more advanced courses in the Digital Media area. Prerequisites: Any 100-level Computing Science course, plus knowledge of first-year level MATH, STAT; and introductory Java, C, or similar programming experience; or consent of the instructor or SCI 100. Open to students in the Faculty of Arts, Engineering and Sciences; others require consent of the instructor.Winter Term 2021
An introductory course on the theory and applications of computer based 3D modeling and animation. The course will cover a selection of topics from overview of tools supporting modeling and animation, automatically generating 3D models, and animation of skeleton based models through algorithms and software. Applications of 3D modeling and animation in games, virtual/augmented environments, movies, and emerging video transmission algorithms will be discussed. Prerequisites: Some background in image processing or graphics, e.g., CMPUT 206 or CMPUT 311; knowledge of first or preferably second-year level MATH/STAT, e.g., STAT 141/151/252 or 266, and MATH 214 or 225; experience in programming, e.g., CMPUT 174 or 100. Consent of the instructor needed if some background courses are lacking.Winter Term 2021
Introduction to visual recognition to recognize objects and classify scenes or images automatically by a computer. Supervised and unsupervised machine learning principles and deep learning techniques will be utilized for visual recognition. Successful commercial systems based on visual recognition range from entertainment to serious scientific research: face detection and recognition on personal devices, social media. Prerequisites: CMPUT 115 or 175; one of MATH 100, 113, 114, 117, 134, 144, 154; MATH 125; STAT 141, 151 or 235.Fall Term 2020
Quality assessment of image and video (or 3D data) is essential in many applications, which deliver educational content, medical images, games, movies, video-on-demand and so on. In order to generate high quality image and video, especially given the sheer volume of consumer demand and under constrained resources, e.g., time and bandwidth, it is necessary to understand the image and video processing pipeline from the initial creation limitations to the final display at the receiver. This course focuses on reviewing various image/video processing techniques, as well as the quality assessment metrics proposed in the literature. Sections offered in a Cost Recovery format at an increased rate of fee assessment; refer to the Fees Payment Guide in the University Regulations and Information for Students.Fall Term 2020