Artificial Intelligence Group

Welcome to the Artificial Intelligence Group

The Artificial Intelligence (AI) Group conducts research in many areas of artificial intelligence. The group has active interests in: models of intelligent interaction, multi-agent systems, natural language understanding, constraint programming, computational vision, robotics, machine learning, and reasoning under uncertainty.

News

News archive

  1. June 21, 2018PhD Seminar: Computer Vision on Web Pages: A Study of Man-Made Images

    Michael Cormier, PhD candidate
    David R. Cheriton School of Computer Science

    This thesis is focused on the development of computer vision techniques for parsing web pages using an image of the rendered page as evidence, and on understanding this under-explored class of images from the perspective of computer vision. This project is divided into two tracks — applied and theoretical — which complement each other. Our practical motivation is the application of improved web page parsing to assistive technology, such as screenreaders for visually impaired users or the ability to declutter the presentation of a web page for those with cognitive deficit. From a more theoretical standpoint, images of rendered web pages have interesting properties from a computer vision perspective; in particular, low-level assumptions can be made in this domain, but the most important cues are often subtle and can be highly non-local. The parsing system developed in this thesis is a principled Bayesian segmentation-classification pipeline, using innovative techniques to produce valuable results in this challenging domain. The thesis includes both implementation and evaluation solutions.

  2. June 29, 2018PhD Defence: Computer Vision on Web Pages: A Study of Man-Made Images

    Speaker: Michael Cormier, PhD candidate

    This thesis is focused on the development of computer vision techniques for parsing web pages using an image of the rendered page as evidence, and on understanding this under-explored class of images from the perspective of computer vision. This project is divided into two tracks — applied and theoretical — which complement each other. Our practical motivation is the application of improved web page parsing to assistive technology, such as screenreaders for visually impaired users or the ability to declutter the presentation of a web page for those with cognitive deficit. From a more theoretical standpoint, images of rendered web pages have interesting properties from a computer vision perspective; in particular, low-level assumptions can be made in this domain, but the most important cues are often subtle and can be highly non-local. The parsing system developed in this thesis is a principled Bayesian segmentation-classification pipeline, using innovative techniques to produce valuable results in this challenging domain. The thesis includes both implementation and evaluation solutions.

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