Computer Vision & Robotics
Hi! My name isI live in Toronto, Canada and I’m a Postdoctoral Visitor at the John Tsotsos Laboratory for Active and Attentive Vision, York University. I am interested in Computer Vision, Active Vision, Robotics, Visuospatial Behaviours, Machine Learning, Big Data Analytics and single-board computer. In my free time, I enjoy travelling and sports.
Most past and present research in computer vision involves passively observed data. Humans, however, are active observers outside the lab; they explore, search, select what and how to look. Nonetheless, how exactly active observation occurs in humans so that it can inform the design of active computer vision systems is an open problem. PESAO is designed for investigating active, visual observation in a 3D world.
It is almost universal to regard attention as the facility that permits an agent, human or machine, to give priority processing resources to relevant stimuli while ignoring the irrelevant. The reality of how this might manifest itself throughout all the forms of perceptual and cognitive processes possessed by humans, however, is not as clear. Here we examine this reality with a broad perspective in order to highlight the myriad ways that attentional processes impact both perception and cognition.
Perceptual judgment of image similarity by humans relies on rich internal representations ranging from low-level features to high-level concepts, scene properties and even cultural associations. However, existing methods and datasets attempting to explain perceived similarity use stimuli that arguably do not cover the full breadth of factors that affect human similarity judgments, even those geared toward this goal.
We present a Polyhedral Scene Generator system that creates a random scene based on a few user parameters, renders the scene from random viewpoints and creates a dataset containing the renderings and corresponding annotation files. We hope that this generator will enable research on how a program could parse a scene if it had multiple viewpoints to consider.
Falls are serious and costly for elderly people. The Centers for Disease Control and Prevention of the US reports that millions of older people, 65 and older, fall each year at least once. Serious injuries such as; hip fractures, broken bones or head injury, are caused by 20% of the falls. The time it takes to respond and treat a fallen person is crucial. With this paper, we present a new, non-invasive system for fallen people detection.
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