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Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services

October 5, 2011

Speaker: Ms. Huixuan Tang, PhD Student, Department of Computer Science, University of Toronto

Title: Learning a Blind Measure for Perceptual Image Quality

Abstract: In this talk I present our paper on blind measurement of perceptual image quality in CVPR2010. Evaluating the perceptual quality of an image is a desirable but challenging problem. However, most commonly used measure do not map well to human judgement of image quality. A further complication of many existing image measure is that they require a reference image, which is often not available in practice. We propose a "blind" image quality measure, where potentially neither the ground-truth image nor the degradation process are known. Our method uses a set of novel low-level image features in a machine learning framework to learn a mapping from these features to subjective image quality scores. The image quality features stem from natural image measure and texture statistics. Neighbourhood embedding of our proposed features well clusters images of similar quality and relevant distortion type, indicating good potential to generalize our learned measure to new images and distortion types. Experiments on a standard image quality benchmark dataset shows that our method significantly outperforms state of art no-reference image assessment algorithm in all aspects.

Biography: Huixuan Tang is a PhD student at University of Toronto. Her research interest lies in computer vision and machine learning, with an concentration in computational photography. During her study, she interned at Microsoft Research Asia in 2006-2007 and Microsoft Research in 2010.