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Spring 2013 GRASP Seminar: Raquel Urtasun, Toyota Technological Institute At Chicago, "Efficient Algorithms For Semantic Scene Parsing"
Presenter: Raquel Urtasun (Homepage)
Friday February 1, 2013 from 11:00am to 12:00pm
Developing autonomous systems that are able to assist humans in everyday’s tasks is one of the grand challenges in modern computer science. Notable examples are personal robotics for the elderly and people with disabilities, as well as autonomous driving systems which can help decrease fatalities caused by traffic accidents. In order to perform tasks such as navigation, recognition and manipulation of objects, these systems should be able to efficiently extract 3D knowledge of their environment. While a variety of novel sensors have been developed in the past few years, in this work we focus on the extraction of this knowledge from visual information alone. In this talk, I'll show how Markov random fields provide a great mathematical formalism to extract this knowledge. In particular, I'll focus on a few examples, i.e., 3D reconstruction, 3D layout estimation, 2D holistic parsing and object detection, and show representations and inference strategies that allow us to achieve state-of-the-art performance as well as several orders of magnitude speed-ups.
Raquel Urtasun is an Assistant Professor at TTI-Chicago aphilanthropically endowed academic institute located in the campus of the University of Chicago. She was a visiting professor at ETH Zurich during the spring semester of 2010. Previously, she was a postdoctoral research scientist at UC Berkeley and ICSI and a postdoctoral associate at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Raquel Urtasun completed her PhD at the Computer Vision Laboratory, at EPFL, Switzerland in 2006 working with Pascal Fua and David Fleet at the University of Toronto. She has been area chair of multiple learning and vision conferences (i.e., NIPS, UAI, ICML, ICCV), and served in the committee of numerous international computer vision and machine learning conferences. Her major interests are statistical machine learning and computer vision, with a particular interest in non-parametric Bayesian statistics, latent variable models, structured prediction and their application to semantic scene understanding.