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Dr. Simon Lucey is Systems Scientist in the
Robotics Institute at
Carnegie Mellon University.
In his tutorial he will cover some of the core fundamentals in vision and demonstrate how they can be interpreted in terms of machine learning fundamentals. Unbeknownst to most researchers in the field of machine learning, the fundamentals of object registration and tracking such as optical flow, interest descriptors (e.g., SIFT), segmentation and correlation filters are inherently related to the learning topics of regression, regularization, graphical models, generative models and discriminative models. As a result many aspects of vision can be interpreted as applied forms of learning. From this discussion on fundamentals we shall also explore advanced topics in object registration and tracking such as non-rigid object alignment/ tracking and non-rigid structure from motion and how the application of machine learning is continuing to improve these technologies.
Learning in Computer Vision
as author at Machine Learning Summer School (MLSS), Kioloa 2008,
as author at 23rd IEEE Conference on Computer Vision and Pattern Recognition 2010 - San Francisco,
together with: Simon Baker, Yu-Wing Tai, David Marimon, Yunpeng Li, Deqing Sun, Shaojie Zhuo, Jingyi Yu, Xiaohui Shen, Manuel Werlberger, Christopher Kanan, Dhruv Batra, Kyong Joon Lee, Ayan Chakrabarti, Iasonas Kokkinos, Matthew Zeiler, Hanno Scharr, David S. Bolme, Marc’Aurelio Ranzato, Y-Lan Boureau,