homepage: | http://www.ri.cmu.edu/people/lucey_simon.html |
search externally: | Google Scholar, Springer, CiteSeer, Microsoft Academic Search, Scirus , DBlife |
Description
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.
Lectures:
tutorial Learning in Computer Vision as author at Machine Learning Summer School (MLSS), Kioloa 2008, 53038 views |
demonstration video Poster Spotlights 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, 5084 views |
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