Tuesday, 22.3.2016, 11:30
Geometrical understanding of bendable and stretchable structures is crucial for many applications where comparison, inference and reconstruction play an important role. Moreover, it is the first step in quantifying normal and abnormal phenomena in non-rigid domains. Moving from Euclidean (straight) distances towards intrinsic (geodesic) measures, revolutionized the way we handle bendable structures, but did not take stretching into account. Human organs, such as the heart, lungs and kidneys, are great examples for such models. In this lecture I will show that stretching can be accounted for in the atom (local) level, in a closed form using higher derivatives of the data. I further show that invariants can play a critical part in modern learning systems, used for statistical analysis of non-rigid structures, and assist in fabricating soft-models. The lecture will be self-contained and no prior knowledge is needed.
Bio: Dan Raviv is a postdoctoral associate at the Camera Culture Group at MIT Media Lab, Massachusetts Institute of Technology. His current research provides new practical tools for analyzing geometric data. He earned his Ph.D. in Computer Science from the Technion - Israel institute of Technology, working in the Geometric Image Processing (GIP) laboratory on invariant metrics of non-rigid shapes. He has a bachelor degree, Summa cum Laude, in Mathematics and Computer Science, as an alumnus of Technion's Excellence Program, and a Master’s degree in Computer Science from the Technion. Dr. Raviv's research was granted several excellence prizes including the Gutwirth and Intel awards, and SIAM best paper award. Moreover, he was elected best lecturer on several occasions including the prestigious Technion directorship prize for outstanding adjunct teacher.