News Highlight

Title: Distribution Fields: A Unifying Representation for Low-Level Vision Problems

Speaker: Erik Learned-Miller (http://people.cs.umass.edu/~elm/), University of Massachusetts, Amherst

Time: Thursday, October 25th, 2:00pm-3:00pm

Location: Babbio Center 203

Host: Gang Hua

Abstract:

Consider the following fundamental problem of low level vision: given a large image I an a patch J from another image, find the "best matching" location of the patch J to image I. We believe the solution to this problem can be significantly improved. A significantly better solution to this problem has the potential to improve a wide variety of low-level vision problems, such as backgrounding, tracking, medical image registration, optical flow, image stitching, and invariant feature definition.

We introduce a set of techniques for solving this problem based upon a representation called distribution fields. Distribution fields are an attempt to take the best from a wide variety of low-level vision techniques including geometric blur (Berg), mixture of Gaussians backgrounding (Stauffer), SIFT (Lowe) and HoG (Dalal and Triggs), local color histograms, bilateral filtering, congealing (Learned-Miller) and many other techniques.

We show how distribution fields solve this "patch" matching problem, and, in addition to finding the optimum match of patch J to image I with a high success rate, the algorithm produces, as a by-product, a very natural assessment of the quality of that match. We call this algorithm the "sharpening match".
Using the sharpening match for tracking yields an extremely simple but state-of-the-art tracker. He also discuss application of these techniques to background subtraction and other low level vision problems.

Brief Bio:

Erik G. Learned-Miller (previously Erik G. Miller) is an Associate Professor of Computer Science at the University of Massachusetts, Amherst, where he joined the faculty in 2004. He spent two years as a post-doctoral researcher at the University of California, Berkeley, in the Computer Science Division. Learned-Miller received a B.A. in Psychology from Yale University in 1988. In 1989, he co-founded CORITechs, Inc., where he and co-founder Rob Riker developed the second FDA cleared system for image-guided neurosurgery. He worked for Nomos Corporation, Pittsburgh, PA, for two years as the manager of neurosurgical product engineering. He obtained Master of Science (1997) and Ph. D. (2002) degrees from the Massachusetts Institute of Technology, both in Electrical Engineering and Computer Science. In 2006, he received an NSF CAREER award for his work in computer vision and machine learning.