[cs-talks] Upcoming Seminars: IVC (Tues) + BUSec (Wed)
cs at bu.edu
Mon Sep 14 10:39:11 EDT 2015
Seeing the invisible: Detecting camouflaged animals with motion segmentation
Erik Learned-Miller, UMass, Amherst
Tuesday, September 15th, 2015 (2-3pm) in MCS 148
Abstract: This talk is about detecting moving objects. The ultimate challenge for these methods is to see well-camouflaged objects, such as animals with natural camouflage, which are virtually undetectable until they move. To make the problem even more challenging, we consider the detection of camouflaged objects when the camera is moving relative to the background. We detect these objects by segmenting them based upon their motion. In particular, we estimate the parameters of the background motion (translation and rotation) and highlight objects that are moving in a way that is inconsistent with this motion. We demonstrate our methods on some challenging videos including videos of naturally camouflaged animals.
Related publication: (ICCV 2013) https://people.cs.umass.edu/~elm/papers/motionSegICCV13.pdf
Bio: Erik 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 co-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.
Jeremiah Blocki, MSR, Towards Usable and Secure Human Authentication
Wednesday, September 16, 2015 at 10AM in MCS 180 (Hariri Seminar Room)
Abstract: A typical computer user today manages passwords for many different online accounts. Users struggle with this task — often forgetting their passwords or adopting insecure practices, such as using the same passwords for multiple accounts and selecting weak passwords. While there are many books, articles, papers and even comics about selecting strong individual passwords, there is very little work on password management schemes — systematic strategies to help users create and remember multiple passwords. Before we can design good password management schemes it is necessary to address a fundamental question: How can we quantify the usability or security of a password management scheme.
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