[4eyes] [FACULTY] CS Colloquium: February 15, 2012: Ben Shneiderman

[Matthew Turk]

There are two talks this week very relevant to the lab's research interests,
which I hope most of you can attend, both in the CS conference room:

- Tuesday at 11:00am, Mubarak Shah, University of Central Florida
  "Motion Patterns: A Semantic Representation of Actions, Events, and
Activities"

- Wednesday at 3:30pm, Ben Shneiderman, University of Maryland
  "Information Visualization for Social Media Network Analysis"

Details of both are below.

	Matthew

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"Motion Patterns: A Semantic Representation of Actions, Events, and
Activities"
Mubarak Shah, Dept. of Electrical Engineering and Computer Science,
University of Central Florida

Feb 14 (Tue) 11:00 am
Computer Science Conference Room, Harold Frank Hall Room 1132

ABSTRACT:
Automatic analysis of videos is one of most challenging problems in Computer
vision. In this talk I will introduce the problem of action, event, and
activity representation and recognition from video sequences. I will begin
by giving a brief overview of a few interesting methods to solve this
problem, including trajectories, volumes, and local interest points based
representations.

The main part of the talk will focus on a newly developed framework for the
discovery and statistical representation of motion patterns in videos, which
can act as primitive, atomic actions. These action primitives are employed
as a generalizable representation of articulated human actions, gestures,
and facial expressions. The motion primitives are learned by hierarchical
clustering of observed optical flow in four dimensional, spatial and motion
flow space, and a sequence of these primitives can be represented as a
simple string, a histogram, or a Hidden Markov model.

I will then describe methods to extend the framework of motion patterns
estimation to the problem of multi-agent activity recognition. First, I will
talk about transformation invariant matching of motion patterns in order to
recognize simple events in surveillance scenarios. I will end the talk by
presenting a framework in which a motion pattern represents the behavior of
a single agent, while multi-agent activity takes the form of a graph, which
can be compared to other activity graphs, by attributed inexact graph
matching. This method is applied to the problem of American football plays
recognition.


BIO:
Dr. Mubarak Shah, Agere Chair Professor of Computer Science, is the founding
director of the Computer Visions Lab at University of Central Florida (UCF).
He is a co-author of three books (Motion-Based Recognition (1997), Video
Registration (2003), and Automated Multi-Camera Surveillance: Algorithms and
Practice (2008)), all by Springer.  He has published extensively on topics
related to visual surveillance, tracking, human activity and action
recognition, object detection and categorization, shape from shading, geo
registration, visual crowd analysis, etc. Dr. Shah is a fellow of IEEE,
IAPR, AAAS and SPIE. In 2006, he was awarded the Pegasus Professor award,
the highest award at UCF, given to a faculty member who has made a
significant impact on the university, has made an extraordinary contribution
to the university community, and has demonstrated excellence in teaching,
research and service. He is ACM Distinguished Speaker. He was an IEEE
Distinguished Visitor speaker for
1997-2000, and received IEEE Outstanding Engineering Educator Award in 1997.
He received the Harris Corporation's Engineering Achievement Award in 1999,
the TOKTEN awards from UNDP in 1995, 1997, and 2000; SANA award in 2007, an
honorable mention for the ICCV 2005 Where Am I? Challenge Problem, and was
nominated for the best paper award in ACM Multimedia Conference in 2005 and
2010. At UCF he received Scholarship of Teaching and Learning (SoTL) award
in 20111; College of Engineering and Computer Science Advisory Board award
for faculty excellence in 2011; Teaching Incentive Program awards in 1995
and 2003, Research Incentive Award in 2003 and 2009, Millionaires' Club
awards in 2005, 2006,  2009, 2010 and 2011; University Distinguished
Researcher award in 2007.  He is an editor of international book series on
Video Computing; editor in chief of Machine Vision and Applications journal,
and an associate editor of ACM Computing Surveys journal. He was an
associate editor of the
IEEE Transactions on PAMI, and a guest editor of the special issue of
International Journal of Computer Vision on Video Computing. He was the
program co-chair of IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 2008.

Hosted by: Professor B. S. Manjunath

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UCSB COMPUTER SCIENCE DEPARTMENT PRESENTS:

Wednesday, February 15, 2012
3:30 - 4:30 PM
Computer Science Conference Room, Harold Frank Hall Rm. 1132

HOST: Matthew Turk

SPEAKER: Ben Shneiderman
University of Maryland

Title: Information Visualization for Social Media Network Analysis

Abstract:

Network visualization has been a lively topic for a half century, but 
the intense challenges from many facets of this problem demand diverse 
solutions. While the popular force-directed approaches produce appealing 
presentations, they are often so cluttered that the benefits are limited 
to showing large clusters and disconnected outliers.

Interactive approaches that give users control of node and link 
visibility enable them to make more fine-grained analyses that lead to 
important insights about relationships among nodes or the presence of 
exceptional nodes and links. Another important task is to spot the 
absence of expected nodes and links. One strategy is coordinating 
network visualizations with statistical measures from graph theory and 
social network analysis to give users interactive control of ranking, 
filtering and clustering (http://www.cs.umd.edu/hcil/socialaction). A 
second strategy involves a novel layout technique to arrange node 
positions according to their attributes in stable yet comprehensible 
semantic substrates (http://www.cs.umd.edu/hcil/nvss). These novel 
strategies have influenced the design of the novel network visualization 
tool that is embedded in Excel: Network Overview for Discovery and 
Exploration in Excel (NodeXL:http://www.codeplex.com/nodexl ). The main 
application has been social media networks extracted from Twitter, 
Facebook, Flickr, or YouTube usage patterns.

Bio:

Ben Shneiderman is a Professor in the Department of Computer Science and 
Founding Director (1983-2000) of the Human-Computer Interaction 
Laboratory (http://www.cs.umd.edu/hcil/) at the University of Maryland. 
He is a Fellow of the AAAS, ACM, and IEEE, and a Member of the National 
Academy of Engineering.

Prof. Shneiderman is the co-author with Catherine Plaisant of Designing 
the User Interface: Strategies for Effective Human-Computer Interaction 
(5th ed., 2010, http://www.awl.com/DTUI/). With Stu Card and Jock 
Mackinlay, he co-authored Readings in Information Visualization: Using 
Vision to Think (1999). His bookLeonardo's Laptop appeared in October 
2002 (MIT Press) and won the IEEE book award for Distinguished Literary 
Contribution. His latest book, with Derek Hansen and Marc Smith, is 
Analyzing Social Media Networks with NodeXL (www.codeplex.com/nodexl, 
2010). This is available for sale on Amazon.com, and Ben would be happy 
to sign copies of the book after his talk.

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