The University of Pennsylvania and Carnegie Mellon University have received a $5.65 million U.S.
Presenter: Domenico Daniele Bloisi (Homepage)
Wednesday October 2, 2013 from 1:00pm to 2:00pm
* Alternate Location: Levine 512 (3330 Walnut Street)*
In this talk a set of intelligent surveillance systems are presented. Possible solutions for solving automatic video surveillance challenges such as gradual and sudden illumination changes, modifications in the background geometry, dynamic background, shadows and reflections are discussed. Different state-of-the-art approaches for detecting and recognizing object of interest in the monitored scene, tracking them over time, and handling events are presented as well as examples and results from real systems.
Domenico Daniele Bloisi is an assistant professor with the Department of Computer, Control, and Management Engineering "Antonio Ruberti" at Sapienza University of Rome (Italy) since November 2012. He received his PhD, M.Sc. (best thesis award), and B.Sc. degrees in Computer Engineering from Sapienza University of Rome in 2010, 2006 and 2004, respectively. His main research interests are related to intelligent surveillance (including object detection, visual tracking, and multiple sensor data fusion) and robotics. He is a faculty member of RoCoCo (Cognitive Cooperating Robots) laboratory. He was visiting scholar at Kingston University, London (UK) in 2008 and reserch associate with the Department of Computer Science at University of Verona (Italy) in 2009. From 2010 to 2012 he collaborated with Finmeccanica Group for developing a port surveillance application. Since October 2007 he have taught courses on Computer Science at the Faculty of Engineering, Sapienza University of Rome. He serves as reviewer for several International Journals, including Pattern Recognition, Sensors, Information Fusion, and Neurocomputing.
Wednesday June 19, 2013
Konstantinos Gatsis Receives Best Student Paper Awardat ACC 2013
Konstantinos Gatsis, doctoral student in Electrical and Systems Engineering (ESE), is the recipient of the Best Student Paper Award at the 2013 American Control Conference held in Washington, DC. His paper, Optimal Power Management in Wireless Control Systems, considers a closed-loop control system implemented over a wireless channel and provides the theoretical framework to explore the trade-off between system performance and power consumption of the wireless sensors.
Presenter: Cesar Cadena (Homepage)
Monday October 7, 2013 from 12:00pm to 1:00pm
* Alternate Location: Levine 512 (3330 Walnut Street)*
The semantic mapping of the environment requires simultaneous segmentation and categorization of the acquired stream of sensory information. The existing methods typically consider the semantic mapping as the final goal and differ in the number and types of considered semantic categories. We envision semantic understanding of the environment as an on-going process and seek representations which can be refined and adapted depending on the task and robot's interaction with the environment.
Cesar Cadena received the Electronic Engineering and
Mechanical Engineering degrees in 2005 and the M.Sc. degree in 2006, all
from the Universidad de los Andes, Colombia. He received his Ph.D.
degree in Computer Science from the University of Zaragoza, Spain, in
2011, and was Research Assistant until June 2012 with the Group of
Robotics, Perception adn Real Time, at the same university. Since July
2013 he has been Postdoctoral researcher working Prof. Jana Kosecka at
the Computer Science Department of George Mason University.
His research is on robotics and control systems, with a current focus on persistent and semantic mapping in dynamic environments, life long learning for data association and place recognition problems.
Presenter: Viktor Gruev (Homepage)
Friday November 22, 2013 from 11:00am to 12:00pm
activity using light has opened up unprecedented possibilities in the quest of
understanding functionality of the nervous system. Light offers great
advantages over electrophysiology such as: incredible spatial resolution, which
is limited by the diffraction of light, contact-less probing capabilities,
which avoids physical damage and interference with neural activity during
recording, and simultaneous recording from large ensemble of neurons.
Viktor Gruev received his B.S. in electrical engineering with distinction from Southern Illinois University in 1997. He completed his M.S. and PhD. in electrical engineering from Johns Hopkins University in 2000 and 2004 respectively. Dr. Gruev was a post doctoral researcher at the University of Pennsylvania before he joined the Computer Science and Engineering faculty at Washington University in St. Louis in 2008. His current research interests are in: polarization imaging and integrating nano-fabrication techniques with CMOS technology, camera-on-a-chip, polarization image sensors, mixed signal VLSI systems, 3-D image sensors, VLSI systems for adaptive optics and computer vision.
Presenter: GRASP Faculty
Friday September 6, 2013 from 11:00am to 12:00pm
Presenter: Byron Stanley
Friday December 6, 2013 from 11:00am to 12:00pm
Few, if any, autonomous ground vehicles (AGVs) navigate successfully in adverse conditions, such as snow or GPS denied areas. A fundamental limitation is that they are using optical sensors, such as LIDAR or imagers, to fuse with GPS/INS solutions to localize themselves. When the optical surfaces become distorted or obscured, such as with snow, dust, or heavy rain, there is no robust way to localize the vehicle to the required accuracy.
Byron Stanley, co-inventor of the GPR Localization technology, has led the development of the autonomous systems component of the world's first autonomous vehicle to be guided via GPR localization. Byron Stanley has served as the Principal Investigator for several autonomous ground vehicle programs at MIT Lincoln Laboratory including indoor mapping and outdoor navigation. He has been developing robotics and control systems for ground,
maritime, and airborne applications as a full Technical Staff Member in the Control Systems Engineering group for the last 13 years at MIT Lincoln Laboratory and received the Engineering Division early career award in 2011. Byron Stanley received SM and SB degrees in 2001 and 1999 respectively at the Massachusetts Institute of Technology in mechanical engineering, where his contributions included a hardware-in-the-loop simulation for an autonomous air-drop system as a Draper Fellow and development of a touch sensitive chest and a series-elastic actuated hand for COG at the MIT Artificial Intelligence Laboratory.
This work is sponsored by the Assistant Secretary of Defense for Research & Engineering under Air Force Contract #FA8721‐05‐C‐0002. Opinions, interpretations, conclusions and recommendations are those of the author and are not necessarily endorsed by the United States Government.
Presenter: Joachim Buhmann (Homepage)
Friday September 27, 2013 from 11:00am to 12:00pm
The digital revolution has created unprecedented opportunities in computing and communication but it also has generated the data deluge with an urgent demand for new pattern recognition technology. Learning patterns in data requires to extract interesting, statistically significant regularities in (large) data sets, e.g. the identification of connection patterns in the brain (connectomics) or the detection of cancer cells in tissue microarrays and estimating their staining as a cancer severity score.
Joachim M. Buhmann leads the Machine Learning Laboratory in the Department of Computer Science at ETH Zurich. He has been a full professor of Information Science and Engineering since October 2003. He studied physics at the Technical University Munich and obtained his PhD in Theoretical Physics. As postdoc and research assistant professor, he spent 1988-92 at the University of Southern California, Los Angeles, and the Lawrence Livermore National Laboratory. He held a professorship for applied computer science at the University of Bonn, Germany from 1992 to 2003. His research interests spans the areas of pattern recognition and data analysis, including machine learning, statistical learning theory and information theory. Application areas of his research include image analysis, medical imaging, acoustic processing and bioinformatics. Currently, he serves as president of the German Pattern Recognition Society.