*** Professional Development Prize Pack (Padfolio and CSC giveaways) and Five Guys Gift Card!***

***All students who participate will be entered to win Capitol Cities Tickets for Quadmania***

Center for Hybrid Multicore Productivity Research
Distinguished Computational Science Lecture Series

Image Registration for Multisource Remote Sensing

Dr. Jacqueline Le Moigne
NASA Goddard Space Flight Center
Software Engineering Division – Code 580

3:00pm Thursday, 3 April 2014, ITE 456, UMBC

Satellite remote sensing systems provide large amounts of global coverage and repetitive measurements representing simultaneous or multi-temporal observations of the same features by different sensors; for example over the last 40 years, Landsat satellites have been acquiring more than three million images representing about one petabyte of data. Furthermore, most sensors are carried on separate platforms, resulting in a tremendous amount of data that must be combined. In meeting some of the Earth System Science objectives, the combination of all these data at various resolutions — spatial, radiometric and temporal — will facilitate a better understanding of Earth and space science phenomena, and image registration enables the first step towards this integration.

In this talk, we will describe the image registration challenge in the context of Earth and space remote sensing. Then, we will review a subset of the methods that are being utilized to tackle this challenge, and finally we will describe some of our work that utilizes multiscale representations, in particular wavelets and over-complete representations, as well as more recent work dealing with the registration of Martian data based on crater extraction and matching.

Dr. Le Moigne is the Assistant Chief for Technology in the Software Engineering Division at NASA Goddard, and was Goddard Center Associate for ESTO/Advanced Information Systems Technology Program, from 2009 to 2012. Dr. Le Moigne received a B.S. and a M.S. in Mathematics, and a Ph.D. in Computer Science from the University Pierre and Marie Curie, France. While her Ph.D. thesis dealt with biomedical imagery, her post-doctoral work at the UMD Computer Vision Lab focused on the development of a visual navigation system for the first DARPA Autonomous Land Vehicle project. At NASA Goddard since 1990, Dr. Le Moigne has performed extensive work in the processing and the analysis of remote sensing data. Her work particularly focuses on image registration, utilizing multiscale representations as well as high-performance and on-board processing. More recent work dealt with creating web-based access to benchmark data for Image Processing education and research (imageseer.nasa.gov). Currently, Dr. Le Moigne is the PI of a Goddard Internal project focused on Distributed Spacecraft Missions. She has published over 120 publications, including 23 journal papers, holds one patent, and has co-edited a book on “Image Registration for Remote Sensing” published by Cambridge University Press in 2011. She is a NASA Goddard Senior Fellow, an IEEE Senior Member and an ABET Program Evaluator. She was a NATO Science for Peace and Security Committee Panel Member from 2008 to 2011. In 2012, she received the NASA Exceptional Service Medal and the Goddard Information Science and Technology Award.

Center for Hybrid Multicore Productivity Research
Distinguished Computational Science Lecture Series

Image Registration for Multisource Remote Sensing

Dr. Jacqueline Le Moigne
NASA Goddard Space Flight Center
Software Engineering Division – Code 580

3:00pm Thursday, 3 April 2014, ITE 456, UMBC

Satellite remote sensing systems provide large amounts of global coverage and repetitive measurements representing simultaneous or multi-temporal observations of the same features by different sensors; for example over the last 40 years, Landsat satellites have been acquiring more than three million images representing about one petabyte of data. Furthermore, most sensors are carried on separate platforms, resulting in a tremendous amount of data that must be combined. In meeting some of the Earth System Science objectives, the combination of all these data at various resolutions — spatial, radiometric and temporal — will facilitate a better understanding of Earth and space science phenomena, and image registration enables the first step towards this integration.

In this talk, we will describe the image registration challenge in the context of Earth and space remote sensing. Then, we will review a subset of the methods that are being utilized to tackle this challenge, and finally we will describe some of our work that utilizes multiscale representations, in particular wavelets and over-complete representations, as well as more recent work dealing with the registration of Martian data based on crater extraction and matching.

Dr. Le Moigne is the Assistant Chief for Technology in the Software Engineering Division at NASA Goddard, and was Goddard Center Associate for ESTO/Advanced Information Systems Technology Program, from 2009 to 2012. Dr. Le Moigne received a B.S. and a M.S. in Mathematics, and a Ph.D. in Computer Science from the University Pierre and Marie Curie, France. While her Ph.D. thesis dealt with biomedical imagery, her post-doctoral work at the UMD Computer Vision Lab focused on the development of a visual navigation system for the first DARPA Autonomous Land Vehicle project. At NASA Goddard since 1990, Dr. Le Moigne has performed extensive work in the processing and the analysis of remote sensing data. Her work particularly focuses on image registration, utilizing multiscale representations as well as high-performance and on-board processing. More recent work dealt with creating web-based access to benchmark data for Image Processing education and research (imageseer.nasa.gov). Currently, Dr. Le Moigne is the PI of a Goddard Internal project focused on Distributed Spacecraft Missions. She has published over 120 publications, including 23 journal papers, holds one patent, and has co-edited a book on “Image Registration for Remote Sensing” published by Cambridge University Press in 2011. She is a NASA Goddard Senior Fellow, an IEEE Senior Member and an ABET Program Evaluator. She was a NATO Science for Peace and Security Committee Panel Member from 2008 to 2011. In 2012, she received the NASA Exceptional Service Medal and the Goddard Information Science and Technology Award.

This coming weekend, the UMBC chess team will play for the President’s Cup in the Final Four of College Chess. UMBC will compete with chess teams from Webster University, the University of Illinois at Urbana Champaign, and Texas Tech University.

The match has been held each year since 2001 between the top four US schools from the Pan American Intercollegiate Team Chess Championship and the winner is considered the top chess team among U.S. colleges and universities.  UMBC has placed first at the Final Four a record six times.

CSEE Professor Alan Sherman, who is the director of the UMBC chess program, will accompany the team to the match, which will be held at the New York Athletic Club in New York City on Friday through Sunday, April 4-6, 2014. Games will be broadcast live on Monroi.

The UMBC team will consist of:

• Board 1: GM Niclas “The Dark Knight” Huschenbeth (USCF rating 2610)
• Board 2: GM Akshayraj “The Indian Knight” Kore (2519)
• Board 3: M Levan “The Georgian Gangster” Bregadze (2469)
• Board 4: IM and WGM Nazi “The Black Widow” Paikidze (2378)
• Alternate: WGM Sabina “Sunshine” Foisor (2315)

This coming weekend, the UMBC chess team will play for the President’s Cup in the Final Four of College Chess. UMBC will compete with chess teams from Webster University, the University of Illinois at Urbana Champaign, and Texas Tech University.

The match has been held each year since 2001 between the top four US schools from the Pan American Intercollegiate Team Chess Championship and the winner is considered the top chess team among U.S. colleges and universities.  UMBC has placed first at the Final Four a record six times.

CSEE Professor Alan Sherman, who is the director of the UMBC chess program, will accompany the team to the match, which will be held at the New York Athletic Club in New York City on Friday through Sunday, April 4-6, 2014. Games will be broadcast live on Monroi.

The UMBC team will consist of:

• Board 1: GM Niclas “The Dark Knight” Huschenbeth (USCF rating 2610)
• Board 2: GM Akshayraj “The Indian Knight” Kore (2519)
• Board 3: M Levan “The Georgian Gangster” Bregadze (2469)
• Board 4: IM and WGM Nazi “The Black Widow” Paikidze (2378)
• Alternate: WGM Sabina “Sunshine” Foisor (2315)

Talking to Robots: Learning to Ground Human
Language in Robotic Perception

Cynthia Matuszek
University of Washington

1:00pm Monday, 7 April 2014, ITE325b, UMBC

Advances in computation, sensing, and hardware are enabling robots to perform an increasing variety of tasks in ever less constrained settings. It is now possible to imagine robots that can operate in traditionally human-centric settings. However, such robots need the flexibility to take instructions and learn about tasks from nonspecialists using language and other natural modalities. At the same time, learning to process natural language about the physical world is difficult without a robot’s sensors and actuators. Combining these areas to create useful robotic systems is a fundamentally multidisciplinary problem, requiring advances in natural language processing, machine learning, robotics, and human-robot interaction. In this talk, I describe my work on learning natural language from end users in a physical context; such language allows a person to communicate their needs in a natural, unscripted way. I demonstrate that this approach can enable a robot to follow directions, learn about novel objects in the world, and perform simple tasks such as navigating an unfamiliar map or putting away objects.

Cynthia Matuszek is a Ph.D. candidate in the University of Washington Computer Science and Engineering department, where she is a member of both the Robotics and State Estimation lab and the Language, Interaction, and Learning group. She earned a B.S. in Computer Science from the University of Texas at Austin, and M.Sc. from the University of Washington. She is published in the areas of artificial intelligence, robotics, ubiquitous computing, and human-robot interaction.

Talking to Robots: Learning to Ground Human
Language in Robotic Perception

Cynthia Matuszek
University of Washington

1:00pm Monday, 7 April 2014, ITE325b, UMBC

Advances in computation, sensing, and hardware are enabling robots to perform an increasing variety of tasks in ever less constrained settings. It is now possible to imagine robots that can operate in traditionally human-centric settings. However, such robots need the flexibility to take instructions and learn about tasks from nonspecialists using language and other natural modalities. At the same time, learning to process natural language about the physical world is difficult without a robot’s sensors and actuators. Combining these areas to create useful robotic systems is a fundamentally multidisciplinary problem, requiring advances in natural language processing, machine learning, robotics, and human-robot interaction. In this talk, I describe my work on learning natural language from end users in a physical context; such language allows a person to communicate their needs in a natural, unscripted way. I demonstrate that this approach can enable a robot to follow directions, learn about novel objects in the world, and perform simple tasks such as navigating an unfamiliar map or putting away objects.

Cynthia Matuszek is a Ph.D. candidate in the University of Washington Computer Science and Engineering department, where she is a member of both the Robotics and State Estimation lab and the Language, Interaction, and Learning group. She earned a B.S. in Computer Science from the University of Texas at Austin, and M.Sc. from the University of Washington. She is published in the areas of artificial intelligence, robotics, ubiquitous computing, and human-robot interaction.