prof.irfanessa.com

In Spring 2012, I am teaching 2 classes.

Advanced Computational Photography (CS 8803 PHO) [with Grant Schindler]

This is an advanced topics class in Computational Photography, building on my intro class and explores technical aspects of pictures, and more precisely the capture and depiction of reality on a 2D medium. The scientific, perceptual, and artistic principles behind image-making will be emphasized. Topics include the relationship between pictorial techniques and the human visual system; intrinsic limitations of 2D representations and their possible compensations; and technical issues involving depiction. Technical aspects of image capture and rendering, and exploration of how such a medium can be used to its maximum potential, will be examined. Students are strongly encouraged (not required) to bring their digital cameras and a laptop to facilitate experiments. The class will explore recent and state of the art paper in Computational Photography from leading conferences and journals in the area and students will do projects in a variety of topics.

Computation + Journalism (CS 4464 / CS 6465)

This class is aimed at understanding the computational and technological advancements in the area of journalism. Primary focus is on the study of technologies for developing new tools for (a) sense-making from diverse news information sources, (b) the impact of more and cheaper networked sensors (c) collaborative human models for information aggregation and sense-making, (d) mashups and the use of programming in journalism, (e) the impact of mobile computing and data gathering, (f) computational approaches to information quality, (g) data mining for personalization and aggregation, and (h) citizen journalism. Complete schedule and other information will be on the t-square site available to only students taking the class.

Just a brief post to welcome in the new year and to wish all a very happy 2012.  2011 was a very busy and productive year in terms of research and other academic pursuits (follow the 2011 tag for an overview).   I am especially thankful to my team and my collaborators who have made 2011 such a successful year. I expect to 2012 to be equally productive.

Again, Happy 2012 and Best Wishes to all.

Spatio-temporal Data Interpolation for Dynamic Scene Analysis

Kihwan Kim, PhD Candidate

School of Interactive Computing, College of Computing, Georgia Institute of Technology

Date: Tuesday, December 6, 2011

Time: 1:00 pm – 3:00 pm EST

Location: Technology Square Research Building (TSRB) Room 223

Abstract

Analysis and visualization of dynamic scenes is often constrained by the amount of spatio-temporal information available from the environment. In most scenarios, we have to account for incomplete information and sparse motion data, requiring us to employ interpolation and approximation methods to fill for the missing information. Scattered data interpolation and approximation techniques have been widely used for solving the problem of completing surfaces and images with incomplete input data. We introduce approaches for such data interpolation and approximation from limited sensors, into the domain of analyzing and visualizing dynamic scenes. Data from dynamic scenes is subject to constraints due to the spatial layout of the scene and/or the configurations of video cameras in use. Such constraints include: (1) sparsely available cameras observing the scene, (2) limited field of view provided by the cameras in use, (3) incomplete motion at a specific moment, and (4) varying frame rates due to different exposures and resolutions.

In this thesis, we establish these forms of incompleteness in the scene, as spatio- temporal uncertainties, and propose solutions for resolving the uncertainties by applying scattered data approximation into a spatio-temporal domain.

The main contributions of this research are as follows: First, we provide an effi- cient framework to visualize large-scale dynamic scenes from distributed static videos. Second, we adopt Radial Basis Function (RBF) interpolation to the spatio-temporal domain to generate global motion tendency. The tendency, represented by a dense flow field, is used to optimally pan and tilt a video camera. Third, we propose a method to represent motion trajectories using stochastic vector fields. Gaussian Pro- cess Regression (GPR) is used to generate a dense vector field and the certainty of each vector in the field. The generated stochastic fields are used for recognizing motion patterns under varying frame-rate and incompleteness of the input videos. Fourth, we also show that the stochastic representation of vector field can also be used for modeling global tendency to detect the region of interests in dynamic scenes with camera motion. We evaluate and demonstrate our approaches in several applications for visualizing virtual cities, automating sports broadcasting, and recognizing traffic patterns in surveillance videos.

Committee:

• Prof. Irfan Essa (Advisor, School of Interactive Computing, Georgia Institute of Technology)
• Prof. James M. Rehg (School of Interactive Computing, Georgia Institute of Technology)
• Prof. Thad Starner (School of Interactive Computing, Georgia Institute of Technology)
• Prof. Greg Turk (School of Interactive Computing, Georgia Institute of Technology)
• Prof. Jessica K. Hodgins (Robotics Institute, Carnegie Mellon University, and Disney Research Pittsburgh)

Via Georgia Tech School of Interactive Computing‘s Website > Essa, Egerstedt Named IEEE Fellows.

The IEEE Board of Directors has elected professors Irfan Essa and Magnus Egerstedt (both Interactive Computing) as Fellows in its Class of 2012.

Essa is a professor whose research focus is in computer vision, computer graphics, computational perception, robotics and computer animation. In his Fellow citation, Essa was lauded for “contributions to computer vision and graphics.”

“I feel honored to be selected to be part of a group of my peers that I respect and who have made amazing contributions to their fields,” Essa said. “I am glad that my contributions to computer vision and graphics are considered worthy for this honor, and I intend to continue working on my multi-disciplinary research.”

Egerstedt, an adjunct faculty member in the School of Interactive Computing with a primary appointment in the School of Electrical and Computer Engineering, works in optimal control, as well as modeling and analysis of hybrid and discrete event systems, with emphasis on motion planning and control of (teams of) mobile robots. His IEEE citation acknowledged “contributions to hybrid and networked control, with applications in robotics.”

Both professors are affiliated with the Robotics & Intelligent Machines (RIM) Center.

The IEEE Grade of Fellow is conferred by the Board of Directors upon those members with extraordinary records of accomplishment in any IEEE field of interest. IEEE Fellow is the highest grade of membership and is recognized by the technical community as a prestigious honor and an important career achievement. For a full list of the Fellow Class of 2012, visit the IEEE website.

Computational Journalism is defined as the application of computation to the activities of journalism such as information gathering, organization, communication, and dissemination of information, while upholding values of journalism such as accuracy and verifiability. Journalists are increasingly adopting and using the proliferation of open-source tools and embracing different styles of journalism. Explore how newsrooms are opening, what new tools are being created, and how to use those tools most effectively.

• Part of the Events associated with: FutureMedia Fest 2011 (Panel WebSite hosted by FutureMedia Fest 2011)
• Date and time: Tue, 11/15/2011 – 2:15 PM – 3:25 PM
• Location: Georgia Tech Hotel, Atlanta, GA 30308, USA.

Panelists:

• Irfan Essa, Professor, School of Interactive Computing, Georgia Tech [Bio] [Website] [Projects: CnJ].
• David Clinch, Clinch Media [Bio] [Project: Storyful.com to curatehttp://youtube.com/news and http://youtube.com/politics]
• Eric Gilbert, Assistant Professor, School of Interactive Computing, Georgia Tech [Bio] [Website] [Projects: comp.social].
• King-wa Fu, Research Assistant Professor, Journalism and Media Studies Centre University of Hong Kong. [Bio] [Project: Real-time Sina Weibo statistics
• John Perry, Database Specialist, Investigative Team, Atlanta Journal-Constitution.
• Leonard Witt, Robert D. Fowler Distinguished Chair in Communication at Kennesaw State University. [Bio] [Projects: JJIE.org, PJNet.org].

Topics of discussion will include (but will not be limited to):

• What is Computational Journalism?
• What impact has Computation / Information Technology / Networking Technology had on Journalism?
• What is the newsroom of the future? How has the newsroom changed?
• How has investigative journalism changed with new technologies?
• How is social networking changed how we gather, distribute, and share news (and information)?
• What are the economic / financial models that need to explored to support (and sustain) journalism?
• What is the role of an Editor in the new journalism model?
• What should we be teaching the next generation of journalists?

via CnJ Panel at Georgia Tech’s Future Media Fest 2011 | Computation + Journalism.

Gaussian Process Regression Flow for Analysis of Motion Trajectories

• Kim, Lee, and Essa (2011), “Gaussian Process Regression Flow for Analysis of Motion Trajectories,” in Proceedings of IEEE International Conference on Computer Vision (ICCV), 2011. [PDF] [WEBSITE] [VIDEO] [BIBTEX]

   @inproceedings{Kim2011-GPRF, Author = {K. Kim and D. Lee and I. Essa}, Booktitle = {Proceedings of IEEE International Conference on Computer Vision (ICCV)}, Month = {November}, Pdf = {http://www.cc.gatech.edu/~irfan/p/2011-Kim-GPRFAMT.pdf}, Publisher = {IEEE Computer Society}, Title = {Gaussian Process Regression Flow for Analysis of Motion Trajectories}, Url = {http://www.cc.gatech.edu/cpl/projects/gprf/}, Video = {http://www.youtube.com/watch?v=UtLr37hDQz0}, Year = {2011}}

Abstract

Analysis and Recognition of motions and activities of objects in videos requires effective representations for analysis and matching of motion trajectories. In this paper, we introduce a new representation specifically aimed at matching motion trajectories. We model a trajectory as a continuous dense flow field from a sparse set of vector sequences using Gaussian Process Regression. Furthermore, we introduce a random sampling strategy for learning stable classes of motions from limited data.

Our representation allows for incrementally predicting possible paths and detecting anomalous events from online trajectories. This representation also supports matching of complex motions with acceleration changes and pauses or stops within a trajectory. We use the proposed approach for classifying and predicting motion trajectories in traffic monitoring domains and test on several data sets. We show that our approach works well on various types of complete and incomplete trajectories from a variety of video data sets with different frame rates

YouTube effects: Shake it like an Instagram picture

via YouTube effects: Shake it like an Instagram picture — Online Video News.

YouTube users can now apply a number of Instagram-like effects to their videos, giving them a cartoonish or Lomo-like look with the click of a button. The effects are part of a new editing feature that also includes cropping and advanced image stabilization.

Taking the shaking out of video uploads should go a long way towards making some of the amateur footage captured on mobile phones more watchable, but it can also be resource-intensive — which is why Google’s engineers invented an entirely new approach toward image stabilization.

The new editing functionality will be part of YouTube’s video page, where a new “Edit video” button will offer access to filters and other editing functionality. This type of post-processing is separate from YouTube’s video editor, which allows to produce new videos based on existing clips.

EAGER: Linguistic Task Transfer for Humans and Cyber Systems (Mike Stillman, Irfan Essa) NSF/RI

This project, investigating formal languages as a general methodology for task transfer between distinct cyber-physical systems such as humans and robots, aims to expand the science of cyber physical systems by developing Motion Grammars that will enable task transfer between distinct systems.

Formal languages are tools for encoding, describing and transferring structured knowledge. In natural language, the latter process is called communication. Similarly, we will develop a formal language through which arbitrary cyber-physical systems communicate tasks via structured actions. This investigation of Motion Grammars will contribute to the science of human cognition and the engineering of cyber-physical algorithms. By observing human activities during manipulation we will develop a novel class of hybrid control algorithms based on linguistic representations of task execution. These algorithms will broaden the capabilities of man-made systems and provide the infrastructure for motion transfer between humans, robots and broader systems in a generic context. Furthermore, the representation in a rigorous grammatical context will enable formal verification and validation in future work.
Broader Impacts: The proposed research has direct applications to new solutions for manufacturing, medical treatments such as surgery, logistics and food processing. In turn, each of these areas has a significant impact on the efficiency and convenience of our daily lives. The PIs serve as coordinators of graduate/undergraduate programs and mentors to community schools. In order to guarantee that women and minorities have a significant role in the research, the PIs will annually invite K-12 students from Atlanta schools with primarily African American populations to the laboratories. One-day robot classes will be conducted that engage students in the excitement of hands-on science by interactively using lab equipment to transfer their manipulation skills to a robot arm.

Via Award#1146352 – EAGER: Linguistic Task Transfer for Humans and Cyber Systems.

via Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths – Google Research Blog.

Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths
Posted by Matthias Grundmann, Vivek Kwatra, and Irfan Essa,

Earlier this year, we announced the launch of new features on the YouTube Video Editor, including stabilization for shaky videos, with the ability to preview them in real-time. The core technology behind this feature is detailed in this paper, which will be presented at the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2011).

Casually shot videos captured by handheld or mobile cameras suffer from significant amount of shake. Existing in-camera stabilization methods dampen high-frequency jitter but do not suppress low-frequency movements and bounces, such as those observed in videos captured by a walking person. On the other hand, most professionally shot videos usually consist of carefully designed camera configurations, using specialized equipment such as tripods or camera dollies, and employ ease-in and ease-out for transitions. Our goal was to devise a completely automatic method for converting casual shaky footage into more pleasant and professional looking videos.

Our technique mimics the cinematographic principles outlined above by automatically determining the best camera path using a robust optimization technique. The original, shaky camera path is divided into a set of segments, each approximated by either a constant, linear or parabolic motion. Our optimization finds the best of all possible partitions using a computationally efficient and stable algorithm.

To achieve real-time performance on the web, we distribute the computation across multiple machines in the cloud. This enables us to provide users with a real-time preview and interactive control of the stabilized result. Above we provide a video demonstration of how to use this feature on the YouTube Editor. We will also demo this live at Google’s exhibition booth in CVPR 2011.

For more details see the Project Site. See the youtube video of the system on youtube. See the paper in PDF, and a technical video of the work.

Full paper is

• Matthias Grundmann, Vivek Kwatra, and Irfan Essa (2011), “Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths,” In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2011), Colorado Springs, CO, USA. [PDF][Video][Blog][Demo][Project Site]