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Two Ph. D. Defenses the same day. A first for me!

April 2nd, 2014 Irfan Essa Posted in Activity Recognition, Computational Photography and Video, Health Systems, PhD, S. Hussain Raza, Students, Yachna Sharma No Comments »

Today, two of my Ph. D. Students defended their Dissertations.  Back to back.  Congrats to both as they are both done.

Thesis title: Surgical Skill Assessment Using Motion Texture analysis
Student: Yachna Sharma, Ph. D. Candidate in ECE
http://users.ece.gatech.edu/~ysharma3/
Date/Time : 2nd April, 1:00 pm

Title : Temporally Consistent Semantic Segmentation in Videos
S. Hussain Raza, Ph. D. Candidate in ECE
https://sites.google.com/site/shussainraza5/
Date/Time : 2nd April, 1:00 pm

Location : CSIP Library, Room 5186, CenterGy One Building

 

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Paper in CVIU 2013 “A Visualization Framework for Team Sports Captured using Multiple Static Cameras”

October 3rd, 2013 Irfan Essa Posted in Activity Recognition, Computational Photography and Video, Jessica Hodgins, PAMI/ICCV/CVPR/ECCV, Papers, Raffay Hamid, Sports Visualization No Comments »

  • R. Hamid, R. Kumar, J. Hodgins, and I. Essa (2013), “A Visualization Framework for Team Sports Captured using Multiple Static Cameras,” Computer Vision and Image Understanding, p. -, 2013. [PDF] [WEBSITE] [VIDEO] [DOI] [BIBTEX]
    @article{2013-Hamid-VFTSCUMSC,
      Author = {Raffay Hamid and Ramkrishan Kumar and Jessica Hodgins and Irfan Essa},
      Date-Added = {2013-10-22 13:42:46 +0000},
      Date-Modified = {2013-10-22 13:51:43 +0000},
      Doi = {10.1016/j.cviu.2013.09.006},
      Issn = {1077-3142},
      Journal = {Computer Vision and Image Understanding},
      Number = {0},
      Pages = {-},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2013-Hamid-VFTSCUMSC.pdf},
      Title = {A Visualization Framework for Team Sports Captured using Multiple Static Cameras},
      Url = {http://raffayhamid.com/sports_viz.shtml},
      Video = {http://www.youtube.com/watch?v=VwzAMi9pUDQ},
      Year = {2013},
      Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S1077314213001768},
      Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.cviu.2013.09.006},
      Bdsk-Url-3 = {http://raffayhamid.com/sports_viz.shtml}}

Abstract

We present a novel approach for robust localization of multiple people observed using a set of static cameras. We use this location information to generate a visualization of the virtual offside line in soccer games. To compute the position of the offside line, we need to localize players′ positions, and identify their team roles. We solve the problem of fusing corresponding players′ positional information by finding minimum weight K-length cycles in a complete K-partite graph. Each partite of the graph corresponds to one of the K cameras, whereas each node of a partite encodes the position and appearance of a player observed from a particular camera. To find the minimum weight cycles in this graph, we use a dynamic programming based approach that varies over a continuum from maximally to minimally greedy in terms of the number of graph-paths explored at each iteration. We present proofs for the efficiency and performance bounds of our algorithms. Finally, we demonstrate the robustness of our framework by testing it on 82,000 frames of soccer footage captured over eight different illumination conditions, play types, and team attire. Our framework runs in near-real time, and processes video from 3 full HD cameras in about 0.4 seconds for each set of corresponding 3 frames.

via Science Direct A Visualization Framework for Team Sports Captured using Multiple Static Cameras.

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Paper in ACM Ubicomp 2013 “Technological approaches for addressing privacy concerns when recognizing eating behaviors with wearable cameras”

September 14th, 2013 Irfan Essa Posted in Activity Recognition, Computational Photography and Video, Edison Thomaz, Gregory Abowd, ISWC, Mobile Computing, Papers, Ubiquitous Computing No Comments »

  • E. Thomaz, A. Parnami, J. Bidwell, I. Essa, and G. D. Abowd (2013), “Technological Approaches for Addressing Privacy Concerns when Recognizing Eating Behaviors with Wearable Cameras.,” in Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’13), 2013. [PDF] [DOI] [BIBTEX]
    @inproceedings{2013-Thomaz-TAAPCWREBWWC,
      Author = {Edison Thomaz and Aman Parnami and Jonathan Bidwell and Irfan Essa and Gregory D. Abowd},
      Booktitle = {Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '13)},
      Date-Added = {2013-10-22 18:31:23 +0000},
      Date-Modified = {2013-10-22 19:19:14 +0000},
      Doi = {10.1145/2493432.2493509},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2013-Thomaz-TAAPCWREBWWC.pdf},
      Title = {Technological Approaches for Addressing Privacy Concerns when Recognizing Eating Behaviors with Wearable Cameras.},
      Year = {2013},
      Bdsk-Url-1 = {http://dx.doi.org/10.1145/2493432.2493509}}

 Abstract

First-person point-of-view (FPPOV) images taken by wearable cameras can be used to better understand people’s eating habits. Human computation is a way to provide effective analysis of FPPOV images in cases where algorithmic approaches currently fail. However, privacy is a serious concern. We provide a framework, the privacy-saliency matrix, for understanding the balance between the eating information in an image and its potential privacy concerns. Using data gathered by 5 participants wearing a lanyard-mounted smartphone, we show how the framework can be used to quantitatively assess the effectiveness of four automated techniques (face detection, image cropping, location filtering and motion filtering) at reducing the privacy-infringing content of images while still maintaining evidence of eating behaviors throughout the day.

via ACM DL Technological approaches for addressing privacy concerns when recognizing eating behaviors with wearable cameras.

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At ICVSS (International Computer Vision Summer School) 2013, in Calabria, ITALY (July 2013)

July 11th, 2013 Irfan Essa Posted in Computational Photography, Computational Photography and Video, Daniel Castro, Matthias Grundmann, Presentations, S. Hussain Raza, Vivek Kwatra No Comments »

Teaching at the ICVSS 2013, in Calabria, Italy, July 2013 (Programme)

Computational Video: Post-processing Methods for Stabilization, Retargeting and Segmentation

Irfan Essa
(This work in collaboration with
Matthias Grundmann, Daniel Castro, Vivek Kwatra, Mei Han, S. Hussian Raza).

Abstract

We address a variety of challenges for analysis and enhancement of Computational Video. We present novel post-processing methods to bridge the difference between professional and casually shot videos mostly seen on online sites. Our research presents solutions to three well-defined problems: (1) Video stabilization and rolling shutter removal in casually-shot, uncalibrated videos; (2) Content-aware video retargeting; and (3) spatio-temporal video segmentation to enable efficient video annotation. We showcase several real-world applications building on these techniques.

We start by proposing a novel algorithm for video stabilization that generates stabilized videos by employing L1-optimal camera paths to remove undesirable motions. We compute camera paths that are optimally partitioned into con- stant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To achieve this, we propose a linear program- ming framework to minimize the first, second, and third derivatives of the result- ing camera path. Our method allows for video stabilization beyond conventional filtering, that only suppresses high frequency jitter. An additional challenge in videos shot from mobile phones are rolling shutter distortions. Modern CMOS cameras capture the frame one scanline at a time, which results in non-rigid image distortions such as shear and wobble. We propose a solution based on a novel mixture model of homographies parametrized by scanline blocks to correct these rolling shutter distortions. Our method does not rely on a-priori knowl- edge of the readout time nor requires prior camera calibration. Our novel video stabilization and calibration free rolling shutter removal have been deployed on YouTube where they have successfully stabilized millions of videos. We also discuss several extensions to the stabilization algorithm and present technical details behind the widely used YouTube Video Stabilizer.

We address the challenge of changing the aspect ratio of videos, by proposing algorithms that retarget videos to fit the form factor of a given device without stretching or letter-boxing. Our approaches use all of the screens pixels, while striving to deliver as much video-content of the original as possible. First, we introduce a new algorithm that uses discontinuous seam-carving in both space and time for resizing videos. Our algorithm relies on a novel appearance-based temporal coherence formulation that allows for frame-by-frame processing and results in temporally discontinuous seams, as opposed to geometrically smooth and continuous seams. Second, we present a technique, that builds on the above mentioned video stabilization approach. We effectively automate classical pan and scan techniques by smoothly guiding a virtual crop window via saliency constraints.

Finally, we introduce an efficient and scalable technique for spatio-temporal segmentation of long video sequences using a hierarchical graph-based algorithm. We begin by over-segmenting a volumetric video graph into space-time regions grouped by appearance. We then construct a region graph over the ob- tained segmentation and iteratively repeat this process over multiple levels to create a tree of spatio-temporal segmentations. This hierarchical approach gen- erates high quality segmentations, and allows subsequent applications to choose from varying levels of granularity. We demonstrate the use of spatio-temporal segmentation as users interact with the video, enabling efficient annotation of objects within the video.

Part of this talks will will expose attendees to use the Video Stabilizer on YouTube and the video segmentation system at videosegmentation.com. Please find appropriate videos to test the systems.

Part of the work described above was done at Google, where Matthias Grundmann, Vivek Kwatra and Mei Han are, and Professor Essa is working as a Consultant. Part of the work were efforts of research by Matthias Grundmann, Daniel Castro and S. Hussain Raza, as part of their research efforts as students at GA Tech.

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Google I/O 2013: Secrets of Video Stabilization on YouTube

May 28th, 2013 Irfan Essa Posted in Computational Photography and Video, Google, In The News, Matthias Grundmann, Presentations, Vivek Kwatra No Comments »

Presentation at Google I/0 2013 by Matthias Grundmann, John Gregg, and Vivek Kwatra on our Video Stabilizer on YouTube

Video stabilization is a key component of YouTubes video enhancement tools and youtube.com/editor. All YouTube uploads are automatically detected for shakiness and suggested stabilization if needed. This talk will describe the technical details behind our fully automatic one-click stabilization technology, including aspects such as camera path optimization, rolling shutter detection and removal, distributed computing for real-time previews, and camera shake detection. More info: http://googleresearch.blogspot.com/2012/05/video-stabilization-on-youtube.html

via Secrets of Video Stabilization on YouTube — Google I/O 2013.

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Computational Photography MOOC on Coursera, comes to a close.

May 7th, 2013 Irfan Essa Posted in Computational Photography, Computational Photography and Video, Coursera, Denis Lantsman No Comments »

The Computational Photography MOOC offering in Coursera came to a close with the following final announcement (abridged here) on May 7, 2013.

Computational photographers:

Thanks for joining us for an engaging 5 weeks of collaboratively learning the wonderful aspects of computational photography. We bid you all farewell now and hope to see some of you in a future reincarnation of this class, building on the feedback provided by many of you. Keep a lookout for the repeat of the same class, and for another class continuing to more advanced topics.

Final graded scores, and the the certificate of completion will be made available this week. All assignment solutions are available, as requested. We will also keep the class site open for a while.

Do remember that we still welcome your feedback, so use the forums. If you haven’t done so already, please do spend a few minutes to fill out the survey for the last week of class, which is part of a survey we are conducting to understand and evaluate online classes offerings like this one.

Again, thanks for participating, and good luck with your future endeavors. And remember to take good pictures, and to have fun computing with photographs.

via Announcements | Computational Photography.

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Paper in ICCP 2013 “Post-processing approach for radiometric self-calibration of video”

April 19th, 2013 Irfan Essa Posted in Computational Photography and Video, ICCP, Matthias Grundmann, Papers, Sing Bing Kang No Comments »

  • M. Grundmann, C. McClanahan, S. B. Kang, and I. Essa (2013), “Post-processing Approach for Radiometric Self-Calibration of Video,” in Proceedings of IEEE International Conference on Computational Photography, 2013. [PDF] [WEBSITE] [VIDEO] [DOI] [BIBTEX]
    @inproceedings{2013-Grundmann-PARSV,
      Author = {Matthias Grundmann and Chris McClanahan and Sing Bing Kang and Irfan Essa},
      Booktitle = {Proceedings of IEEE International Conference on Computational Photography},
      Date-Added = {2013-06-25 11:54:57 +0000},
      Date-Modified = {2013-10-22 18:41:09 +0000},
      Doi = {10.1109/ICCPhot.2013.6528307},
      Month = {April},
      Organization = {IEEE Computer Society},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2013-Grundmann-PARSV.pdf},
      Title = {Post-processing Approach for Radiometric Self-Calibration of Video},
      Url = {http://www.cc.gatech.edu/cpl/projects/radiometric},
      Video = {http://www.youtube.com/watch?v=sC942ZB4WuM},
      Year = {2013},
      Bdsk-Url-1 = {http://www.cc.gatech.edu/cpl/projects/radiometric},
      Bdsk-Url-2 = {http://dx.doi.org/10.1109/ICCPhot.2013.6528307}}

Abstract

We present a novel data-driven technique for radiometric self-calibration of video from an unknown camera. Our approach self-calibrates radiometric variations in video, and is applied as a post-process; there is no need to access the camera, and in particular it is applicable to internet videos. This technique builds on empirical evidence that in video the camera response function (CRF) should be regarded time variant, as it changes with scene content and exposure, instead of relying on a single camera response function. We show that a time-varying mixture of responses produces better accuracy and consistently reduces the error in mapping intensity to irradiance when compared to a single response model. Furthermore, our mixture model counteracts the effects of possible nonlinear exposure-dependent intensity perturbations and white-balance changes caused by proprietary camera firmware. We further show how radiometrically calibrated video improves the performance of other video analysis algorithms, enabling a video segmentation algorithm to be invariant to exposure and gain variations over the sequence. We validate our data-driven technique on videos from a variety of cameras and demonstrate the generality of our approach by applying it to internet video.

via IEEE Xplore – Post-processing approach for radiometric self-calibration of video.

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Matthias Grundmann’s PhD Thesis Defense (2013): “Title: Computational Video: Post-processing Methods for Stabilization, Retargeting and Segmentation”

February 4th, 2013 Irfan Essa Posted in Computational Photography and Video, Matthias Grundmann, PhD No Comments »

Title: Computational Video: Post-processing Methods for Stabilization, Retargeting and Segmentation

Matthias Grundmann
School of Interactive Computing
College of Computing
Georgia Institute of Technology

Date: February 04, 2013 (Monday)
Time: 3:00p – 6:00p EST
Location: Nano building, 116-118

Abstract:

M+I

In this thesis, we address a variety of challenges for analysis and enhancement of Computational Video. We present novel post-processing methods to bridge the difference between professional and casually shot videos mostly seen on online sites. Our research presents solutions to three well-defined problems: (1) Video stabilization and rolling shutter removal in casually-shot, uncalibrated videos; (2) Content-aware video retargeting; and (3) spatio-temporal video segmentation to enable efficient video annotation. We showcase several real-world applications building on these techniques.

We start by proposing a novel algorithm for video stabilization that generates stabilized videos by employing L1-optimal camera paths to remove undesirable motions. We compute camera paths that are optimally partitioned into constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To achieve this, we propose a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond conventional filtering, that only suppresses high frequency jitter. An additional challenge in videos shot from mobile phones are rolling shutter distortions. Modern CMOS cameras capture the frame one scanline at a time, which results in non-rigid image distortions such as shear and wobble. We propose a solution based on a novel mixture model of homographies parametrized by scanline blocks to correct these rolling shutter distortions. Our method does not rely on a-priori knowledge of the readout time nor requires prior camera calibration. Our novel video stabilization and calibration free rolling shutter removal have been deployed on YouTube where they have successfully stabilized millions of videos. We also discuss several extensions to the stabilization algorithm and present technical details behind the widely used YouTube Video Stabilizer.

We address the challenge of changing the aspect ratio of videos, by proposing algorithms that retarget videos to fit the form factor of a given device without stretching or letter-boxing. Our approaches use all of the screen’s pixels, while striving to deliver as much video-content of the original as possible. First, we introduce a new algorithm that uses discontinuous seam-carving in both space and time for resizing videos. Our algorithm relies on a novel appearance-based temporal coherence formulation that allows for frame-by-frame processing and results in temporally discontinuous seams, as opposed to geometrically smooth and continuous seams. Second, we present a technique, that builds on the above mentioned video stabilization approach. We effectively automate classical pan and scan techniques by smoothly guiding a virtual crop window via saliency constraints.

Finally, we introduce an efficient and scalable technique for spatio-temporal segmentation of long video sequences using a hierarchical graph-based algorithm. We begin by over-segmenting a volumetric video graph into space-time regions grouped by appearance. We then construct a “region graph” over the obtained  segmentation and iteratively repeat this process over multiple levels to create a tree of spatio-temporal segmentations. This hierarchical approach generates high quality segmentations, and allows subsequent applications to choose from varying levels of granularity. We demonstrate the use of spatio-temporal segmentation as users interact with the video, enabling efficient annotation of objects within the video.

Committee:

  • Dr. Irfan Essa (Advisor, School of Interactive Computing, Georgia Tech)
  • Dr. Jim Rehg (School of Interactive Computing, Georgia Tech)
  • Dr. Frank Dellaert (School of Interactive Computing, Georgia Tech)
  • Dr. Michael Black (Perceiving Systems Department, Max Planck Institute for Intelligent Systems)
  • Dr. Sing Bing Kang (Adjunct Faculty, Georgia Tech; Microsoft Research, Microsoft Corp.)
  • Dr. Vivek Kwatra (Google Research, Google Inc.)
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Presentation (2012): CMU Robotics Institute Seminar

October 19th, 2012 Irfan Essa Posted in Computational Photography and Video, Matthias Grundmann, Presentations, Vivek Kwatra No Comments »

Video Analysis and Enhancement: Video Stabilization and Rolling Shutter Removal on YouTube

Irfan Essa
Georgia Tech
School of Interactive Computing
GVU and RIM @ GT Centers

October 19, 2012, 3:30 PM, NSH 1305

Abstract

In this talk, I will discuss a variety of approaches my group is working on for video analysis and enhancement. In particular, I will describe our approach for a video stabilizer, currently implemented and running on YouTube, and its extensions.

This method generates stabilized videos by employing L1-optimal camera paths to remove undesirable motions [1]. We compute camera paths that are optimally partitioned into constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. We propose a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering that only suppresses high frequency jitter. An additional challenge in videos shot from mobile phones are rolling shutter distortions. Modern CMOS cameras capture the frame one scan-line at a time, which results in non-rigid image distortions such as shear and wobble. I will demonstrate a solution based on a novel mixture model of homographies parametrized by scan-line blocks to correct these rolling shutter distortions [2]. Our method does not rely on a-priori knowledge of the readout time nor requires prior camera calibration. A thorough evaluation based on a user study and direct comparisons to other approaches, demonstrates a general preference for our algorithm.

I will conclude the talk by showcasing a live demo of the stabilizer. This work is in collaboration with Matthias Grundmann and Vivek Kwatra at Google, and appears in following two papers.

Time permitting, I will discuss some other projects we are working on, including video segmentation and retargetting.

[1] Matthias Grundmann, Vivek Kwatra, Irfan Essa, CVPR 2011, www.cc.gatech.edu/cpl/projects/videostabilization

[2] Matthias Grundmann, Vivek Kwatra, Daniel Castro Irfan Essa, ICCP 2012, Best paper, www.cc.gatech.edu/cpl/projects/rollingshutter

Host: Takeo Kanade

via Robotics Institute: Talks and Seminars.

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Presentation (2012): Distinguished Seminar Series in Computer Science at the Imperial College, London

October 4th, 2012 Irfan Essa Posted in Computational Photography and Video, Matthias Grundmann, Presentations, Vivek Kwatra No Comments »

Video Enhancement and Analysis: From Content Analysis to Video Stabilization for YouTube

Irfan Essa
Georgia Institute of Technology

October 14, 2012 15:00 – 16:00, Huxely Room. South Kensington Campus, Imperial College, London

Abstract

The talk will describe a variety of efforts undertaken on analysis of  video to enhancement and synthesis of video. An overview of the past work on representing and analyzing videos as a stochastic process and use of this in a form of Video Textures will be provided.  Majority of the talk will then focus on the recent effort which resulted in a widely-used video stabilizer currently implemented on YouTube and its extensions. This method generates stabilized videos by employing L1-optimal camera paths to remove undesirable motions. We compute camera paths that are optimally partitioned into constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To this end, we propose a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering that only suppresses high frequency jitter. An additional challenge in videos shot from mobile phones are rolling shutter distortions.  We demonstrate a solution based on a novel mixture model of homographies parametrized by scanline blocks to correct these rolling shutter distortions. Our method does not rely on a-priori knowledge of the readout time nor requires prior camera calibration.  This work is in collaboration with Matthias Grundmann and Vivek Kwatra at Google.

Via Distinguished Seminar Series in Computer Science Irfan Essa – GA Tech.

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