Paper in MICCAI (2015): “Automated Assessment of Surgical Skills Using Frequency Analysis”

October 6th, 2015 Irfan Essa Posted in Activity Recognition, Aneeq Zia, Eric Sarin, Mark Clements, Medical, MICCAI, Papers, Vinay Bettadapura, Yachna Sharma No Comments »

Paper

  • A. Zia, Y. Sharma, V. Bettadapura, E. Sarin, M. Clements, and I. Essa (2015), “Automated Assessment of Surgical Skills Using Frequency Analysis,” in International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), 2015. [PDF] [BIBTEX]
    @InProceedings{    2015-Zia-AASSUFA,
      author  = {A. Zia and Y. Sharma and V. Bettadapura and E.
          Sarin and M. Clements and I. Essa},
      booktitle  = {International Conference on Medical Image Computing
          and Computer Assisted Interventions (MICCAI)},
      month    = {October},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Zia-AASSUFA.pdf}
          ,
      title    = {Automated Assessment of Surgical Skills Using
          Frequency Analysis},
      year    = {2015}
    }

Abstract

We present an automated framework for a visual assessment of the expertise level of surgeons using the OSATS (Objective Structured Assessment of Technical Skills) criteria. Video analysis technique for extracting motion quality via  frequency coefficients is introduced. The framework is tested in a case study that involved analysis of videos of medical students with different expertise levels performing basic surgical tasks in a surgical training lab setting. We demonstrate that transforming the sequential time data into frequency components effectively extracts the useful information differentiating between different skill levels of the surgeons. The results show significant performance improvements using DFT and DCT coefficients over known state-of-the-art techniques.

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Dagstuhl Workshop 2015: “Modeling and Simulation of Sport Games, Sport Movements, and Adaptations to Training”

September 13th, 2015 Irfan Essa Posted in Activity Recognition, Behavioral Imaging, Computer Vision, Human Factors, Modeling and Animation, Presentations No Comments »

Participated in the Dagstuhl Workshop on “Modeling and Simulation of Sport Games, Sport Movements, and Adaptations to Training” at the Dagstuhl Castle, September 13  – 16, 2015.

Motivation

Computational modeling and simulation are essential to analyze human motion and interaction in sports science. Applications range from game analysis, issues in training science like training load-adaptation relationship, motor control & learning, to biomechanical analysis. The motivation of this seminar is to enable an interdisciplinary exchange between sports and computer scientists to advance modeling and simulation technologies in selected fields of applications: sport games, sport movements and adaptations to training. In addition, contributions to the epistemic basics of modeling and simulation are welcome.

Source: Schloss Dagstuhl : Seminar Homepage

Past Seminars on this topic include

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Paper in Ubicomp 2015: “A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing”

September 8th, 2015 Irfan Essa Posted in ACM UIST/CHI, Activity Recognition, Behavioral Imaging, Edison Thomaz, Gregory Abowd, Health Systems, Machine Learning, Mobile Computing, Papers, UBICOMP, Ubiquitous Computing No Comments »

Paper

  • E. Thomaz, I. Essa, and G. D. Abowd (2015), “A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing,” in Proceedings of ACM International Conference on Ubiquitous Computing (UBICOMP), 2015. [PDF] [BIBTEX]
    @InProceedings{    2015-Thomaz-PAREMWWIS,
      author  = {Edison Thomaz and Irfan Essa and Gregory D. Abowd},
      booktitle  = {Proceedings of ACM International Conference on
          Ubiquitous Computing (UBICOMP)},
      month    = {September},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Thomaz-PAREMWWIS.pdf}
          ,
      title    = {A Practical Approach for Recognizing Eating Moments
          with Wrist-Mounted Inertial Sensing},
      year    = {2015}
    }

Abstract

Thomaz-UBICOMP15.pngRecognizing when eating activities take place is one of the key challenges in automated food intake monitoring. Despite progress over the years, most proposed approaches have been largely impractical for everyday usage, requiring multiple onbody sensors or specialized devices such as neck collars for swallow detection. In this paper, we describe the implementation and evaluation of an approach for inferring eating moments based on 3-axis accelerometry collected with a popular off-the-shelf smartwatch. Trained with data collected in a semi-controlled laboratory setting with 20 subjects, our system recognized eating moments in two free-living condition studies (7 participants, 1 day; 1 participant, 31 days), with Fscores of 76.1% (66.7% Precision, 88.8% Recall), and 71.3% (65.2% Precision, 78.6% Recall). This work represents a contribution towards the implementation of a practical, automated system for everyday food intake monitoring, with applicability in areas ranging from health research and food journaling.

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Paper in ISWC 2015: “Predicting Daily Activities from Egocentric Images Using Deep Learning”

September 7th, 2015 Irfan Essa Posted in Activity Recognition, Daniel Castro, Gregory Abowd, Henrik Christensen, ISWC, Machine Learning, Papers, Steven Hickson, Ubiquitous Computing, Vinay Bettadapura No Comments »

Paper

  • D. Castro, S. Hickson, V. Bettadapura, E. Thomaz, G. Abowd, H. Christensen, and I. Essa (2015), “Predicting Daily Activities from Egocentric Images Using Deep Learning,” in Proceedings of International Symposium on Wearable Computers (ISWC), 2015. [PDF] [WEBSITE] [arXiv] [BIBTEX]
    @InProceedings{    2015-Castro-PDAFEIUDL,
      arxiv    = {http://arxiv.org/abs/1510.01576},
      author  = {Daniel Castro and Steven Hickson and Vinay
          Bettadapura and Edison Thomaz and Gregory Abowd and
          Henrik Christensen and Irfan Essa},
      booktitle  = {Proceedings of International Symposium on Wearable
          Computers (ISWC)},
      month    = {September},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Castro-PDAFEIUDL.pdf}
          ,
      title    = {Predicting Daily Activities from Egocentric Images
          Using Deep Learning},
      url    = {http://www.cc.gatech.edu/cpl/projects/dailyactivities/}
          ,
      year    = {2015}
    }

Abstract

Castro-ISWC2015We present a method to analyze images taken from a passive egocentric wearable camera along with the contextual information, such as time and day of a week, to learn and predict everyday activities of an individual. We collected a dataset of 40,103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities. Classification is conducted using a Convolutional Neural Network (CNN) with a classification method we introduce called a late fusion ensemble. This late fusion ensemble incorporates relevant contextual information and increases our classification accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a person’s activity across the 19 activity classes. We also demonstrate some promising results from two additional users by fine-tuning the classifier with one day of training data.

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Paper in ACM IUI15: “Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study”

April 1st, 2015 Irfan Essa Posted in ACM ICMI/IUI, Activity Recognition, Audio Analysis, Behavioral Imaging, Edison Thomaz, Gregory Abowd, Health Systems, Machine Learning, Multimedia No Comments »

Paper

  • E. Thomaz, C. Zhang, I. Essa, and G. D. Abowd (2015), “Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study,” in Proceedings of ACM Conference on Intelligence User Interfaces (IUI), 2015. (Best Short Paper Award) [PDF] [BIBTEX]
    @InProceedings{    2015-Thomaz-IMEARWSFASFS,
      author  = {Edison Thomaz and Cheng Zhang and Irfan Essa and
          Gregory D. Abowd},
      awards  = {(Best Short Paper Award)},
      booktitle  = {Proceedings of ACM Conference on Intelligence User
          Interfaces (IUI)},
      month    = {May},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Thomaz-IMEARWSFASFS.pdf}
          ,
      title    = {Inferring Meal Eating Activities in Real World
          Settings from Ambient Sounds: A Feasibility Study},
      year    = {2015}
    }

Abstract

2015-04-IUI-AwardDietary self-monitoring has been shown to be an effective method for weight-loss, but it remains an onerous task despite recent advances in food journaling systems. Semi-automated food journaling can reduce the effort of logging, but often requires that eating activities be detected automatically. In this work we describe results from a feasibility study conducted in-the-wild where eating activities were inferred from ambient sounds captured with a wrist-mounted device; twenty participants wore the device during one day for an average of 5 hours while performing normal everyday activities. Our system was able to identify meal eating with an F-score of 79.8% in a person-dependent evaluation, and with 86.6% accuracy in a person-independent evaluation. Our approach is intended to be practical, leveraging off-the-shelf devices with audio sensing capabilities in contrast to systems for automated dietary assessment based on specialized sensors.

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Paper in IEEE WACV (2015): “Leveraging Context to Support Automated Food Recognition in Restaurants”

January 6th, 2015 Irfan Essa Posted in Activity Recognition, Computer Vision, Edison Thomaz, First Person Computing, Gregory Abowd, Mobile Computing, PAMI/ICCV/CVPR/ECCV, Papers, Ubiquitous Computing, Uncategorized, Vinay Bettadapura No Comments »

Paper

  • V. Bettadapura, E. Thomaz, A. Parnami, G. Abowd, and I. Essa (2015), “Leveraging Context to Support Automated Food Recognition in Restaurants,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. [PDF] [WEBSITE] [DOI] [arXiv] [BIBTEX]
    @InProceedings{    2015-Bettadapura-LCSAFRR,
      arxiv    = {http://arxiv.org/abs/1510.02078},
      author  = {Vinay Bettadapura and Edison Thomaz and Aman
          Parnami and Gregory Abowd and Irfan Essa},
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      doi    = {10.1109/WACV.2015.83},
      month    = {January},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Bettadapura-LCSAFRR.pdf}
          ,
      publisher  = {IEEE Computer Society},
      title    = {Leveraging Context to Support Automated Food
          Recognition in Restaurants},
      url    = {http://www.vbettadapura.com/egocentric/food/},
      year    = {2015}
    }

 

Abstract

The pervasiveness of mobile cameras has resulted in a dramatic increase in food photos, which are pictures reflecting what people eat. In this paper, we study how taking pictures of what we eat in restaurants can be used for the purpose of automating food journaling. We propose to leverage the context of where the picture was taken, with additional information about the restaurant, available online, coupled with state-of-the-art computer vision techniques to recognize the food being consumed. To this end, we demonstrate image-based recognition of foods eaten in restaurants by training a classifier with images from restaurant’s online menu databases. We evaluate the performance of our system in unconstrained, real-world settings with food images taken in 10 restaurants across 5 different types of food (American, Indian, Italian, Mexican and Thai).food-poster

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Paper in WACV (2015): “Egocentric Field-of-View Localization Using First-Person Point-of-View Devices”

January 6th, 2015 Irfan Essa Posted in Activity Recognition, Caroline Pantofaru, Computer Vision, First Person Computing, Mobile Computing, PAMI/ICCV/CVPR/ECCV, Papers, Vinay Bettadapura No Comments »

Paper

  • V. Bettadapura, I. Essa, and C. Pantofaru (2015), “Egocentric Field-of-View Localization Using First-Person Point-of-View Devices,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. (Best Paper Award) [PDF] [WEBSITE] [DOI] [arXiv] [BIBTEX]
    @InProceedings{    2015-Bettadapura-EFLUFPD,
      arxiv    = {http://arxiv.org/abs/1510.02073},
      author  = {Vinay Bettadapura and Irfan Essa and Caroline
          Pantofaru},
      awards  = {(Best Paper Award)},
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      doi    = {10.1109/WACV.2015.89},
      month    = {January},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Bettadapura-EFLUFPD.pdf}
          ,
      publisher  = {IEEE Computer Society},
      title    = {Egocentric Field-of-View Localization Using
          First-Person Point-of-View Devices},
      url    = {http://www.vbettadapura.com/egocentric/localization/}
          ,
      year    = {2015}
    }

Abstract

We present a technique that uses images, videos and sensor data taken from first-person point-of-view devices to perform egocentric field-of-view (FOV) localization. We define egocentric FOV localization as capturing the visual information from a person’s field-of-view in a given environment and transferring this information onto a reference corpus of images and videos of the same space, hence determining what a person is attending to. Our method matches images and video taken from the first-person perspective with the reference corpus and refines the results using the first-person’s head orientation information obtained using the device sensors. We demonstrate single and multi-user egocentric FOV localization in different indoor and outdoor environments with applications in augmented reality, event understanding and studying social interactions.

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Paper in M2CAI 2014: “Video Based Assessment of OSATS Using Sequential Motion Textures”

September 14th, 2014 Irfan Essa Posted in Activity Recognition, Behavioral Imaging, Computer Vision, Medical, MICCAI, Papers, Thomas Ploetz, Vinay Bettadapura, Yachna Sharma No Comments »

Paper

  • Y. Sharma, V. Bettadapura, T. Ploetz, N. Hammerla, S. Mellor, R. McNaney, P. Olivier, S. Deshmukh, A. Mccaskie, and I. Essa (2014), “Video Based Assessment of OSATS Using Sequential Motion Textures,” in Proceedings of Workshop on Modeling and Monitoring of Computer Assisted Interventions (M2CAI), 2014. (Best Paper Honorable Mention Award) [PDF] [BIBTEX]
    @InProceedings{    2014-Sharma-VBAOUSMT,
      author  = {Yachna Sharma and Vinay Bettadapura and Thomas
          Ploetz and Nils Hammerla and Sebastian Mellor and
          Roisin McNaney and Patrick Olivier and Sandeep
          Deshmukh and Andrew Mccaskie and Irfan Essa},
      awards  = {(Best Paper Honorable Mention Award)},
      booktitle  = {{Proceedings of Workshop on Modeling and Monitoring
          of Computer Assisted Interventions (M2CAI)}},
      month    = {September},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2014-Sharma-VBAOUSMT.pdf}
          ,
      title    = {Video Based Assessment of OSATS Using Sequential
          Motion Textures},
      year    = {2014}
    }

Abstract

2014-Sharma-VBAOUSMTA fully automated framework for video-based surgical skill assessment is presented that incorporates the sequential and qualitative aspects of surgical motion in a data-driven manner. The Objective Structured Assessment of Technical Skills (OSATS) assessments is replicated, which provides both an overall and in-detail evaluation of basic suturing skills required for surgeons. Video analysis techniques are introduced that incorporate sequential motion aspects into motion textures. Significant performance improvement over standard bag-of-words and motion analysis approaches is demonstrated. The framework is evaluated in a case study that involved medical students with varying levels of expertise performing basic surgical tasks in a surgical training lab setting.

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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},
      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-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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