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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