Welcome to my website (prof.irfanessa.com). Here you will find information related to my academic pursuits. This includes updates on my research projects, list of publications, classes I teach and my collaborators/students. If you'd like to contact me, I suggest please see the FAQ. Students wanted to contact me about working with me are highly encouraged to read the FAQ. My bio is also available. Use the menu bar above, or the TAGS and CATEGORIES listed in the columns to find relevant information.
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).
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.
