Recognizing locations with Google Glass: A case study

Research output: Contribution to journal › Conference article › Research › peer-review

Standard

Recognizing locations with Google Glass : A case study. / Altwaijry, Hani; Moghimi, Mohammad; Belongie, Serge.

In: 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, 2014, p. 167-174.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Altwaijry, H, Moghimi, M & Belongie, S 2014, 'Recognizing locations with Google Glass: A case study', 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, pp. 167-174. https://doi.org/10.1109/WACV.2014.6836105

APA

Altwaijry, H., Moghimi, M., & Belongie, S. (2014). Recognizing locations with Google Glass: A case study. 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, 167-174. https://doi.org/10.1109/WACV.2014.6836105

Vancouver

Altwaijry H, Moghimi M, Belongie S. Recognizing locations with Google Glass: A case study. 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014. 2014;167-174. https://doi.org/10.1109/WACV.2014.6836105

Author

Altwaijry, Hani ; Moghimi, Mohammad ; Belongie, Serge. / Recognizing locations with Google Glass : A case study. In: 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014. 2014 ; pp. 167-174.

Bibtex

@inproceedings{750e1e0eae2c4ca18d55b725f8904fe4,
title = "Recognizing locations with Google Glass: A case study",
abstract = "Wearable computers are rapidly gaining popularity as more people incorporate them into their everyday lives. The introduction of these devices allows for wider deployment of Computer Vision based applications. In this paper, we describe a system developed to deliver users of wearable computers a tour guide experience. In building our system, we compare and contrast different techniques towards achieving our goals. Those techniques include using various descriptor types, such as HOG, SIFT and SURF, under different encoding models, such as holistic approaches, Bag-of-Words, and Fisher Vectors. We evaluate those approaches using classification methods including Nearest Neighbor and Support Vector Machines. We also show how to incorporate information external to images, specifically GPS, to improve the user experience.",
author = "Hani Altwaijry and Mohammad Moghimi and Serge Belongie",
year = "2014",
doi = "10.1109/WACV.2014.6836105",
language = "English",
pages = "167--174",
journal = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014",
note = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 ; Conference date: 24-03-2014 Through 26-03-2014",

}

RIS

TY - GEN

T1 - Recognizing locations with Google Glass

T2 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

AU - Altwaijry, Hani

AU - Moghimi, Mohammad

AU - Belongie, Serge

PY - 2014

Y1 - 2014

N2 - Wearable computers are rapidly gaining popularity as more people incorporate them into their everyday lives. The introduction of these devices allows for wider deployment of Computer Vision based applications. In this paper, we describe a system developed to deliver users of wearable computers a tour guide experience. In building our system, we compare and contrast different techniques towards achieving our goals. Those techniques include using various descriptor types, such as HOG, SIFT and SURF, under different encoding models, such as holistic approaches, Bag-of-Words, and Fisher Vectors. We evaluate those approaches using classification methods including Nearest Neighbor and Support Vector Machines. We also show how to incorporate information external to images, specifically GPS, to improve the user experience.

AB - Wearable computers are rapidly gaining popularity as more people incorporate them into their everyday lives. The introduction of these devices allows for wider deployment of Computer Vision based applications. In this paper, we describe a system developed to deliver users of wearable computers a tour guide experience. In building our system, we compare and contrast different techniques towards achieving our goals. Those techniques include using various descriptor types, such as HOG, SIFT and SURF, under different encoding models, such as holistic approaches, Bag-of-Words, and Fisher Vectors. We evaluate those approaches using classification methods including Nearest Neighbor and Support Vector Machines. We also show how to incorporate information external to images, specifically GPS, to improve the user experience.

UR - http://www.scopus.com/inward/record.url?scp=84904671393&partnerID=8YFLogxK

U2 - 10.1109/WACV.2014.6836105

DO - 10.1109/WACV.2014.6836105

M3 - Conference article

AN - SCOPUS:84904671393

SP - 167

EP - 174

JO - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

JF - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Y2 - 24 March 2014 through 26 March 2014

ER -

ID: 302164418