Recognizing locations with Google Glass: A case study
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Recognizing locations with Google Glass : A case study. / Altwaijry, Hani; Moghimi, Mohammad; Belongie, Serge.
I: 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, 2014, s. 167-174.Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
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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