| Outdoors augmented reality on mobile phone using loxel-based visual feature organization |
| Full text |
Pdf
(5.62 MB)
|
Source
|
International Multimedia Conference
archive
Proceeding of the 1st ACM international conference on Multimedia information retrieval
table of contents
Vancouver, British Columbia, Canada
SESSION: 3D Object retrieval
table of contents
Pages 427-434
Year of Publication: 2008
ISBN:978-1-60558-312-9
|
|
Authors
|
|
Gabriel Takacs
|
Stanford University, Stanford, CA, USA
|
|
Vijay Chandrasekhar
|
Stanford University, Stanford, CA, USA
|
|
Natasha Gelfand
|
Nokia Research Center, Palo Alto, CA, USA
|
|
Yingen Xiong
|
Nokia Research Center, Palo Alto, CA, USA
|
|
Wei-Chao Chen
|
Nokia Research Center, Palo Alto, CA, USA
|
|
Thanos Bismpigiannis
|
Stanford University, Stanford, CA, USA
|
|
Radek Grzeszczuk
|
Nokia Research Center, Palo Alto, CA, USA
|
|
Kari Pulli
|
Nokia Research Center, Palo Alto, CA, USA
|
|
Bernd Girod
|
Stanford University, Stanford, CA, USA
|
|
| Sponsors |
|
| Publisher |
|
| Bibliometrics |
Downloads (6 Weeks): 63, Downloads (12 Months): 353, Citation Count: 2
|
|
|
ABSTRACT
We have built an outdoors augmented reality system for mobile phones that matches camera-phone images against a large database of location-tagged images using a robust image retrieval algorithm. We avoid network latency by implementing the algorithm on the phone and deliver excellent performance by adapting a state-of-the-art image retrieval algorithm based on robust local descriptors. Matching is performed against a database of highly relevant features, which is continuously updated to reflect changes in the environment. We achieve fast updates and scalability by pruning of irrelevant features based on proximity to the user. By compressing and incrementally updating the features stored on the phone we make the system amenable to low-bandwidth wireless connections. We demonstrate system robustness on a dataset of location-tagged images and show a smart-phone implementation that achieves a high image matching rate while operating in near real-time.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
 |
1
|
|
| |
2
|
R. Fergus, P. Perona, and A. Zisserman, "Object class recognition by unsupervised scale-invariant learning," in Proc. of Conference on Computer Vision and Pattern Recognition (CVPR), vol. 02. IEEE Computer Society, 2003, p. 264.
|
| |
3
|
A. Ferencz, E. Learned-Miller, and J. Malik, "Learning Hyper-Features for Visual Identification," in Neural Information Processing Systems, vol. 18, 2004.
|
| |
4
|
|
| |
5
|
Y. Zhou, X. Fan, X. Xie, Y. Gong, and W.-Y. Ma, "Inquiring of the Sights from the Web via Camera Mobiles," ph2006 IEEE International Conference on Multimedia and Expo, pp. 661--664, July 2006.
|
| |
6
|
|
| |
7
|
H. Bay, T. Tuytelaars, and L. V. Gool, "SURF: Speeded Up Robust Features," in ECCV (1), 2006, pp. 404--417.
|
| |
8
|
J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, "Object Retrieval with Large Vocabularies and Fast Spatial Matching," in Proc. of Conference on Computer Vision and Pattern Recognition (CVPR), 2007.
|
| |
9
|
K. Greene, "Hyperlinking Reality via Phones," MIT Technology Review, Nov/Dec 2006.
|
| |
10
|
E. Bruns and O. Bimber, "Adaptive Training of Video Sets for Image Recognition on Mobile Phones," Journal of Personal and Ubiquitous Computing, 2008, to appear.
|
| |
11
|
C. Seifert, L. Paletta, A. Jeitler, E. Hödl, J.-P. Andreu, P. M. Luley, and A. Almer, "Visual Object Detection for Mobile Road Sign Inventory," in Proc. of Mobile Human-Computer Interaction - Mobile HCI 2004, 6th International Symposium, 2004, pp. 491--495.
|
| |
12
|
|
| |
13
|
H. Bay, B. Fasel, and L. V. Gool, "Interactive Museum Guide: Fast and Robust Recognition of Museum Objects," in Proceedings of the First International Workshop on Mobile Vision, May 2006.
|
| |
14
|
|
| |
15
|
G. Takacs, V. Chandrasekhar, B. Girod, and R. Grzeszczuk, "Feature Tracking for Mobile Augmented Reality Using Video Coder Motion Vectors," in ISMAR '07: Proceedings of the Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'07), 2007.
|
| |
16
|
T. Yeh, K. Tollmar, and T. Darrell, "Searching the Web with Mobile Images for Location Recognition," in Proc. of Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2. IEEE Computer Society, 2004, pp. 76--81.
|
 |
17
|
|
| |
18
|
L. V. G. H. Shao, T. Svoboda, "Zubud-Zürich buildings database for image based recognition." ETH Zürich, Tech. Rep. 260, 2003.
|
| |
19
|
Y. Ke and R. Sukthankar, "PCA-SIFT: A More Distinctive Representation for Local Image Descriptors," in Proc. of Conference on Computer Vision and Pattern Recognition (CVPR), vol. IEEE Computer Society, 2004, pp. 506--513.
|
| |
20
|
W.-C. Chen, Y. Xiong, J. Gao, N. Gelfand, and R. Grzeszczuk, "Efficient Extraction of Robust Image Features on Mobile Devices," in ISMAR '07: Proceedings of the Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'07), 2007.
|
| |
21
|
P. Viola and M. J. Jones, "Rapid object detection using a boosted cascade of simple features," in Proc. of Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 2001, pp. I:511--518.
|
CITED BY 2
|
|
|
|
|
Harlan Hile , Ramakrishna Vedantham , Gregory Cuellar , Alan Liu , Natasha Gelfand , Radek Grzeszczuk , Gaetano Borriello, Landmark-based pedestrian navigation from collections of geotagged photos, Proceedings of the 7th International Conference on Mobile and Ubiquitous Multimedia, December 03-05, 2008, Umeå, Sweden
|
|