A Virtual Telescope on Mobile DevicesQiyuan Tian, Qingyi Meng
Department of Electrical Engineering, Stanford University
Mobile Image Matching Application Feature-based Matching (SIFT/ SURF)
Speeded Up Robust Features (SURF)
[Bay et al., ECCV 2006]
Scale-invariant feature transform (SIFT)
[Lowe, ICCV 1999]
Mobile Virtual Telescope System
Query
Information
Wireless
Network
Reference�D.G. Lowe, "Distinctive image features from scale-invariant
keypoints", Int. J. Comput. Vision, 60 (2) (2004), pp. 91 –110.
�H. Bay, T. Tuytelaars, L.V. Gool, SURF: speeded up robust
features, in: European Conference on Computer Vision, vol. 1,
2006, pp. 404–417.
�J.M. Morel and G.Yu, ASIFT, A new framework for fully affine
invariant image comparison. SIAM Journal on Imaging Sciences,
2(2):438-469 (2009)
Client Motorola MOTA855
Network 802.11gn
Server Lenovo Laptop Y470
2.3 GHz 4 GB RAM + WAMP
Database 8 Stanford Buildings
System Description
Zoom in
Camera
Capture
Image
Matched +
Satellite
Images
Crop
Image
Match
Features
Amplified
Object
Extract
Features
Display
Viewfinder
KDTree RANSAC
xaaa
aaax
=
121110
020100'
(1124, 1072) SIFT features
1124 pre-RANSAC matches
11 post-RANSAC matches
ANN Affine Model
Threshold
Feature-based Matching (Affine-SIFT)
Acknowledgements: Images and figures used in this poster
courtesy of corresponding authors.
Future Work�User Interface
�Low-resolution Image
retrieval (multiple zooming in
and retrieval/ more robust
features for small images)
� Affine invariant Image
matching
�Geographic information to
assist retrieval (Client: GPS
+ inertial sensor; server:
images with location tag)Image Formation Model Affine-SIFT in One FigureMain Decomposition
SIFT:
�Rotation and translation are
normalized.
�Zoom is simulated in scale space
�No latitude and longitude
Affine-SIFT:
�Simulate latitude and longitude,
then apply SIFT
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