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http://dx.doi.org/10.5370/KIEE.2010.59.2.429

Combining Shape and SIFT Features for 3-D Object Detection and Pose Estimation  

Tak, Yoon-Sik (고려대학교 전자컴퓨터공학과)
Hwang, Een-Jun (고려대학교 전기전자전파공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.59, no.2, 2010 , pp. 429-435 More about this Journal
Abstract
Three dimensional (3-D) object detection and pose estimation from a single view query image has been an important issue in various fields such as medical applications, robot vision, and manufacturing automation. However, most of the existing methods are not appropriate in a real time environment since object detection and pose estimation requires extensive information and computation. In this paper, we present a fast 3-D object detection and pose estimation scheme based on surrounding camera view-changed images of objects. Our scheme has two parts. First, we detect images similar to the query image from the database based on the shape feature, and calculate candidate poses. Second, we perform accurate pose estimation for the candidate poses using the scale invariant feature transform (SIFT) method. We earned out extensive experiments on our prototype system and achieved excellent performance, and we report some of the results.
Keywords
Pose estimation; 3-D object retrieval; Shape-based retrieval; Distance curve; SIFT;
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1 J. Zhang, S.K. Zhou, L. McMilan and D. Comaniciu, "Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network," CVPR'07, pp.1-8, 2007
2 G. Panin and A. Knoll, "Fully Automatic Real-Time 3D Object Tracking using Active Contour and Appearance Models," Journal of Multimedia, vol. 1, no. 7, pp. 62-70, 2006.
3 H. Kim, Y. Tak and E. Hwang, "A Shape-Based Indexing Scheme for Camera View-Invariant 3-D Object Retrieval," Multimedia Tools and Applications, Accepted.
4 Y. Tak and E. Hwang, "An indexing scheme for efficient camera angle invariant image retrieval," International Conference on Computer and Information Technology, pp.143-148, 2008
5 Y. Tak and E. Hwang, "Indexing and Matching Scheme for Recognizing 3D Objects from Single 2D Image," International Conference on Internet and Multimedia Systems and Applications (IMSA'09), pp. 60-67, 2009.
6 D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," IJCV, vol. 60, no. 2, pp. 91-110, Nov. 2004   DOI
7 D. Wagner, G. Reitmayr, A. Mulloni, T. Drummond and D. Schmalstieg, "Pose Tracking from Natural Features on Mobile Phones," IEEE/ACM International Symposium on Mixed and Augmented Reality, pp. 125-134, 2008.
8 C. Faloutsos, M. Ranganathan and Y. Manolopoulos, "Fast subsequence matching in time-series databases," ACM SIGMOD, pp.419-429, 1993
9 Y. Nam, E. Hwang and D. Kim, "A smilarity-based leaf image retrieval scheme: Joining shape and venation features," Computer Vision and Image Understanding, vol. 110, no. 2, pp.245-259, 2008.   DOI   ScienceOn
10 C. Faloutsos and K. Lin, "FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets," ACM SIGMOD, pp.163-174. 1995.
11 L. Vacchetti, V. Lepetit and P. Fua, "Stable real-time 3D tracking using online and offline information," IEEE Transactions on PAMI, vol. 26, no. 10, pp. 1385-1391, 2004   DOI   ScienceOn
12 M. Saito and K. Kitaguchi, "Appearance based object pose estimation using regression models," SICE Annual Conference, pp.1926-1929, 2008
13 E. Keogh and C. Ratanamahatana, "Exact indexing of dynamic time warping," Knowledge and Information Systems, Vol. 7, pp. 358-386, 2005.   DOI   ScienceOn