Local Geometric Consensus: A General Purpose Point Pattern-Based Tracking Algorithm

IEEE Transactions on Visualization and Computer Graphics

Volumn 21, Issue 11, 2015, Pages 1299-1308

  Yang, Liming ^a   Normand, Jean Marie ^a   Moreau, Guillaume ^a  

^a ECOLE CENTRALE DE NANTES   (France)

Author keywords

Augmented reality; Point pattern matching; Pose computation; Tracking

Indexed keywords

ALGORITHMS; AUGMENTED REALITY; PATTERN MATCHING; SURFACE DISCHARGES;

2-D HOMOGRAPHY; IMAGE TRACKING; PERSPECTIVE TRANSFORMATION; POINT PATTERN MATCHING; POINT PATTERNS; SIMILARITY TRANSFORMATION; SPEED PERFORMANCE; TRACKING ALGORITHM;

IMAGE MATCHING;

EID: 84943751117     PISSN: 10772626     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVCG.2015.2459897     Document Type: Article

References (30)

1. D. H. Ballard. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn., 13(2):111-122, Jan. 1981.
2. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool. Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst., 110(3):346-359, June 2008.
3. P. J. Besl and N. D. McKay. A Method for Registration of 3-D Shapes. IEEE Trans. Pattern Anal. Mach. Intell., 14(2):239-256, Feb. 1992.
4. M. Calonder, V. Lepetit, M. Ozuysal, T. Trzcinski, C. Strecha, and P. Fua. BRIEF: Computing a Local Binary Descriptor Very Fast. IEEE Trans. Pattern Anal. Mach. Intell., 34(7):1281-1298, July 2012.
5. O. Chum and J. Matas. Matching with PROSAC-Progressive Sample Consensus. In Proc. of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR '05, pages 220-226, 2005.
6. S. Filipe and L. A. Alexandre. A Comparative Evaluation of 3D Keypoint Detectors in a RGB-D Object Dataset. In VISAPP 2014-Proc. of the 9th International Conference on Computer Vision Theory and Applications, pages 476-483, 2014.
7. M. A. Fischler and R. C. Bolles. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Commun. ACM, 24(6):381-395, June 1981.
8. S. Gold, A. Rangarajan, C.-P. Lu, S. Pappu, and E. Mjolsness. New algorithms for 2D and 3D point matching: pose estimation and correspondence. Pattern Recogn., 31(8):1019-1031, Aug. 1998.
9. J. Herling and W. Broll. Random Model Variation for Universal Feature Tracking. In Proc. of the 18th ACM Symposium on Virtual Reality Software and Technology, VRST '12, pages 169-176, 2012.
10. D. Q. Huynh. Metrics for 3D Rotations: Comparison and Analysis. J. Math. Imaging Vis., 35(2):155-164, Oct. 2009.
11. Y. Lamdan, J. T. Schwartz, and H. J. Wolfson. Affine Invariant Model-Based Object Recognition. IEEE Trans. Robot. Autom., 6(5):578-589, Oct. 1990.
12. D. G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision, 60(2):91-110, Nov. 2004.
13. P. McIlroy, S. Izadi, and A. Fitzgibbon. Kinectrack: Agile 6-DoF Tracking Using a Projected Dot Pattern. In Mixed and Augmented Reality (ISMAR), 2012 IEEE International Symposium on, pages 23-29, 2012.
14. D. M. Mount, N. S. Netanyahu, and J. Le Moigne. Efficient algorithms for robust feature matching. Pattern Recogn., 32(1):17-38, Jan. 1999.
15. T. Nakai, K. Kise, and M. Iwamura. Use of Affine Invariants in Locally Likely Arrangement Hashing for Camera-based Document Image Retrieval. In Proc. of the 7th International Conference on Document Analysis Systems, DAS'06, pages 541-552, 2006.
16. C. F. Olson. Efficient Pose Clustering Using a Randomized Algorithm. Int. J. Comput. Vision, 23(2):131-147, June 1997.
17. T.-A. Pham, M. Delalandre, S. Barrat, and J.-Y. Ramel. Accurate junction detection and characterization in line-drawing images. Pattern Recogn., 47(1):282-295, Jan. 2014.
18. R. Raguram, O. Chum, M. Pollefeys, J. Matas, and J.-M. Frahm. USAC: A Universal Framework for Random Sample Consensus. IEEE Trans. Pattern Anal. Mach. Intell., 35(8):2022-2038, Aug. 2013.
19. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski. ORB: An Efficient Alternative to SIFT or SURF. In Proc. of the 2011 IEEE International Conference on Computer Vision, ICCV '11, pages 2564-2571, 2011.
20. M. F. Schilling. The surprising predictability of long runs. Math. Mag., 85(2):141-149, Apr. 2012.
21. J. Shi and C. Tomasi. Good Features to Track. In Proc. of the 1994 IEEE Conference on Computer Vision and Pattern Recognition, CVPR '94, pages 593-600, June 1994.
22. F. Tombari, S. Salti, and L. Di Stefano. Performance Evaluation of 3D Keypoint Detectors. Int. J. Comput. Vision, 102(1-3):198-220, Mar. 2013.
23. Y. Tsin and T. Kanade. A Correlation-Based Approach to Robust Point Set Registration. In Proc. of the 8th European Conference on Computer Vision, ECCV 2004, pages 558-569, 2004.
24. H. Uchiyama and E. Marchand. Toward Augmenting Everything: Detecting and Tracking Geometrical Features on Planar Objects. In Mixed and Augmented Reality (ISMAR), 10th IEEE International Symposium on, pages 17-25, 2011.
25. H. Uchiyama and H. Saito. Random Dot Markers. In Proc. of the 2011 IEEE Virtual Reality Conference, VR '11, pages 271-272, 2011.
26. H. Uchiyama, H. Saito, M. Servìeres, and G. Moreau. Camera tracking by online learning of keypoint arrangements using LLAH in augmented reality applications. Virtual Real., 15(2-3):109-117, June 2011.
27. S. Umeyama. Least-Squares Estimation of Transformation Parameters Between Two Point Patterns. IEEE Trans. Pattern Anal. Mach. Intell., 13(4):376-380, Apr. 1991.
28. H. J.Wolfson and I. Rigoutsos. Geometric Hashing: An Overview. IEEE Comput. Sci. Eng., 4(4):10-21, Oct. 1997.
29. L. Yang, J.-M. Normand, and G. Moreau. Robust Random Dot Markers: Towards Augmented Unprepared Maps with Pure Geographic Features. In Proc. of the 20th ACM Symposium on Virtual Reality Software and Technology, VRST '14, pages 45-54, 2014.
30. Y. Zhong. Intrinsic Shape Signatures: A Shape Descriptor for 3D Object Recognition. In Proc. of the IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pages 689-696, 2009.