Coarse-to-fine dot array marker detection with accurate edge localization for stereo visual tracking

Biomedical Signal Processing and Control

Volumn 15, Issue , 2015, Pages 49-59

  Wang, Junchen ^a   Kobayashi, Etsuko ^a   Sakuma, Ichiro ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Dot feature; Image filtering; Marker detection; Stereo tracking; Sub pixel edge localization

Indexed keywords

PIXELS; SIGNAL DETECTION;

DOT FEATURE; IMAGE FILTERING; MARKER DETECTIONS; STEREO TRACKING; SUB-PIXEL EDGE LOCALIZATIONS;

FEATURE EXTRACTION;

ACCURACY; ALGORITHM; ARTICLE; EYE TRACKING; FEASIBILITY STUDY; LAPAROSCOPY; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL;

EID: 84908460824     PISSN: 17468094     EISSN: 17468108     Source Type: Journal    
DOI: 10.1016/j.bspc.2014.09.008     Document Type: Article

References (37)

1. Z. Zhang, A flexible new technique for camera calibration, IEEE Trans. Pattern Anal. Mach. Intell. 22 (11) (2000) 1330-1334, http://dx.doi.org/10.1109/34.888718
2. J. Helferty, C. Zhang, G. McLennan, W. Higgins, Videoendoscopic distortion correction and its application to virtual guidance of endoscopy, IEEE Trans. Med. Imaging 20 (7) (2001) 605-617, http://dx.doi.org/10.1109/42.932745.
3. V. Lepetit, F. Moreno-Noguer, P. Fua, EPnP: an accurate O(n) solution to the PNP problem, Int. J. Comput. Vis. 81 (2) (2009) 155-166, http://dx.doi.org/10.1007/s11263-008-0152-6.
4. F. Zhou, H.B.-L. Duh, M. Billinghurst, Trends in augmented reality tracking, interaction and display: a review of ten years of Ismar, in: Proceedings of the Seventh IEEE/ACM International Symposium on Mixed and Augmented Reality, ISMAR '08, IEEE Computer Society, Washington, DC, USA, 2008, pp. 193-202, http://dx.doi.org/10.1109/ISMAR.2008.4637362.
5. B. Espiau, F. Chaumette, P. Rives, A new approach to visual servoing in robotics, IEEE Trans. Robot. Autom. 8 (3) (1992) 313-326, http://dx.doi.org/10.1109/70.143350.
6. F. Nageotte, P. Zanne, C. Doignon, M. de Mathelin, Visual servoing-based endoscopic path following for robot-assisted laparoscopic surgery, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 2364-2369, http://dx.doi.org/10.1109/IROS.2006.282647.
7. U. Jayarathne, A. McLeod, T. Peters, E. Chen, Robust intraoperative US probe tracking using a monocular endoscopic camera, in: K. Mori, I. Sakuma, Y. Sato, C. Barillot, N. Navab (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013, Vol. 8151 of Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2013, pp. 363-370, http://dx.doi.org/10.1007/978-3-642-40760-4-46.
8. P. Pratt, A. Marco, C. Payne, A. Darzi, G.-Z. Yang, Intraoperative ultrasound guidance for transanal endoscopic microsurgery, in: N. Ayache, H. Delingette, P. Golland, K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012, Vol. 7510 of Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2012, pp. 463-470, http://dx.doi.org/10.1007/978-3-642-33415-3-57.
9. J. West, C.R. Maurer Jr., Designing optically tracked instruments for image-guided surgery, IEEE Trans. Med. Imaging 23 (5) (2004) 533-545, http://dx.doi.org/10.1109/TMI.2004.825614.
10. J. Snchez-Margallo, F. Snchez-Margallo, J. Pagador, I. Oropesa, M. Lucas, E. Gmez, J. Moreno, Technical evaluation of a third generation optical pose tracker for motion analysis and image-guided surgery, in: K. Drechsler, M. Erdt, M. Linguraru, C. Oyarzun Laura, K. Sharma, R. Shekhar, S. Wesarg (Eds.), Clinical Image-Based Procedures. From Planning to Intervention, Vol. 7761 of Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2013, pp. 75-82, http://dx.doi.org/10.1007/978-3-642-38079-2-10.
11. H. Kato, M. Billinghurst, I. Poupyrev, K. Imamoto, K. Tachibana, Virtual object manipulation on a table-top AR environment, in: IEEE and ACM International Symposium on Augmented Reality, 2000, pp. 111-119, http://dx.doi.org/10.1109/ISAR.2000.880934.
12. M. Fiala, Artag, a fiducial marker system using digital techniques, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 2005, pp. 590-596, http://dx.doi.org/10.1109/CVPR.2005.74.
13. X. Zhang, S. Fronz, N. Navab, Visual marker detection and decoding in AR systems: a comparative study, in: Proceedings of International Symposium on Mixed and Augmented Reality, 2002, pp. 97-106, http://dx.doi.org/10.1109/ISMAR.2002.1115078.
14. Opencv reference: http://opencv.org/
15. L. Lucchese, S. Mitra, Using saddle points for subpixel feature detection in camera calibration targets, in: Asia-Pacific Conference on Circuits and Systems, Vol. 2, 2002, pp. 191-195, http://dx.doi.org/10.1109/APCCAS.2002.1115151.
16. S. Bennett, J. Lasenby, Chess - quick and robust detection of chess-board features, Comput. Vis. Image Understand. 118 (2014) 197-210, http://dx.doi.org/10.1016/j.cviu.2013.10.008.
17. A. De la Escalera, J.M. Armingol, Automatic chessboard detection for intrinsic and extrinsic camera parameter calibration, Sensors 10 (3) (2010) 2027-2044, http://dx.doi.org/10.3390/s100302027.
18. W. Sun, X. Yang, S. Xiao, W. Hu, Robust checkerboard recognition for efficient nonplanar geometry registration in projector-camera systems, in: Proceedings of the Fifth ACM/IEEE International Workshop on Projector Camera Systems, PROCAMS'08, ACM, New York, USA, 2008, pp. 2:1-2:7, http://dx.doi.org/10.1145/1394622.1394625.
19. J. Xia, J. Long Xiong, X. Xu, H. Qin, A multiscale sub-pixel detector for corners in camera calibration targets, in: International Conference on Intelligent Computation Technology and Automation (ICICTA), Vol. 1, 2010, pp. 196-199, http://dx.doi.org/10.1109/ICICTA.2010.429.
20. A. Huertas, G. Medioni, Detection of intensity changes with subpixel accuracy using Laplacian-Gaussian masks, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (5) (1986) 651-664, http://dx.doi.org/10.1109/TPAMI.1986.4767838.
21. H. Zhou, A.M. Wallace, P.R. Green, Efficient tracking and ego-motion recovery using gait analysis, Signal Process. 89 (12) (2009) 2367-2384, http://dx.doi.org/10.1016/j.sigpro.2009.04.010, special Section: Visual Information Analysis for Security.
22. H. Zhou, X. Li, A. Sadka, Nonrigid structure-from-motion from 2-D images using Markov chain Monte Carlo, IEEE Trans. Multimedia 14 (1) (2012) 168-177, http://dx.doi.org/10.1109/TMM.2011.2170406.
23. W. Zhou, M. Fei, H. Zhou, K. Li, A sparse representation based fast detection method for surface defect detection of bottle caps, Neurocomputing 123 (2014) 406-414, http://dx.doi.org/10.1016/j.neucom.2013.07.038.
24. Halcon http://www.halcon.com/
25. S. Suzuki, K. Be, Topological structural analysis of digitized binary images by border following, Comput. Vis. Graph. Image Process. 30 (1) (1985) 32-46, http://dx.doi.org/10.1016/0734-189X(85)90016-7.
26. Y. Nomura, M. Sagara, H. Naruse, A. Ide, Edge location to subpixel precision and analysis, Syst. Comput. Japan 22 (9) (1991) 70-81, http://dx.doi.org/10.1002/scj.4690220908.
27. E. Lyvers, O. Mitchell, M. Akey, A. Reeves, Subpixel measurements using a moment-based edge operator, IEEE Trans. Pattern Anal. Mach. Intell. 11 (12) (1989) 1293-1309, http://dx.doi.org/10.1109/34.41367.
28. Y.-D. Qu, C.-S. Cui, S.-B. Chen, J.-Q. Li, A fast subpixel edge detection method using Sobel-Zernike moments operator, Image Vis. Comput. 23 (1) (2005) 11-17, http://dx.doi.org/10.1016/j.imavis.2004.07.003.
29. F. Da, H. Zhang, Sub-pixel edge detection based on an improved moment, Image Vis. Comput. 28 (12) (2010) 1645-1658, http://dx.doi.org/10.1016/j.imavis.2010.05.003.
30. F. Bouchara, S. Ramdani, Subpixel edge re.nement using deformable models, J. Opt. Soc. Am. A: Opt. Image Sci. 26 (4) (2009) 820-832, http://dx.doi.org/10.1364/JOSAA.26.000820.
31. V. Berzins, Accuracy of Laplacian edge detectors, Comput. Vis. Graph. Image Process. 27 (2) (1984) 195-210, http://dx.doi.org/10.1016/S0734-189X(84)80043-2.
32. R. Deriche, Recursively Implementating the Gaussian and its Derivatives, Rapport de recherche RR-1893, INRIA, 1993.
33. L. van Vliet, I. Young, P. Verbeek, Recursive Gaussian derivative filters, in: Proceedings of Fourteenth International Conference on Pattern Recognition, Vol. 1, 1998, pp. 509-514, http://dx.doi.org/10.1109/ICPR.1998.711192.
34. S. Tan, J.L. Dale, A. Johnston, Performance of three recursive algorithms for fast space-variant Gaussian filtering, Real-Time Imaging 9 (3) (2003) 215-228, http://dx.doi.org/10.1016/S1077-2014(03)00040-8.
35. B.K.P. Horn, Closed-form solution of absolute orientation using unit quaternions, J. Opt. Soc. Am. A: Opt. Image Sci. 4 (4) (1987) 629-642, http://dx.doi.org/10.1364/JOSAA.4.000629.
36. J. Wang, H. Suenaga, L. Yang, H. Liao, E. Kobayashi, T. Takato, I. Sakuma, Real-time marker-free patient registration and image-based navigation using stereovision for dental surgery, in: H. Liao, C. Linte, K. Masamune, T. Peters, G. Zheng (Eds.), Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, Vol. 8090 of Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2013, pp. 9-18, http://dx.doi.org/10.1007/978-3-642-40843-4-2.
37. J. Wang, H. Suenaga, K. Hoshi, L. Yang, E. Kobayashi, I. Sakuma, H. Liao, Augmented reality navigation with automatic marker-free image registration using 3-D image overlay for dental surgery, IEEE Trans. Biomed. Eng. 61 (4) (2014) 1295-1304, http://dx.doi.org/10.1109/TBME.2014.2301191.