GENERIC OBJECT RECOGNITION BASED ON FEATURE FUSION IN ROBOT PERCEPTION

Xinde Li, Chaomin Luo, Jean Dezert, and Yingzi Tan

References

  1. [1] Y. Lei, M. Bennamoun, M. Hayat, and Y. Guo An efficient 3D face recognition approach using local geometrical signatures, Pattern Recognition, 47(2), 2014, 509–524.
  2. [2] X.D. Li, X. Zhang, B. Zhu, and X.Z. Dai, A visual navigation method for robot based on a GOR and GPU algorithm, Robot, 34(4), 2012, 466–475 (in Chinese).
  3. [3] S. Allaire, J.J. Kim, S.L. Breen, D.A. Jaffray, et al. Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis, Proc. IEEE CVPR Workshops, Anchorage, AK, 2008, 23–28.
  4. [4] Y. Wakuda, K. Sekiyama, and T. Fukuda, Dynamic eventinterpretation and description from visual scene based oncognitive ontology for recognition by a robot, International Journal of Robotics & Automation, 24(3), 2009, 263–279.
  5. [5] Z. Hamici, Real-time pattern recognition using circular cross-correlation: A robot vision system, International Journal of Robotics & Automation, 21(3), 2006, 174–183.
  6. [6] D.G. Lowe, Object recognition from local scale-invariant features, Proceedings of the IEEE CCV Conference, 2(September), 1999, 1150–1157.
  7. [7] D.G. Lowe, Distinctive image features from scale-invariant key points, International Journal of Computer Vision, 60(2), 2004, 91–110.
  8. [8] P. Scovanner, S. Ali, and M. Shah, A 3-dimensional SIFTdescriptor and its application to action recognition, Proc. 15th ACM MM Conf., Augsburg, Germany, 2007, 357–360.
  9. [9] W. Cheung and G. Hamarneh, N-SIFT: N-dimensional scaleinvariant feature transform for matching medical images, Proc. 4th IEEE Int. Symp. on Biomedical Imaging, Arlington, VA, 2007, 720–723.
  10. [10] R.N. Dalvi, I. Hacihaliloglu, and R. Abugharbieh, 3D ultrasound volume stitching using phase symmetry and Har-ris corner detection for orthopaedic applications, Proc. SPIE, Vol. 7623 (Medical Imaging, 2010), San Diego, CA, 2010.
  11. [11] M. Niemeijer, et al., Registration of 3D spectral OCT volumes using 3D SIFT feature point matching, Proc. SPIE, Vol. 7259 (Medical Imaging, 2009), Lake Buena Vista, FL, 2009.
  12. [12] G.T. Flitton, T.P. Breckon, and N. Megherbi, Object recognition using 3D SIFT in complex CT volumes, Proc. BMVConf., Aberystwyth, UK, 2010, 1–12.
  13. [13] R.B. Rusu, N. Blodow, Z.C. Marton, and M. Beetz, Aligning point cloud views using persistent feature histograms, Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Nice, France, 2008, 3384–3391.
  14. [14] R.B. Rusu, N. Blodow, and M. Beetz, Fast point featurehistograms (FPFH) for 3D registration, Proc. IEEE Int. Conf. on Robotics and Automation, Kobe, Japan, 2009, 3212–3217.
  15. [15] S. Lazebnik, C. Schmid, and J. Ponce, A sparse texturerepresentation using local affine regions, IEEE Transactionson PAMI, 27(8), 2005, 1265–1278.
  16. [16] SIFT demo program (Version 4, July 2005), Beijing, China, [Online], http://www.cs.ubc.ca/lowe/keypoints/.
  17. [17] R. Hess, An open source SIFT library, ACM MM, 2010 [Online], http://robwhess.github.io/opensift/
  18. [18] R.B. Rusu and S. Cousins, 3D is here: Point cloud library (PCL), Proc. IEEE Int. Conf. on Robotics and Automation, Shanghai, China, 2011, 1–4.
  19. [19] J. Sivic and A. Zisserman, Video Google: A text retrieval approach to objects matching in videos, Proc. 9th CCV Conf., 2003, 1470–1478.
  20. [20] D. Arthur and S. Vassilvitskii, k-means++: The advantages of careful seeding, Proc. SODA ’07, Philadelphia, PA, USA, 2007, 1027–1035.
  21. [21] B.E. Boser, I.M. Guyon, and V.N. Vapnik, A training algorithm for optimal margin classifiers, Proc. the 5th ACM Workshop on Computer Learning Theory, Pittsburgh, PA, 1992, 144–152.
  22. [22] K. Lai, L.-F. Bo, X.F. Ren, and D. Fox, A large-scale hierarchical multi-view RGB-D object dataset, Proc. IEEE Int. Conf. on Robotics and Automation, Shanghai, China, 2011, 1817–1824.

Important Links:

Go Back