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BAG: A Binary Descriptor for RGB-D Images Combining Appearance and Geometric Cues

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 710))

Abstract

Feature matching forms the basis for numerous computer vision applications. With the rapid development of 3D sensors, the availability of RGB-D images has been increased stably. Compared to traditional 2D images, the additional depth images in RGB-D images can provide more geometric information. In this paper, we propose a new efficient binary descriptor (namely BAG) for RGB-D image representation by combining appearance and geometric cues. Experimental results show that the proposed BAG descriptor produces better feature matching performance with faster matching speed and less memory than the existing methods.

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References

  1. Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J.: 3D object recognition in cluttered scenes with local surface features: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2270–2287 (2014)

    Article  Google Scholar 

  2. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  3. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  4. Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 356–369. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15558-1_26

    Chapter  Google Scholar 

  5. Tombari, F., Salti, S., Di Stefano, L.: A combined texture-shape descriptor for enhanced 3D feature matching. In: 2011 18th IEEE International Conference on Image Processing, pp. 809–812. IEEE, September 2011

    Google Scholar 

  6. Guo, Y., Sohel, F., Bennamoun, M., Lu, M., Wan, J.: Rotational projection statistics for 3D local surface description and object recognition. Int. J. Comput. Vis. 105(1), 63–86 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15561-1_56

    Chapter  Google Scholar 

  8. Prakhya, S.M., Liu, B., Lin, W.: B-SHOT: a binary feature descriptor for fast and efficient keypoint matching on 3D point clouds. In: 2015 IEEE/RSJ International Conference Intelligent Robots and Systems (IROS), pp. 1929–1934. IEEE, September 2015

    Google Scholar 

  9. Beksi, W.J., Papanikolopoulos, N.: Object classification using dictionary learning and RGB-D covariance descriptors. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 1880–1885. IEEE, May 2015

    Google Scholar 

  10. Feng, G., Liu, Y., Liao, Y.: Loind: an illumination and scale invariant RGB-D descriptor. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 1893–1898. IEEE, May 2015

    Google Scholar 

  11. do Nascimento, E.R., Oliveira, G.L., Vieira, A.W., Campos, M.F.: On the development of a robust, fast and lightweight keypoint descriptor. Neurocomputing 120, 141–155 (2013)

    Article  Google Scholar 

  12. Glocker, B., Izadi, S., Shotton, J., Criminisi, A.: Real-time RGB-D camera relocalization. In: 2013 IEEE International Symposium Mixed and Augmented Reality (ISMAR), pp. 173–179. IEEE, October 2013

    Google Scholar 

  13. Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 573–580. IEEE, October 2012

    Google Scholar 

  14. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  15. Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J., Kwok, N.M.: A comprehensive performance evaluation of 3D local feature descriptors. Int. J. Comput. Vis. 116(1), 66–89 (2016)

    Article  MathSciNet  Google Scholar 

  16. Agrawal, M., Konolige, K., Blas, M.R.: CenSurE: center surround extremas for realtime feature detection and matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 102–115. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88693-8_8

    Chapter  Google Scholar 

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (Nos. 61602499 and 61471371), the National Postdoctoral Program for Innovative Talents (No. BX201600172), and China Postdoctoral Science Foundation.

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Correspondence to Yulan Guo .

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Xiao, X., He, S., Guo, Y., Lu, M., Zhang, J. (2017). BAG: A Binary Descriptor for RGB-D Images Combining Appearance and Geometric Cues. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_7

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  • DOI: https://doi.org/10.1007/978-981-10-5230-9_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5229-3

  • Online ISBN: 978-981-10-5230-9

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