Keypoint Detection in RGB-D Images Using Binary Patterns

  • Cristina Romero-González
  • Jesus Martínez-Gómez
  • Ismael García-Varea
  • Luis Rodríguez-Ruiz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 418)


Detection of keypoints in an image is a crucial step in most registration and recognition tasks. The information encoded in RGB-D images can be redundant and, usually, only specific areas in the image are useful for the classification process. The process of identifying those relevant areas is known as keypoint detection. The use of keypoints can facilitate the following stages in the image processing process by reducing the search space. To properly represent an image by means of a set of keypoints, properties like repeatability and distinctiveness have to be fullfilled. In this work, we propose a keypoint detection technique based on the Shape Binary Pattern (SBP) descriptor that can be computed from RGB-D images. Next, we rely on this method to identify the most discriminative patterns that are used to detect the most relevant keypoint. Experiments on a well-know benchmark for 3D keypoint detection have been performed to assess our proposal.


Keypoint detection RGB-D images Binary patterns Local descriptors Shape binary pattern 


  1. 1.
    Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retina keypoint. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510–517, June 2012Google Scholar
  2. 2.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Computer Vision ECCV 2006. Lecture Notes in Computer Science, vol. 3951, pp. 404–417 (2006)Google Scholar
  3. 3.
    Bro, R., Acar, E., Kolda, T.G.: Resolving the sign ambiguity in the singular value decomposition. Journal of Chemometrics 22(2), 135–140 (2008)CrossRefGoogle Scholar
  4. 4.
    Castellani, U., Cristani, M., Fantoni, S., Murino, V.: Sparse points matching by combining 3D mesh saliency with statistical descriptors. Computer Graphics Forum 27(2), 643–652 (2008)CrossRefGoogle Scholar
  5. 5.
    Chen, H., Bhanu, B.: 3D free-form object recognition in range images using local surface patches. Pattern Recognition Letters 28(10), 1252–1262 (2007)CrossRefGoogle Scholar
  6. 6.
    Filipe, S., Alexandre, L.A.: A comparative evaluation of 3D keypoint detectors in a RGB-D object dataset. In: 9th International Conference on Computer Vision Theory and Applications. Citeseer, Lisbon (2014)Google Scholar
  7. 7.
    Koenderink, J.J., van Doorn, A.J.: Surface shape and curvature scales. Image and Vision Computing 10(8), 557–564 (1992)CrossRefGoogle Scholar
  8. 8.
    Leutenegger, S., Chli, M., Siegwart, R.: BRISK: binary robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555, November 2011Google Scholar
  9. 9.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Mair, E., Hager, G., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: Computer Vision ECCV 2010. Lecture Notes in Computer Science, vol. 6312, pp. 183–196 (2010)Google Scholar
  11. 11.
    Mian, A., Bennamoun, M., Owens, R.: On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes. International Journal of Computer Vision 89(2–3), 348–361 (2010)CrossRefGoogle Scholar
  12. 12.
    Mian, A., Bennamoun, M., Owens, R.: 3D Model-Based Object Recognition and Segmentation in Cluttered Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1584–1601 (2006)CrossRefGoogle Scholar
  13. 13.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefMATHGoogle Scholar
  14. 14.
    Romero-González, C., Martínez-Gómez, J., García-Varea, I., Rodríguez-Ruiz, L.: Binary patterns for shape description in RGB-D object registration. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (2016). Submission ID 135Google Scholar
  15. 15.
    Rosten, E., Porter, R., Drummond, T.: Faster and Better: A Machine Learning Approach to Corner Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(1), 105–119 (2010)CrossRefGoogle Scholar
  16. 16.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571, November 2011Google Scholar
  17. 17.
    Rusu, R.B.: Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments. Ph.D. thesis, Computer Science department, Technische Universitaet Muenchen, Germany, October 2009Google Scholar
  18. 18.
    Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9–13 2011Google Scholar
  19. 19.
    Salti, S., Tombari, F., Di Stefano, L.: A performance evaluation of 3D keypoint detectors. In: 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), pp. 236–243, May 2011Google Scholar
  20. 20.
    Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of Interest Point Detectors. International Journal of Computer Vision 37(2), 151–172 (2000)CrossRefMATHGoogle Scholar
  21. 21.
    Somanath, G., Kambhamettu, C.: Abstraction and generalization of 3D structure for recognition in large intra-class variation. In: Computer Vision ACCV 2010. Lecture Notes in Computer Science, vol. 6494, pp. 483–496 (2011)Google Scholar
  22. 22.
    Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Computer Vision ECCV 2010. Lecture Notes in Computer Science, vol. 6313, pp. 356–369 (2010)Google Scholar
  23. 23.
    Unnikrishnan, R., Hebert, M.: Multi-scale interest regions from unorganized point clouds. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–8, June 2008Google Scholar
  24. 24.
    Zaharescu, A., Boyer, E., Varanasi, K., Horaud, R.: Surface feature detection and description with applications to mesh matching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 373–380, June 2009Google Scholar
  25. 25.
    Zhong, Y.: Intrinsic shape signatures: a shape descriptor for 3D object recognition. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 689–696, September 2009Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Cristina Romero-González
    • 1
  • Jesus Martínez-Gómez
    • 1
    • 2
  • Ismael García-Varea
    • 1
  • Luis Rodríguez-Ruiz
    • 1
  1. 1.Computer Systems DepartmentUniversity of Castilla-La ManchaAlbaceteSpain
  2. 2.Computer Science and Artificial Intelligence DepartmentUniversity of AlicanteAlicanteSpain

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