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Pattern Analysis and Applications

, Volume 20, Issue 4, pp 1061–1076 | Cite as

Object recognition in noisy RGB-D data using GNG

  • José Carlos Rangel
  • Vicente Morell
  • Miguel CazorlaEmail author
  • Sergio Orts-Escolano
  • José García-Rodríguez
Theoretical Advances

Abstract

Object recognition in 3D scenes is a research field in which there is intense activity guided by the problems related to the use of 3D point clouds. Some of these problems are influenced by the presence of noise in the cloud that reduces the effectiveness of a recognition process. This work proposes a method for dealing with the noise present in point clouds by applying the growing neural gas (GNG) network filtering algorithm. This method is able to represent the input data with the desired number of neurons while preserving the topology of the input space. The GNG obtained results which were compared with a Voxel grid filter to determine the efficacy of our approach. Moreover, since a stage of the recognition process includes the detection of keypoints in a cloud, we evaluated different keypoint detectors to determine which one produces the best results in the selected pipeline. Experiments show how the GNG method yields better recognition results than other filtering algorithms when noise is present.

Keywords

3D object recognition Growing neural gas Keypoint detection 

References

  1. 1.
    Aldoma A, Tombari F, Di Stefano L, Vincze M (2012) A global hypotheses verification method for 3d object recognition. In: Fitzgibbon A, Lazebnik S, Perona P, Sato Y, Schmid C (eds) Computer Vision?, vol 7574., ECCV 2012, Lecture Notes in Computer ScienceSpringer, Berlin Heidelberg, pp 511–524Google Scholar
  2. 2.
    Asari M, Sheikh U, Supriyanto E (2014) 3d shape descriptor for object recognition based on kinect-like depth image. Image Vis Comput 32(4):260–269CrossRefGoogle Scholar
  3. 3.
    Besl P, McKay N (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256Google Scholar
  4. 4.
    Chen H, Bhanu B (2007) 3d free-form object recognition in range images using local surface patches. Pattern Recognit Lett 28(10):1252–1262CrossRefGoogle Scholar
  5. 5.
    Chen Y, Medioni G (1991) Object modeling by registration of multiple range images. In: Medioni G (ed) 1991 Proceedings., IEEE International Conference on Robotics and Automation, vol 3. pp 2724–2729Google Scholar
  6. 6.
    Computer Vision LAB: SHOT: Unique signatures of histograms for local surface description—computer vision LAB. http://www.vision.deis.unibo.it/research/80-shot
  7. 7.
    Fritzke B (1995) A growing neural gas network learns topologies. In: Advances in neural information processing systems, vol 7. MIT Press, pp 625–632Google Scholar
  8. 8.
    Guo Y, Bennamoun M, Sohel F, Lu M, Wan J (2014) 3d object recognition in cluttered scenes with local surface features: A survey. IEEE Trans Pattern Anal Mach Intell, IEEE Transactions on 36(11):2270–2287CrossRefGoogle Scholar
  9. 9.
    Hinterstoisser S, Holzer S, Cagniart C, Ilic S, Konolige K, Navab N, Lepetit V (2011) Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: Computer vision (ICCV), 2011 IEEE International Conference on, pp 858–865Google Scholar
  10. 10.
    Johnson A, Hebert M (1998) Surface matching for object recognition in complex three-dimensional scenes. Image Vis Comput 16(9–10):635–651CrossRefGoogle Scholar
  11. 11.
    Johnson A, Hebert M (1999) Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans Pattern Anal Mach Intell 21(5):433–449. doi: 10.1109/34.765655 CrossRefGoogle Scholar
  12. 12.
    Kohonen T (1995) Self-organising maps. Springer-VerlagGoogle Scholar
  13. 13.
    Muja M FLANN—fast library for approximate nearest neighbors: FLANN—FLANN browse. http://www.cs.ubc.ca/research/flann/
  14. 14.
    Martinetz T (1993) Competitive Hebbian learning rule forms perfectly topology preserving maps. In: Gielen S, Kappen B (eds) Proc. ICANN’93, Int. Conf. on Artificial Neural Networks. Springer, London, pp 427–434Google Scholar
  15. 15.
    Martinetz T, Schulten K (1994) Topology representing networks. Neural Netw 7(3):507–522CrossRefGoogle Scholar
  16. 16.
    Muja M, Lowe DG (2014) Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans Pattern Anal Mach Intell 36(11):2227–2240CrossRefGoogle Scholar
  17. 17.
    Pang G, Neumann U (2013) Training-based object recognition in cluttered 3d point clouds. In: 2013 International Conference on 3D Vision—3DV 2013. doi: 10.1109/3DV.2013.20, pp 87–94
  18. 18.
    PCL: Documentation—point cloud library (PCL). http://pointclouds.org/documentation/tutorials/normal_estimation.php
  19. 19.
    Radu Bogdan Rusu: Point cloud library (PCL): pcl::UniformSampling \(<\) PointInT \(>\) class template reference. http://docs.pointclouds.org/1.7.0/classpcl_1_1_uniform_sampling.html#details
  20. 20.
    Rusu R, Blodow N, Beetz M (2009) Fast point feature histograms (fpfh) for 3d registration. In: IEEE International Conference on Robotics and automation. ICRA ’09, pp 3212–3217Google Scholar
  21. 21.
    Sipiran I, Bustos B (2011) Harris 3d: a robust extension of the Harris operator for interest point detection on 3d meshes. Vis Comput 27(11):963–976. doi: 10.1007/s00371-011-0610-y CrossRefGoogle Scholar
  22. 22.
    Tombari F, Di Stefano L (2010) Object recognition in 3d scenes with occlusions and clutter by hough voting. In: 2010 Fourth Pacific-Rim Symposium on Image and Video Technology (PSIVT), pp 349–355 Google Scholar
  23. 23.
    Tombari F, Gori F, Di Stefano L (2011) Evaluation of stereo algorithms for 3d object recognition. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp 990–997Google Scholar
  24. 24.
    Tombari F, Salti S (2011) A combined texture-shape descriptor for enhanced 3d feature matching. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp 809 –812Google Scholar
  25. 25.
    Tombari F, Salti S, Di Stefano L (2010) Unique signatures of histograms for local surface description. In: Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III., ECCV’10. Springer-Verlag, Berlin, Heidelberg, pp 356–369Google Scholar
  26. 26.
    Tombari F, Salti S, Di Stefano L (2013) Performance evaluation of 3d keypoint detectors. Int J Comput Vis 102(1–3):198–220. doi: 10.1007/s11263-012-0545-4 CrossRefGoogle Scholar
  27. 27.
    Viejo D, Garcia J, Cazorla M, Gil D, Johnsson M (2012) Using GNG to improve 3d feature extraction–application to 6DoF egomotion. Neural Netw 32:138–146CrossRefGoogle Scholar
  28. 28.
    Xu G, Mourrain B, Duvigneau R, Galligo A (2013) Analysis-suitable volume parameterization of multi-block computational domain in isogeometric applications. Comput Aid Des 45(2): 395–404 (Solid and Physical Modeling 2012)Google Scholar
  29. 29.
    Zhong Y (2009) Intrinsic shape signatures: a shape descriptor for 3d object recognition. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp 689–696Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • José Carlos Rangel
    • 1
  • Vicente Morell
    • 1
  • Miguel Cazorla
    • 1
    Email author
  • Sergio Orts-Escolano
    • 1
  • José García-Rodríguez
    • 1
  1. 1.Institute for Computer ResearchUniversity of AlicanteAlicanteSpain

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