Salient Spin Images: A Descriptor for 3D Object Recognition

  • Jihad H’rouraEmail author
  • Michaël Roy
  • Alamin Mansouri
  • Driss Mammass
  • Patrick Juillion
  • Ali Bouzit
  • Patrice Méniel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)


In the last decades a wide range of algorithms have been devoted to recognize 3D free-from objects under real conditions such as occlusions, clutters, rotation, scale and translation. Spin image is one of these algorithms known to be robust to rotation, translation, occlusions up to 70% and clutters up to 60%, but still suffer from scaling, resolution changes and it is time consuming. In this paper we present a novel approach based on spin images, called salient spin images (SSI). This method enhances spin images algorithm based on its limits. Particularly, it decreases significantly the complexity of the algorithm using DoG detector, it shows a higher performance due to the relevant localization of salient vertices on the scene, and its robustness to occlusions reaches 80%.


3D object recognition Salient vertex spin image DoG Clutter Occlusion True positives 


  1. 1.
    Barros, J.M.D., Habed, A., Demonceaux, C., Mansouri, A.: Computer vision-based approach for rite decryption in old societies. In: 2015 14th IAPR International Conference on Machine Vision Applications (MVA), pp. 451–454. IEEE (2015)Google Scholar
  2. 2.
    Darom, T., Keller, Y.: Scale-invariant features for 3-D mesh models. IEEE Trans. Image Process. 21(5), 2758–2769 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354–3361. IEEE (2012)Google Scholar
  4. 4.
    Horn, B.K.: Closed-form solution of absolute orientation using unit quaternions. JOSA A 4(4), 629–642 (1987)CrossRefGoogle Scholar
  5. 5.
    H’roura, J., Bekkari, A., Mammass, D., Bouzit, A., Mansouri, A., Roy, M., Le Goïc, G.: 3D objects descriptors methods: overview and trends. In: 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 1–9. IEEE (2017)Google Scholar
  6. 6.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (1999)CrossRefGoogle Scholar
  7. 7.
    Johnson, A.E.: Spin-images: a representation for 3-D surface matching. Ph.D. thesis, Carnegie Mellon University (1997)Google Scholar
  8. 8.
    Loncomilla, P., Ruiz-del Solar, J., Martínez, L.: Object recognition using local invariant features for robotic applications: a survey. Pattern Recognit. 60, 499–514 (2016)CrossRefGoogle Scholar
  9. 9.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)Google Scholar
  10. 10.
    Maes, C., Fabry, T., Keustermans, J., Smeets, D., Suetens, P., Vandermeulen, D.: Feature detection on 3D face surfaces for pose normalisation and recognition. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–6. IEEE (2010)Google Scholar
  11. 11.
    Nouri, A., Charrier, C., Lézoray, O.: Multi-scale saliency of 3D colored meshes. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 2820–2824. IEEE (2015)Google Scholar
  12. 12.
    Shah, S.A.A., Bennamoun, M., Boussaid, F.: Keypoints-based surface representation for 3D modeling and 3D object recognition. Pattern Recognit. 64, 29–38 (2017)CrossRefGoogle Scholar
  13. 13.
    Xiang, Y., Choi, W., Lin, Y., Savarese, S.: Data-driven 3D voxel patterns for object category recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1903–1911 (2015)Google Scholar
  14. 14.
    Zhao, S., Yao, H., Zhang, Y., Wang, Y., Liu, S.: View-based 3D object retrieval via multi-modal graph learning. Signal Process. 112, 110–118 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jihad H’roura
    • 1
    Email author
  • Michaël Roy
    • 2
  • Alamin Mansouri
    • 2
  • Driss Mammass
    • 1
  • Patrick Juillion
    • 2
  • Ali Bouzit
    • 1
  • Patrice Méniel
    • 3
  1. 1.IRF-SIC LaboratoryIbn Zohr UniversityAgadirMorocco
  2. 2.LE2IUniversité de Bourgogne Franche-ComtéAuxerreFrance
  3. 3.ARTEHISUniversité de Bourgogne Franche-ComtéDijonFrance

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