Active Testing Search for Point Cloud Matching

  • Miguel Amável Pinheiro
  • Raphael Sznitman
  • Eduard Serradell
  • Jan Kybic
  • Francesc Moreno-Noguer
  • Pascal Fua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


We present a general approach for solving the point-cloud matching problem for the case of mildly nonlinear transformations. Our method quickly finds a coarse approximation of the solution by exploring a reduced set of partial matches using an approach to which we refer to as Active Testing Search (ATS). We apply the method to registration of graph structures by branching point matching. It is based solely on the geometric position of the points, no additional information is used nor the knowledge of an initial alignment. In the second stage, we use dynamic programming to refine the solution. We tested our algorithm on angiography, retinal fundus, and neuronal data gathered using electron and light microscopy. We show that our method solves cases not solved by most approaches, and is faster than the remaining ones.


point cloud matching graph matching image registration active search dendrites 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Serradell, E., Glowacki, P., Kybic, J., Moreno-Noguer, F., Fua, P.: Robust non-rigid registration of 2D and 3D graphs. In: IEEE CVPR, pp. 996–1003 (2012)Google Scholar
  2. 2.
    Geman, D., Jedynak, B.: An active testing model for tracking roads in satellite images. IEEE Trans. on Pattern Analysis and Machine Intelligence 18, 1–14 (1995)CrossRefGoogle Scholar
  3. 3.
    Yuille, A., Coughlan, J.: Twenty questions, focus of attention, and A*: A theoretical comparison of optimization strategies. In: International Workshop on Energy Minimization Methods in CVPR, pp. 197–212 (1997)Google Scholar
  4. 4.
    Sznitman, R., Jedynak, B.: Active testing for face detection and localization. IEEE Trans. on Pattern Analysis and Machine Intelligence 32(10), 1914–1920 (2010)CrossRefGoogle Scholar
  5. 5.
    Sznitman, R., Richa, R., Taylor, R.H., Jedynak, B., Hager, G.D.: Unified detection and tracking of instruments during retinal microsurgery. IEEE Trans. on Pattern Analysis and Machine Intelligence 99, 1 (2012)Google Scholar
  6. 6.
    Gold, S., Rangarajan, A., Lu, C.P., Mjolsness, E.: New algorithms for 2D and 3D point matching: Pose estimation and correspondence. Pattern Recognition 31, 957–964 (1997)Google Scholar
  7. 7.
    Myronenko, A., Song, X.: Point set registration: Coherent point drift. IEEE Trans. on Pattern Analysis and Machine Intelligence 32(12), 2262–2275 (2010)CrossRefGoogle Scholar
  8. 8.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Torr, P.H.S., Zisserman, A.: MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding 78, 138–156 (2000)CrossRefGoogle Scholar
  10. 10.
    Chum, O., Matas, J.: Matching with PROSAC – progressive sample consensus. In: IEEE CVPR, pp. 220–226 (2005)Google Scholar
  11. 11.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  12. 12.
    Pajdla, T., Van Gool, L.: Matching of 3-D curves using semi-differential invariants. In: IEEE ICCV, pp. 390–395 (1995)Google Scholar
  13. 13.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the icp algorithm. In: International Conference on 3-D Digital Imaging and Modeling, pp. 145–152 (2001)Google Scholar
  14. 14.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 509–522 (2001)CrossRefGoogle Scholar
  15. 15.
    Leordeanu, M., Hebert, M.: A Spectral Technique for Correspondence Problems Using Pairwise Constraints. In: IEEE ICCV, vol. 2, pp. 1482–1489 (2005)Google Scholar
  16. 16.
    Serradell, E., Moreno-Noguer, F., Kybic, J., Fua, P.: Robust elastic 2D/3D geometric graph matching. SPIE Medical Imaging 8314(1), 831408-1–831408-8 (2012)Google Scholar
  17. 17.
    Cour, T., Srinivasan, P., Shi, J.: Balanced graph matching. In: Neural Information Processing Systems, pp. 313–320 (2006)Google Scholar
  18. 18.
    Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Research Logistics 2(1-2), 83–97 (1955)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Miguel Amável Pinheiro
    • 1
  • Raphael Sznitman
    • 2
  • Eduard Serradell
    • 3
  • Jan Kybic
    • 1
  • Francesc Moreno-Noguer
    • 3
  • Pascal Fua
    • 2
  1. 1.Center for Machine Perception, Faculty of Electrical EngineeringCzech Technical University in PragueCzech Republic
  2. 2.Computer Vision LaboratoryÉcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
  3. 3.Institut de Robòtica i Informàtica Industrial (CSIC-UPC)BarcelonaSpain

Personalised recommendations