Adaptive Line Matching for Low-Textured Images

  • Roi Santos
  • Xosé R. Fdez-Vidal
  • Xosé M. Pardo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)


A novel approach for line matching is proposed, aimed at achieving good performance with low-textured scenes, under uncontrolled illumination conditions. Line matching is performed by an iterative process that uses structural information collected through the use of different line neighbourhoods, making the set of matched lines grows robustly at each iteration. Results show that this approach is suitable to deal with low-textured scenes, and also robust under a wide variety of image transformations.


Line matching Low texture images Man-made environments 


  1. 1.
    Mindru, F., Tuytelaars, T., Van Gool, L., Moons, T.: Moment invariants for recognition under changing viewpoint and illumination. Comput. Vis. Image Underst. 94, 3–27 (2004)CrossRefGoogle Scholar
  2. 2.
    Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13, 891–906 (1991)CrossRefGoogle Scholar
  3. 3.
    Montesinos, P., Gouet, V., Deriche, R.: Differential invariants for color images. In: Proceedings of ICPR 1998, Recognition, pp. 838–840 (1998)Google Scholar
  4. 4.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  5. 5.
    Wang, L., Neumann, U., You, S.: Wide-baseline image matching using Line Signatures. In: Proceedings of ICCV 2009, pp. 1311–1318 (2009)Google Scholar
  6. 6.
    López, J., Fuciños, M., Fdez-Vidal, X.R., Pardo, X.M.: Detection and matching of lines for close-range photogrammetry. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds.) IbPRIA 2013. LNCS, vol. 7887, pp. 732–739. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  7. 7.
    Yang, Z., Cohen, S.: Image registration and object recognition using affine invariants and convex hulls. IEEE Trans. Image Process. 8, 934–946 (1999)zbMATHMathSciNetCrossRefGoogle Scholar
  8. 8.
    Fan, B., Wu, F., Hu, Z.: Line matching leveraged by point correspondences. In: Proceedings of CVPR2010, pp. 390–397 (2010)Google Scholar
  9. 9.
    Shao, H., Svoboda, T., Van Gool, L.: HPAT indexing for fast object/scene recognition based on local appearance. In: Bakker, Erwin M., Lew, Michael, Huang, Thomas S., Sebe, Nicu, Zhou, Xiang Sean (eds.) CIVR 2003. LNCS, vol. 2728. Springer, Heidelberg (2003)Google Scholar
  10. 10.
    Zhang, D.-H., Liang, J., Guo, C.: Photogrammetric 3D measurement method applying to automobile panel. In: Proceedings of the 2nd International Conference on Computer and Automation Engineering (ICCAE), Singapore, February 2010, pp. 70–74Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Roi Santos
    • 1
  • Xosé R. Fdez-Vidal
    • 1
  • Xosé M. Pardo
    • 1
  1. 1.Centro de Investigación en Tecnoloxías da Información (CITIUS)Universidade de Santiago de CompostelaSantiago de CompostelaSpain

Personalised recommendations