Object identification with surface signatures

  • Adnan A. Y. Mustafa
  • Linda G. Shapiro
  • Mark A. Ganter
Object Recognition and Tracking
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1296)


In this paper we describe a model-based object identification system. Given a set of 3D objects and a scene containing one or more of these objects, the system identifies which objects appear in the scene by matching surface signatures. Surface signatures are statistical features which are uniform for a uniform surface. Two types of surfaces are employed; curvature signatures and spectral signatures. Furthermore, the system employs an inexpensive acquisition setup consisting of a single CCD camera and two light sources. The system has been tested on 95 observed-surfaces and 77 objects of varying degrees of curvature and color with good results.


Spectral Signature Image Scene Elliptical Cylinder Surface Signature Curvature Signature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anuta, Paul 1970. “Spatial Registration of Multispectral and Multitemporal Digital Imagery Using FFT Techniques”, IEEE Transaction on Geoscience Electronics, 8 (4), Oct., pp. 353–368.Google Scholar
  2. 2.
    Ranade, S. and Rosenfeld A. 1980. “Point Pattern Matching in Relaxation”, Pattern Recognition, 12, pp. 269–275.CrossRefGoogle Scholar
  3. 3.
    Besl, P. and Jain, R. 1985. “Intrinsic and Extrinsic Surface Characterization”. In the Conference on Computer Vision and Pattern Recognition, New York, NY, June.Google Scholar
  4. 4.
    Haralick, R. and Watson, L. and Laffey, T. 1983. “The Topographic Primal Sketch”, Int. J. Robot Res., 2 (1), 50–72.Google Scholar
  5. 5.
    Newman, T., Flynn, P. and Jain, A. 1993. “Model-Based Classification of Quadric surfaces”, CVGIP: Image Understanding, 58 (2), September, pp. 235–249.CrossRefGoogle Scholar
  6. 6.
    Haralick, R. and Kelly, G. 1969. “Pattern Recognition With Measurement Space And Spatial Clustering For Multiple Images”, Proc. IEEE 57, 654–445, April.Google Scholar
  7. 7.
    Ohta, Y., Kanade, K. and Saki, T. 1980. “Color information for region Segmentation”. In Computer Vision, Graphics and Image Processing, 13, 224–241.Google Scholar
  8. 8.
    Healey, G., Shafer, S. and Wolff, L., eds. 1992. Physics-Based Vision: Color, Jones and Bartlett Publishers, Boston.Google Scholar
  9. 9.
    Swain, M. and Ballard, D. 1991. “Color Indexing”, In the International Journal of Computer Vision, 7 (11), pp. 12–32.CrossRefGoogle Scholar
  10. 10.
    Stricker, M. and Orengo, M. 1995. “Similarity of Color Images”. In Storage and Retrieval for Image and Video Data-beases III, Volume 2420, pp. 381–392.Google Scholar
  11. 11.
    Finlayson, G., Chatterjee, S. and Funt, B. “Color Angular Indexing”. In the Fourth European Conference Computer Vision, Cambridge, UK, April 1996, pp. 16–27.Google Scholar
  12. 12.
    Higuchi, K., Delingette, H. and Ikeuchi, K. 1994. “Merging Multiple Views Using a Spherical Representation”, Second CAD-Based Vision Workshop, Champion, Penn, Feb. 8–11, pp. 17–26.Google Scholar
  13. 13.
    Grewe, L. and Kak, A. 1994. “Interactive Learning of Multiple Attribute Hash Table for Fast 3D Object Recognition”, Second CAD-Based Vision Workshop, Champion, Penn, Feb. 8–11, pp. 17–26.Google Scholar
  14. 14.
    Mustafa, A. A., Shapiro, L. G. and Ganter, M. A. 1996. “3D Object Recognition from Color Intensity Images”. In the 13th International Conference On Pattern Recognition, Vienna, Austria, August 25–30.Google Scholar
  15. 15.
    Mustafa, A. A., Shapiro, L. G. and Ganter, M. A. 1997. “Matching Surface Signatures for Object Recognition”. To appear in the eighth Scandinavian Conference On Image Analysis, Lappeenranta, Finland, June 8–11.Google Scholar
  16. 16.
    Mustafa, Adrian. A. 1995. “Object Identification with Surface Signatures using Color Photometric Stereo”. Ph.D. dissertation, Mechanical Engineering Department, University of Washington, March.Google Scholar
  17. 17.
    Mustafa, A. A., Shapiro, L. G. and Ganter, M. A. 1997. “3D Object Identification with Color and Curvature Signatures”. To appear in Pattern Recognition.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Adnan A. Y. Mustafa
    • 1
  • Linda G. Shapiro
    • 2
  • Mark A. Ganter
    • 3
  1. 1.Dept. of Mechanical and Industrial EngineeringKuwait UniversitySafat
  2. 2.Dept. of Computer Science and EngineeringUniversity of WashingtonSeattleUSA
  3. 3.Dept. of Mechanical EngineeringUniversity of WashingtonSeattleUSA

Personalised recommendations