Material Information Acquisition Using a ToF Range Sensor for Interactive Object Recognition

  • Md. Abdul Mannan
  • Hisato Fukuda
  • Yoshinori Kobayashi
  • Yoshinori Kuno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)


This paper proposes a noncontact active vision technique that analyzes the reflection pattern of infrared light to estimate the object material according to the degree of surface smoothness (or roughness). To obtain the surface micro structural details and the surface orientation information of a free-form 3D object, the system employs only a time-of-flight range camera. It measures reflection intensity patterns with respect to surface orientation for various material objects. Then it classifies these patterns by Random Forest (RF) classifier to identify the candidate of material of reflected surface. We demonstrate the efficiency of the method through experiments by using several household objects under normal illuminating condition. Our main objective is to introduce material information in addition to color, shape and other attributes to recognize target objects more robustly in the interactive object recognition framework.


Shape Index Service Robot Range Sensor Quadratic Surface Surface Roughness Parameter 
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.


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  1. 1.
    Kuno, Y., Sakata, K., Kobayashi, Y.: Objet Recognition in Service Robot: Conducting Verbal Interaction on Color and Spatial Relationship. In: Proc. IEEE 12th ICCV Workshop (HIC), pp. 2025–2031 (2009)Google Scholar
  2. 2.
    Nicodemus, F.: Directional Reflectance and Emissivity of an Opaque Surface. Applied Optics 4(7), 767–773 (1986)CrossRefGoogle Scholar
  3. 3.
    Dana, K.J., Van-Ginneken, S.K., Koenderink, J.J.: Reflectance and Texture of Real World Surfaces. ACM Transaction on Graphics 18(1), 1–34 (1999)CrossRefGoogle Scholar
  4. 4.
    Jensen, H.W., Marschner, S., Levoy, M., Hanrahan, P.: A practical Model for Subsurface Light Transport. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques (2001)Google Scholar
  5. 5.
    Pont, S.C., Koenderink, J.J.: Bidirectional Texture Contrast Function. International Journal of Computer Vision 62(1-2), 17–34 (2005)CrossRefzbMATHGoogle Scholar
  6. 6.
    Besl, P.J., Jain, R.C.: Three-dimensional Object Recognition. ACM Computing Surveys 17(1), 75–145 (1985)CrossRefGoogle Scholar
  7. 7.
    Lo, T.-W.R., Paul Siebert, J.: Local Feature Extraction and Matching on Range Image: 2.5D SIFT. Computer Vision and Image Understanding 113(12), 1235–1250 (2009)CrossRefGoogle Scholar
  8. 8.
    Bhanu, B., Chen, H.: Human Ear Recognition in 3D. In: Workshop on Multimodal User Authentication, pp. 91–98 (2003)Google Scholar
  9. 9.
    Bayramoglu, N., Aydin Alatan, A.: Shape Index SIFT: Range Image Recognition Using Local Feature. In: International Conference on Pattern Recognition, pp. 352–355 (2010)Google Scholar
  10. 10.
    Liu, C., Lavanya, S., Adelson, E.H., Rosenholtz, R.: Exploring Features in a Bayesian Framework for Material Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 239–246 (2010)Google Scholar
  11. 11.
    Orun, A.B., Alkis, A.: Material Identification by Surface Reflection Analysis in Combination with Bundle Adjustment Technique. Pattern Recognition Letter 24(9-10), 1589–1598 (2003)CrossRefzbMATHGoogle Scholar
  12. 12.
    Tian, G.Y., Lu, R.S., Gledhill, D.: Surface Measurement Using Active Vision and Light Scattering. Optics and Lasers in Engineering 45(1), 131–139 (2007)CrossRefGoogle Scholar
  13. 13.
    Culshaw, B., Pierce, G., Jun, P.: Non-contact Measurement of the Mechanical Properties of Materials Using an All-optical Technique. IEEE Sensors Journal 3(1), 62–70 (2003)CrossRefGoogle Scholar
  14. 14.
    Mannan, M. A., Das, D., Kobayashi, Y., Kuno, Y.: Object material classification by surface reflection analysis with a time-of-flight range sensor. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammound, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6454, pp. 439–448. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Wyszecki, G., Stiles, W.S.: Color Science, 2nd edn. Wiley, New York (1982)Google Scholar
  16. 16.
    Shafer, S.A.: Using Color to Separate Reflection Components Color Research & Application, vol. 10(4), pp. 210–218 (1985)Google Scholar
  17. 17.
    Tominaga, S., Wandell, A.B.: The Standard Surface Reflectance Model and Illuminant Estimation. Journal Optical Society of America A 6(4), 576–584 (1989)CrossRefGoogle Scholar
  18. 18.
    Angel, E.: Interactive Computer Graphics: A Top-Down Approach Using OpenGL, 3rd edn. Addison-Wesley, Reading (2003)Google Scholar
  19. 19.
    Phong, B.T.: Illumination for Computer Generated Picture. Communication of the ACM 18(6), 311–317 (1975)CrossRefGoogle Scholar
  20. 20.
    Torrance, K.E., Sparrow, E.M.: Theory for Off-Specular Reflection from Roughened Surfaces. Journal Optical Society 57(9), 1105–1112 (1967)CrossRefGoogle Scholar
  21. 21.
  22. 22.
    Suk, M., Bhandarker, M.S.: Three-Dimensional Object Recognition from Range Image. Springer-Verlag New York, Inc., Secaucus (1992)CrossRefGoogle Scholar
  23. 23.
    Dorai, C., Jain, A.K.: COSMOS-A Representation Scheme for 2D Free-Form Object. IEEE Trans. Pattern Analysis Machine Intell. 19(10), 1115–1130 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Md. Abdul Mannan
    • 1
  • Hisato Fukuda
    • 1
  • Yoshinori Kobayashi
    • 1
  • Yoshinori Kuno
    • 1
  1. 1.Graduate School of Science and EngineeringSaitama UniversitySaitama-shiJapan

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