Combining Appearance and Range Based Information for Multi-class Generic Object Recognition

  • Doaa Hegazy
  • Joachim Denzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


The use of range images for generic object recognition is not addressed frequently by the computer vision community. This paper presents two main contributions. First, a new object category dataset of 2D and range images of different object classes is presented. Second, a new generic object recognition model from range and 2D images is proposed. The model is able to use either appearance (2D) or range based information or a combination of both of them for multi-class object learning and recognition. The recognition performance of the proposed recognition model is investigated experimentally using the new database and promising results are obtained. Moreover, the best performance gain by combining both appearance and range based information is 35% for single classes while the average gain over classes is 12%.


Recognition Performance Interest Point Object Class Range Image Recognition Model 
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.


  1. 1.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In: 2004 Conference on Computer Vision and Pattern Recognition Workshop, p. 178 (2004)Google Scholar
  2. 2.
    Fergus, R., Perona, P., Zisserman, A.: Object Class Recognition by Unsupervised Scale-Invariant Learning. In: IEEE Computer Society Conference on computer vision and Pattern Recognition CVPR3, June 2003, vol. 2, pp. 264–271 (2003)Google Scholar
  3. 3.
    Hetzel, G., Leibe, B., Levi, P., Schiele, B.: 3d object recognition from range images using local feature histograms. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2001), vol. 2, pp. 394–399 (2001)Google Scholar
  4. 4.
    Lange, R.: 3D Time-of-Flight Distance Measurement with Custom Solid-State Image Sensors in CMOS/CCD-Technology, PhD thesis, University of Siegen (2000)Google Scholar
  5. 5.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Opelt, A., Pinz, A.: Object localization with boosting and weak supervision for generic object recognition. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 862–871. Springer, Heidelberg (2005)Google Scholar
  8. 8.
    Ruiz-correa, S., Shapiro, L.G., Meil, M.: A new paradigm for recognizing 3-d object shapes from range data. In: Proceedings of the IEEE Computer Society International Conference on Computer Vision 2003, vol. 2, pp. 1126–1133 (2003)Google Scholar
  9. 9.
    Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing visual features for multiclass and multiview object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(5), 854–869 (2007)CrossRefGoogle Scholar
  10. 10.
    van de Weijer, J., Schmid, C.: Coloring local feature extraction. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 334–348. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Doaa Hegazy
    • 1
  • Joachim Denzler
    • 1
  1. 1.Institute of Computer ScienceFriedrich-Schiller-University in JenaJenaGermany

Personalised recommendations