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Recognition of Household Objects by Service Robots Through Interactive and Autonomous Methods

  • Al Mansur
  • Katsutoshi Sakata
  • Yoshinori Kuno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)

Abstract

Service robots need to be able to recognize and identify objects located within complex backgrounds. Since no single method may work in every situation, several methods need to be combined. However, there are several cases when autonomous recognition methods fail. We propose several types of interactive recognition methods in those cases. Each one takes place at the failures of autonomous methods in different situations. We proposed four types of interactive methods such that robot may know the current situation and initiate the appropriate interaction with the user. Moreover we propose the grammar and sentence patterns for the instructions used by the user. We also propose an interactive learning process which can be used to learn or improve an object model through failures.

Keywords

Object Recognition Service Robot Gabor Feature Require Object Class Recognition 
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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References

  1. 1.
    Lowe, D.: Distinctive Image Features from Scale-invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Serre, T., Wolf, L., Poggio, T.: A New Biologically Motivated Framework for Robust Object Recognition. Ai memo, 2004-026, cbcl memo 243, MIT (2004)Google Scholar
  3. 3.
    Li, S.Z., et al.: Kernel Machine Based Learning for MultiView Face Detection and Pose Estimation. In: Eighth International Conference on Computer Vision, pp. 674–679 (2001)Google Scholar
  4. 4.
    Liu, C.: Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 572–581 (2004)CrossRefGoogle Scholar
  5. 5.
    Schölkopf, B., Smola, A.J., Muller, K.-R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10, 1299–1319 (1998)CrossRefGoogle Scholar
  6. 6.
    Intelligent Robotics and Communication Laboratories, http://www.irc.atr.jp/index.html
  7. 7.
    Hossain, M.A., Kurnia, R., Nakamura, A., Kuno, Y.: Interactive Object Recognition through Hypothesis Generation and Confirmation. IEICE Transactions on Information and Systems E89-D, 2197–2206 (2006)CrossRefGoogle Scholar
  8. 8.
    Hossain, M.A., Kurnia, R., Nakamura, A., Kuno, Y.: Interactive Object Recognition System for a Helper Robot Using Photometric Invariance. IEICE Transactions on Information and Systems E88-D, 2500–2508 (2005)CrossRefGoogle Scholar
  9. 9.
    Kurnia, R., Hossain, M.A., Nakamura, A., Kuno, Y.: Generation of Efficient and User-friendly Queries for Helper Robots to Detect Target Objects. Advanced Robotics 20, 499–517 (2006)CrossRefGoogle Scholar
  10. 10.
    Sakata, K., Kuno, Y.: Detection of Objects Based on Research of Human Expression for Objects. In: Symposium on Sensing Via Image Information CD ROM (in Japanese) (2007)Google Scholar
  11. 11.
    Dunn, D., Higgins, W.E.: Optimal Gabor Filters for Texture Segmentation. IEEE Transactions on Image Processing 4, 947–964 (1995)CrossRefGoogle Scholar
  12. 12.
    Jain, A.K., Farrokhnia, F.: Unsupervised Texture Segmentation Using Gabor Filters. Pattern Recognition 24, 1167–1186 (1991)CrossRefGoogle Scholar
  13. 13.
    Manjunath, B.S., Ma, W.Y.: Texture Features for Browsing and Retrieval of Image Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 837–842 (1996)CrossRefGoogle Scholar
  14. 14.
    Mansur, A., Hossain, M.A., Kuno, Y.: Integration of Multiple Methods for Class and Specific Object Recognition. In: International Symposium on Visual Computing Part I, pp. 841–849 (2006)Google Scholar
  15. 15.
    Mansur, A., Kuno, Y.: Integration of Multiple Methods for Robust Object Recognition. Accepted in: SICE Anual Conference, Kagawa, Japan (September 2007)Google Scholar
  16. 16.
    Hanafiah, Z.M., Yamazaki, C., Nakamura, A., Kuno, Y.: Human-Robot Speech Interface Understanding Inexplicit Utterances Using Vision. In: International Conference for Human-Computer Interaction, 1321-1324/CD-ROM Disc2 2p1321.pdf (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Al Mansur
    • 1
  • Katsutoshi Sakata
    • 1
  • Yoshinori Kuno
    • 1
  1. 1.Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, Saitama 338-8570Japan

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