Skip to main content
SpringerLink
Log in

Using Wavelet Extraction for Haptic Texture Classification

  • Conference paper
Visual Informatics: Bridging Research and Practice (IVIC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5857))

Included in the following conference series:

Abstract

While visual texture classification is a widely-research topic in image analysis, little is known on its counterpart i.e. the haptic (touch) texture. This paper examines the visual texture classification in order to investigate how well it could be used for haptic texture search engine. In classifying the visual textures, feature extraction for a given image involving wavelet decomposition is used to obtain the transformation coefficients. Feature vectors are formed using energy signature from each wavelet sub-band coefficient. We conducted an experiment to investigate the extent in which wavelet decomposition could be used in haptic texture search engine. The experimental result, based on different testing data, shows that feature extraction using wavelet decomposition achieve accuracy rate more than 96%. This demonstrates that wavelet decomposition and energy signature is effective in extracting information from a visual texture. Based on this finding, we discuss on the suitability of wavelet decomposition for haptic texture searching, in terms of extracting information from image and haptic information.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Datta, R.: Image Retrieval: Ideas, Influences, and Trends of New Age. ACM Computing surveys 40(2),Article 5 (April 2008)

    Google Scholar 

  2. Conners, R.W., Harlow, C.A.: A Theoretical Comparison of Texture Algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 2, 204–222 (1980)

    Article  MATH  Google Scholar 

  3. Azencott, R.: Texture classification using windowed Fourier filters. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 148–153 (1997)

    Article  Google Scholar 

  4. Fountain, S.R.: Rotation invariant texture features from Gabor filters. In: Chin, R., Pong, T.-C. (eds.) ACCV 1998. LNCS, vol. 1352. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  5. Nikam, S.B., Agarwal, S.,: Wavelet energy signature and GLCM features-based fingerprint anti-spoofing. In: International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2008 (2008)

    Google Scholar 

  6. Pun, C.: Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5) (May 2003)

    Google Scholar 

  7. Shi, M.,: Hyperspectral Texture Recognition Using a Multiscale Opponent Representation. IEEE Transaction on Geoscience and Remote Sensing 41(5) (May 2003)

    Google Scholar 

  8. Yektaii, M., Bhattacharya, P.: Cumulative global distance for dimension reduction in handwritten digits database. In: Qiu, G., Leung, C., Xue, X.-Y., Laurini, R. (eds.) VISUAL 2007. LNCS, vol. 4781, pp. 216–222. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Mallat, S.: A theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. on Pattern Analysis and Machine Inteligence 11(7), 674–693 (1989)

    Article  MATH  Google Scholar 

  10. Daubechies, I.: Orthonormal Bases of Compactly Supported Wavelets. Comm. Pure and Applied Math. 41, 909–996 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  11. Cédric, V.: Generalized Daubechies Wavelet Families. IEEE Transactions on Signal Processing 55(9) (September 2007)

    Google Scholar 

  12. Farrell Michael, D.: On The Impact of PCA Dimmension Reduction for Hyperspectral Detection of Difficult Targets. IEEE Transaction on Geoscience and Remote Sensing Letters 2(2) (April 2005)

    Google Scholar 

  13. Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality Reduction by Learning an nvariant Mapping. In: IEEE Conf. Comp. Vision and Pattern Recog., pp. 1735–1742. IEEE Computer Society Press, Los Alamitos (2006)

    Google Scholar 

  14. Wall, S.: An Investigation of Temporal and Spatial Limitation of Haptic Interfaces. University of Glasgow (2004)

    Google Scholar 

  15. Klatzky, R.L., Lederman, S.J., Touch, I.A.F., Healy, R.W. (eds.): Experimental Psychology. In: Weiner, I.B. (ed.) Handbook of Psychology, vol. 4, Wiley, New York (2004)

    Google Scholar 

  16. Seungmoon, C., Tan, H.Z.: An Analysis of Perceptual Instability During Haptic Texture Rendering. In: 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2002)

    Google Scholar 

  17. Wu, J., Song, A., Zou, C.: A Novel Haptic Texture Display Based on Image Processing. In: IEEE International Conference on Robotics and Biomimetics (December 2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750, Tronoh, Perak, Malaysia

    Waskito Adi & Suziah Sulaiman

Authors
  1. Waskito Adi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suziah Sulaiman
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Halimah Badioze Zaman

  2. Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, CB3 0FD, Cambridge, United Kingdom

    Peter Robinson

  3. Communications and Signal Processing Group, Electrical and Electronic Engineering Department, Imperial College, SW7 2AZ, London, UK

    Maria Petrou

  4. School of Computing Science, Culture Lab, University of Newcastle upon Tyne, NE1 7RU, United Kingdom

    Patrick Olivier

  5. School of Computer Science and Information Technology, RMIT University, GPO Box 2476, 3001, Melbourne, Victoria, Australia

    Heiko Schröder

  6. Department of Computer Science and Information Engineering, Tamkang University, Tamkang University,, Taiwan

    Timothy K. Shih

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Adi, W., Sulaiman, S. (2009). Using Wavelet Extraction for Haptic Texture Classification. In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Schröder, H., Shih, T.K. (eds) Visual Informatics: Bridging Research and Practice. IVIC 2009. Lecture Notes in Computer Science, vol 5857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05036-7_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-05036-7_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05035-0

  • Online ISBN: 978-3-642-05036-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation