Skip to main content
SpringerLink
Log in

Cognition Theory Based Performance Characterization in Computer Vision

  • Conference paper
Book cover Advanced Concepts for Intelligent Vision Systems (ACIVS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3708))

  • 1155 Accesses

Abstract

It is very difficult to evaluate the performance of computer vision algorithms at present. We argue that visual cognition theory can be used to challenge this task. In this paper, we first illustrate why and how to use vision cognition theory to evaluate the performance of computer vision algorithms. Then from the perspective of computer science, we summarize some of important assumptions of visual cognition theory. Finally, some cases are introduced to show effectiveness of our methods.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Haralick, R.M.: Computer Vision Theory: The Lack Thereof. Computer Vision Graphics and Image Processing 36(2), 272–286 (1986)

    Article  Google Scholar 

  2. Haralick, R.M.: Performance Characterization in Computer Vision. Computer Vision, Graphics and Image Processing: Image Understanding 60(2), 245–249 (1994)

    Article  Google Scholar 

  3. Haralick, R.M.: Comments on Performance Characterization Replies. Computer Vision, Graphics, and Image Processing: Image Understanding 60(2), 264–265 (1994)

    Article  Google Scholar 

  4. Foerstner, W.: 10 Pros and Cons Against Performance Characterization of Vision Algorithms. In: Proc. ECCV Workshop on Performance Characteristics of Vision Algorithms (April 1996)

    Google Scholar 

  5. Thacker, N.A.: Using Quantitative Statistics for the Construction of Machine Vision Systems. In: Photonics and Imaging 2002, Keynote presentation given to Optoelectronics (September 2002)

    Google Scholar 

  6. Heath, M., Sarkar, S., et al.: A Robust Visual Method for Assessing the Relative Performance of Edge Detection Algorithms. IEEE Trans. PAMI 19(12), 1338–1359 (1997)

    Google Scholar 

  7. Müller, H., Müller, W., et al.: Performance Evaluation in Content–Based Image Retrieval: Overview and Proposals. Pattern Recognition Letters 22(5), 593–601 (2001)

    Article  MATH  Google Scholar 

  8. Shin, M.C., Goldgolf, D.B., Bowyer, K.W.: Comparison of Edge Detector Performance Through Use in an Object Recognition Task. Computer Vision and Image Understanding 84, 160–178 (2001)

    Article  MATH  Google Scholar 

  9. McCane, B.: On Benchmarking Optical Flow. Computer Vision and Image Understanding 84, 126–143 (2001)

    Article  MATH  Google Scholar 

  10. Cinque, L., Guerra, C., Levialdi, S.: Reply On the Paper by R.M. Haralick. Computer Vision, Graphics, and Image Processing: Image Understanding 60(2), 250–252 (1994)

    Article  Google Scholar 

  11. Bowyer, K.W., Phillips, P.J.: Overview of Work in Empirical Evaluation of Computer Vision Algorithms. In: Empirical Evaluation Techniques in Computer Vision. IEEE Computer Press, Los Alamitos (1998)

    Google Scholar 

  12. Poggio, T., et al.: Computational Vision and Regularization Theory. Nature 317(26), 314–319 (1985)

    Article  Google Scholar 

  13. Marr, D.: Vision. Freeman, New York (1982)

    Google Scholar 

  14. Rock: Perception, Scientific American Books, Inc. (1984)

    Google Scholar 

  15. Gregory, R.L.: Eye and Brain. Princeton University Press, Princeton (1997)

    Google Scholar 

  16. Biederman: Recognition-by-Components: A Theory of Human Image Understanding. Psychological Review 94, 115–147 (1987)

    Article  Google Scholar 

  17. Koffka, K.: Principle of Gestalt Psychology. Harcourt Brace Jovanovich Company (1935)

    Google Scholar 

  18. Zhang, M.: Psychology of Visual Cognition. Normal University Press, East China (1991)

    Google Scholar 

  19. Rock, The Logic of Perception. MIT Press, Cambridge (1983)

    Google Scholar 

  20. Sobel, D.M., et al.: Children’s causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. Cognitive Science 28, 303–333 (2004)

    Google Scholar 

  21. Coad, P., Yourdon, E.: Object-Oriented Analysis. Yourdon Press, New York (1990)

    Google Scholar 

  22. Horn, B.K.P., et al.: Determining Optical Flow. Artificial Intelligence 17, 185–203 (1981)

    Article  Google Scholar 

  23. Verr, et al.: Motion Field and Optical Flow: Qualitative Properties. IEEE Trans. PAMI 11, 490–498 (1989)

    Google Scholar 

  24. Itti, L., Koch, C., Neibur, E.: A Model of Saliency-based Visual Attention for Rapid Scene Analysis. IEEE Trans. PAMI 20(11) (1998)

    Google Scholar 

  25. Navalpakkam, V., Itti, L.: A Goal Oriented Attention Guidance Model. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 453–461. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  26. Oliva, A., Torralba, M.: Top-down Control of Visual Attention in Object Detection. In: International Conference on Image Processing (2003)

    Google Scholar 

  27. Zhang, Y.J.: Content-based Visual Information Retrieval. Science Press, Beijing (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Computer Science & Technology, Beijing Jiaotong Univ., Beijing, 100044, China

    Wu Aimin, Xu De & Yang Xu

  2. Beijing Key Lab of Intelligent Telecommunications Software and Multimedia, Beijing Univerisity of Posts and Communications, Beijing, 100876, China

    Wu Aimin

  3. Dongying Vocational College, Shandong, 257091, China

    Nie Zhaozheng

Authors
  1. Wu Aimin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xu De
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nie Zhaozheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. DGA/D4S/MRIS, 94114, Arcueil, France

    Jacques Blanc-Talon

  2. Ghent University, 9000, Gent, Belgium

    Wilfried Philips

  3. Wireless Technologies Lab, CSIRO ICT Centre, NSW 2122, Marsfield, Australia

    Dan Popescu

  4. University of Antwerp, 2610, Wilrijk, Belgium

    Paul Scheunders

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Aimin, W., De, X., Zhaozheng, N., Xu, Y. (2005). Cognition Theory Based Performance Characterization in Computer Vision. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_27

Download citation

  • DOI: https://doi.org/10.1007/11558484_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29032-2

  • Online ISBN: 978-3-540-32046-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation