Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Deficiencies of Computational Image Recognition in Comparison to Human Counterpart

  • Conference paper
  • First Online:
Proceedings of Seventh International Congress on Information and Communication Technology

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 447))

Abstract

The paper is concerned with the cases where machine-based image recognition fails to succeed and becomes inferior to human visual cognition. We consider the computational experiments on the set of specific images and speculate on the nature of these images that is perceivable only by natural intelligence. We deduce that image recognition and computer vision both based on machine learning or even more sophisticated AI models are unable to represent features of human vision due to the lack of tight coupling with the respective physiology.

WWW home page: https://www.researchgate.net/profile/Vladimir_Vinnikov.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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

References

  1. Kanizsa G (1955) Quasi-perceptional margins in homogenously stimulated fields. Rivista di Psicologia 49:7–30

    Google Scholar 

  2. Simmons S (1996) About the triangle Princeton.edu Website, Retrieved on 1 May 2012

    Google Scholar 

  3. Gregory RL (1997) Eye and brain. Princeton University Press, Princeton

    Google Scholar 

  4. Homan DD (2000) Visual intelligence: how we create what we see. W.W. Norton & Company

    Google Scholar 

  5. Koch C (2004) The quest for consciousness: a neurobiological approach. Roberts & Company Publishers

    Google Scholar 

  6. Norretranders T (1999) The user illusion: cutting consciousness down to size. Penguin

    Google Scholar 

  7. Mendola J, Dale A, Fischl B, Liu A, Tootell R (1999) The representation of illusory and real contours in human cortical visual areas revealed by functional magnetic resonance imaging. J Neurosci 19(19):8560–8572

    Article  Google Scholar 

  8. Knebel J, Murrah M (2012) Towards a resolution of conflicting models of illusory contour processing in humans. Neuroimage 59(3):2808–2817

    Article  Google Scholar 

  9. Sary Gy, Koteles K, Kaposvari P, Lenti L, Csifsak G, Franko E, Benedek G, Tompa T (2008) The representation of Kanizsa illusory contours in the monkey inferior temporal cortex. Eur J Neurosci 28(10):2137–2146

    Article  Google Scholar 

  10. Baker N, Erlikhman G, Kellman PJ, Lu H (2018) Deep convolutional networks do not perceive illusory vontours. In CogSci

    Google Scholar 

  11. Pang Z, O’May CB, Choksi B, VanRullen R (2021) Predictive coding feedback results in perceived illusory contours in a recurrent neural network. arXiv preprint arXiv:2102.01955

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, Higher School of Economics, Pokrovsky Blvd 11, Moscow, Russian Federation, 109028

    Vladimir Vinnikov

  2. Department of Information Security, Moscow Polytechnic University, Avtozavodskaya st., 16 building 4, Moscow, Russian Federation, 115280

    Ekaterina Pshehotskaya

Authors
  1. Vladimir Vinnikov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ekaterina Pshehotskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladimir Vinnikov .

Editor information

Editors and Affiliations

  1. Middlesex University, London, UK

    Xin-She Yang

  2. The University of Reading, Reading, UK

    Simon Sherratt

  3. JIS University, Kolkata, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vinnikov, V., Pshehotskaya, E. (2023). Deficiencies of Computational Image Recognition in Comparison to Human Counterpart. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 447. Springer, Singapore. https://doi.org/10.1007/978-981-19-1607-6_43

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-1607-6_43

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1606-9

  • Online ISBN: 978-981-19-1607-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation