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Quantitative Analysis of a Bioplausible Model of Misperception of Slope in the Café Wall Illusion

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10118))

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Abstract

This paper presents a model explaining tilt illusion effect in the Café Wall pattern. In this geometric illusion, we perceive horizontal edges as tilted. We explain this as the result of innate retinal/gangliar visual processing of the pattern. Our bioplausible model is based on a simple early layer using Difference of Gaussian over simple ON-center and OFF-center receptive fields, with a quantification module replacing later layers of a Deep Neural Network. The experimental results show that this bioplausible filtering technique can explain the tilt illusion of the Café Wall pattern. Our statistical analysis of tilt provides a quantitative measurement and an empirically testable prediction for the degree of tilt. This shows that the Difference of Gaussian reveals cues for perception and clues about the illusions we perceive.

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Acknowledgement

Nasim Nematzadeh was supported by an Australian Postgraduate Award (APA) scholorship for her Ph.D.

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Authors and Affiliations

  1. CSEM, Flinders University, Adelaide, Australia

    Nasim Nematzadeh, David M. W. Powers & Trent Lewis

Authors
  1. Nasim Nematzadeh
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  2. David M. W. Powers
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  3. Trent Lewis
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Corresponding author

Correspondence to Nasim Nematzadeh .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Nematzadeh, N., Powers, D.M.W., Lewis, T. (2017). Quantitative Analysis of a Bioplausible Model of Misperception of Slope in the Café Wall Illusion. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_45

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  • DOI: https://doi.org/10.1007/978-3-319-54526-4_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54525-7

  • Online ISBN: 978-3-319-54526-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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