December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Modifications to Markov illuminance and reflectance improve its performance
Author Affiliations & Notes
  • Yuki Kobayashi
    Ritsumeikan University
    Japan Society for the Promotion of Science
  • Akiyoshi Kitaoka
    Ritsumeikan University
  • Footnotes
    Acknowledgements  The first author received financial support from the Japan Society for the Promotion of Science (grant number: 20J00606).
Journal of Vision December 2022, Vol.22, 3099. doi:
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      Yuki Kobayashi, Akiyoshi Kitaoka; Modifications to Markov illuminance and reflectance improve its performance. Journal of Vision 2022;22(14):3099.

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      © ARVO (1962-2015); The Authors (2016-present)

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Researchers have been seeking to develop a computational model that accounts for lightness/brightness perception in humans. Spatial filtering models have been accepted as a promising approach (e.g., Blakeslee & McCourt, 1999), and a novel lightness model, Markov illuminance and reflectance (MIR), was recently developed to address this issue (Murray, 2020). MIR is a Bayesian model that employs a probabilistic method called a Markov random field to represent observers’ prior beliefs about illuminance and reflectance. This model successfully accounts for some lightness phenomena that extant models fail to predict correctly (e.g., an effect of articulation on simultaneous lightness contrast). It thus appears a promising approach to this research topic. The present study seeks to improve the performance of MIR. First, we modified the inference process of MIR, implemented using the belief propagation method. Because some links in the Markov random field were left unused in the original implementation, we modified it to fully utilize all prepared links. This modification allowed the model to perform a more efficient search for solutions and improved its predictive performance for finely articulated images (i.e., checkerboard assimilation and pixel-wise noised images). Moreover, we also modified a constraint for X junctions. Although the original MIR considered all X junctions to be evidence of illumination edges, they are unlikely to cue an illumination change when two neighboring edges at an X junction show opposing contrast polarities. We find that a modification to the constraint regarding X junctions enabled the model to precisely identify a shadow on a checkerboard. The modified models also replicated all successful predictions using the original model that were reported by Murray (2020). The present study shows high potential for MIR and its ability to be extended to accommodate further improvements.


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