Journal article
Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks
- Abstract:
- We modeled discrimination thresholds for object colors under different lighting environments [1]. Firstly we built models based on chromatic statistics, testing 60 models in total. Secondly we trained convolutional neural networks (CNNs), using 160,280 images labeled either by the ground-truth or by human responses. No single chromatic statistics model was sufficient to describe human discrimination thresholds across conditions, while human-response-trained CNNs nearly perfectly predicted human thresholds. Guided by region-of- interest analysis of the network, we modified the chromatic statistics models to use only the lower regions of the objects, which substantially improved performance.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 2.9MB, Terms of use)
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- Publisher copy:
- 10.1364/JOSAA.479986
Authors
- Publisher:
- Optical Society of America
- Journal:
- Journal of the Optical Society of America A More from this journal
- Volume:
- 40
- Issue:
- 3
- Pages:
- A149-A159
- Publication date:
- 2023-02-15
- Acceptance date:
- 2023-01-12
- DOI:
- EISSN:
-
1520-8532
- ISSN:
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1084-7529
- Language:
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English
- Keywords:
- Pubs id:
-
1321992
- Local pid:
-
pubs:1321992
- Deposit date:
-
2023-01-13
Terms of use
- Copyright holder:
- Optica Publishing Group
- Copyright date:
- 2023
- Rights statement:
- © 2023 Optica Publishing Group. Published by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
- Licence:
- CC Attribution (CC BY)
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