Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks
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...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Optical Society of America
2023
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| Summary: | 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. |
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