ArticleJournal of vision2022
Deep neural models for color classification and color constancy.
Article in Journal of vision, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 26 citations in OpenAlex.
- A Kernel-Aware Regularization Model for Chromatic-Robust Detection: Analysis of Grayscale-to-RGB Generalization.Journal of imaging · 2026Article
- LUF-net: A physically informed color calibration method for UAV RGB images based on exposure and irradiance information.Plant phenomics (Washington, D.C.) · 2026Article
- Deep neural networks trained for estimating reflectance and illumination achieve lightness constancy differently than human observers.Journal of vision · 2026Article
- Human-aligned evaluation of a pixel-wise DNN color constancy model.Frontiers in human neuroscience · 2026Article
- Article
- Color constancy mechanisms in virtual reality environments.Journal of vision · 2024Article
- Color constancy in real-world settings.Journal of vision · 2024Article
- Article
- Contrast sensitivity functions in autoencoders.Journal of vision · 2022Article
- Human color constancy based on the geometry of color distributions.Journal of vision · 2021Article
- Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Color constancy is our ability to perceive constant colors across varying illuminations. Here, we trained deep neural networks to be color constant and evaluated their performance with varying cues. Inputs to the networks consisted of two-dimensional images of simulated cone excitations derived from three-dimensional (3D) rendered scenes of 2,115 different 3D shapes, with spectral reflectances of 1,600 different Munsell chips, illuminated under 278 different natural illuminations. The models were trained to classify the reflectance of the objects. Testing was done with four new illuminations with equally spaced CIEL*a*b* chromaticities, two along the daylight locus and two orthogonal to it. High levels of color constancy were achieved with different deep neural networks, and constancy was higher along the daylight locus. When gradually removing cues from the scene, constancy decreased. Both ResNets and classical ConvNets of varying degrees of complexity performed well. However, DeepCC, our simplest sequential convolutional network, represented colors along the three color dimensions of human color vision, while ResNets showed a more complex representation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.