Color constancy.

By D. Foster. Vision Research, 2011

699 citations recorded by the source corpus.

Subfields: History

View Paper: https://www.semanticscholar.org/paper/1f0e4614fd17f98b0f157e6bdc1e3ba9f9c47dce

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In History

Citations in the corpus

Predictive Processing: A Canonical Cortical Computation (2018, positive): “In visual illusions, we see color where there is none, simply because we know objects rarely change color (Foster, 2011), or miss things that happen right in front of our eyes (Simons and Chabris, 1999).”
Computational Color Constancy: Survey and Experiments (2011, neutral): “Human vision has the natural tendency to correct for the effects of the color of the light source, e.g., [4]–[8], but the mechanism that is involved with this ability is not yet fully understood.”
Emerging From Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer (2017, background): “For underwater image color correction, traditional color constancy methods ( e.g. , Gray World [20], Max RGB [21], White Balance [22], Shades-of-Grey [23], and et al. ) and their variations are usually employed.”
Color Constancy by Learning to Predict Chromaticity from Luminance (2015, background): “While pschophysical experiments have demonstrated that the human visual system is remarkably successful at achieving color constancy [1], it remains a challenging task computationally.”
Illumination Invariant Imaging : Applications in Robust Vision-based Localisation , Mapping and Classification for Autonomous Vehicles (2014, background): “In a similar vein, research in the field of colour constancy [7] has produced a number of”
Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light (2019, methodology): “The spatial domain methods include histogram equalization [27], the Gray World algorithm [28], contrast limited adaptive histogram equalization (CLAHE) [2], multi-scale retinex with color restore [1], automatic white balance [29], and color constancy [30], [31].”
Multiscale Retinex (2014, background): “One of such assumptions is the gray world assumption which states that given an image with sufficient amount of color variations, the average value of the red, green and blue components of the image should average out to a common gray value [3], [4].”