Color Appearance Models

By M. Nezamabadi. J. Electronic Imaging, 2014

735 citations recorded by the source corpus.

Color science is a multidisciplinary field with broad applications in industries such as digital imaging, coatings and textiles, food, lighting, archiving, art, and fashion. Accurate definition and measurement of color appearance is a challenging task that directly affects color reproduction in such applications. Color Appearance Models addresses those challenges and offers insight into the preferred solutions. Extensive research on the human visual system (HVS) and color vision has been performed in the last century, and this book contains a good overview of the most important and relevant literature regarding color appearance models.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Social Psychology

View Paper: https://www.semanticscholar.org/paper/ef65035d6d88d8aa5d3851b1563868d847a093ce

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In Atomic and Molecular Physics, and Optics

In Computer Vision and Pattern Recognition

In Social Psychology

Citations in the corpus

SHOT: Unique signatures of histograms for surface and texture description (2014, methodology): “In particular, we have considered two metrics, known as CIE94 and CIE2000, that were defined by the CIE Commission in 1994 and 2000 respectively (for their definitions the reader is referred to [36]).”
Color psychology: effects of perceiving color on psychological functioning in humans. (2014, background): “In most experimental research, the most important of these attributes to attend to are hue, lightness, and chroma (Fairchild 2005).”
Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis (2017, background): “Although recent generative models suggest that realistic medical image generation is feasible by means of more complex techniques [9], typically basic linear transformations on the image geometry are performed, such as shifting, scaling or rotating.”
Revisiting the Red Effect on Attractiveness and Sexual Receptivity: No Effect of the Color Red on Human Mate Preferences (2016, methodology): “The color parameters for the red shirt (Lightness, Chroma, hue: LCh[54.2, 70.8, 28.7]) were obtained using a GretagMacBeth Eye-One Pro spectrophotometer using the CIELCh color model, which defines color in terms of the parameters lightness, chroma, and hue (Fairchild, 2013).”
Color Preference under LEDs with Diminished Yellow Emission (2014, background): “…difference rather than color [Islam and others 2013; Rea and Freyssinier 2013] because color perceptions might improve with increases in light level (for example, Bezold-Brucke and Hunt effects [Fairchild 2005]), as might color discrimination [Boyce and Simons 1977; Rea and Freyssinier 2008].”
Attentional Resource Allocation in Visuotactile Processing Depends on the Task, But Optimal Visuotactile Integration Does Not Depend on Attentional Resources (2016, background): “Importantly, given that humans can only foveate two visual degrees at a time (Fairchild, 2013), the visual search task is carried out only in the central two visual degrees of the screen in order to avoid any confounds due to visual scanning.”
What is the space of spectral sensitivity functions for digital color cameras? (2013, methodology): “Knowing camera spectral sensitivities is also useful for computational color constancy [2], i.”
A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis (2016, background): “The H index (Hue) that means the pure color (Fairchild 2005), and was the only positively correlated with Chl and Car, while S Fig.”