Uniform Color Spaces

Cheung, Vien

doi:10.1007/978-3-642-35947-7_14-2

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…We first construct both 2D and 3D embeddings and use properly scaled 3D coordinates to map each cell to a color space. We adopt the CIE L*a*b* (CIELAB) color space which, unlike RGB space, is perceptually uniform: i.e, it is designed such that points with similar distances are visually perceived as having similar color differences 47 . This procedure allows us to intuitively map the three-dimensional embedding of the ACTIONet graph into a colorspace that recapitulates its overall topology.…”

Section: De Novo Coloringmentioning

confidence: 99%

A multiresolution framework to characterize single-cell state landscapes

Mohammadi

Dávila-Velderrain

Kellis

2019

Preprint

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Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although a large number of methods and approaches exist, robustly identifying underlying cell states and their associations is still a major challenge; given the nonexclusive and dynamic influence of multiple unknown sources of variability, the existence of state continuum at the time-scale of observation, and the inevitable snapshot nature of experiments. As a way to address some of these challenges, here we introduce ACTIONet, a comprehensive framework that combines archetypal analysis and network theory to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet uses multilevel matrix decomposition and network reconstruction to simultaneously learn cell state patterns, quantify single-cell states, and reconstruct a reproducible structural representation of the transcriptional state space that is geometrically mapped to a color space. A color-enhanced quantitative view of cell states enables novel visualization, prediction, and annotation approaches. Using data from multiple tissues, organisms, and developmental conditions, we illustrate how ACTIONet facilitates the reconstruction and exploration of single-cell state landscapes.

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Section: De Novo Coloringmentioning

confidence: 99%

A multiresolution framework to characterize single-cell state landscapes

Mohammadi

Dávila-Velderrain

Kellis

2019

Preprint

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“…We introduced two quantitative parameters: 1) normalized luminance difference (NLD) and 2) color difference ( ) in the CIELUV uniform color space [45]- [49], in order to systematically investigate the luminance and color variation on the measurement grids. We used a commercial luminance meter (Konica Minolta Optics CA-1500 [50]) to measure the spatial distribution of luminance and chromaticity over the BLU at the normal angle and at the focusing distance.…”

Section: Resultsmentioning

confidence: 99%

“…We measured the reference chromaticity coordinates at the measuring point in the CIELUV uniform color space and denoted them as . The color difference ( ) was represented as the distance in the color coordinates similar to [45]- [49].…”

Section: Resultsmentioning

confidence: 99%

Effects of Light-Emitting Diode (LED) Configuration on Luminance and Color of an Edge-Lit Backlight Unit

Moon

2015

J. Display Technol.

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In this paper, we evaluated quantitative changes in the luminance and the color on the light-exit plane of an LED edge-lit backlight unit (BLU) by systematically controlling the optical properties of individual white LEDs. We placed a light absorbing film and red-green-blue filters to control the white intensity and the color of an individual LED string, respectively. Impacts of this 'defect' LED's optical properties were systematically monitored across the light-exit plane in terms of local distribution of the luminance and the color, overlaying prism films and diffusion film on a light guiding plate (LGP). The roles of prism films and diffusion film were also experimentally analyzed to assess their homogenizing capability for the luminance and the color with respect to the defect-like LEDs.

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The validated circular shape space: Quantifying the visual similarity of shape.

Li¹,

Liang²,

Lee³

et al. 2020

Journal of Experimental Psychology: General

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Subjective similarity holds a prominent place in many psychological theories, influencing diverse cognitive processes ranging from attention and categorization to memory and problem solving. Despite the known importance of subjective similarity, there are few resources available to experimenters interested in manipulating the visual similarity of shape, one common type of subjective similarity. Here, across seven validation iterations, we incrementally developed a stimulus space consisting of 360 shapes using a novel image-processing method in conjunction with collected similarity judgments. The result is the Validated Circular Shape space, the first Validated Circular Shape space comparable to the commonly used “color wheel”, whereby angular distance along a 2D circle is a proxy for visual similarity. This extensively validated resource is freely available to experimenters wishing to precisely manipulate the visual similarity of shape.

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Uniform Color Spaces

Cited by 3 publications

References 9 publications

A multiresolution framework to characterize single-cell state landscapes

A multiresolution framework to characterize single-cell state landscapes

Effects of Light-Emitting Diode (LED) Configuration on Luminance and Color of an Edge-Lit Backlight Unit

The validated circular shape space: Quantifying the visual similarity of shape.

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