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1. Color Semantics in Human Cognition

   https://doi.org/10.1177/09637214231208189  

   Schloss, Karen B (February 2024, Current Directions in Psychological Science)

   People have associations between colors and concepts that influence the way they interpret color meaning in information visualizations (e.g., charts, maps, diagrams). These associations are not limited to concrete objects (e.g., fruits, vegetables); even abstract concepts, like sleeping and driving, have systematic color-concept associations. However, color-concept associations and color meaning (color semantics) are not the same thing, and sometimes they conflict. This article describes an approach to understanding color semantics called the color inference framework. The framework shows how color semantics is highly flexible and context dependent, which makes color an effective medium for communication. 

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2. The Color of Rivers

   https://doi.org/10.1029/2020GL088946  

   Gardner, John R.; Yang, Xiao; Topp, Simon N.; Ross, Matthew R. V.; Altenau, Elizabeth H.; Pavelsky, Tamlin M. (January 2021, Geophysical Research Letters)

   Abstract Rivers are among the most imperiled ecosystems globally, yet we do not have broad‐scale understanding of their changing ecology because most are rarely sampled. Water color, as perceived by the human eye, is an integrative measure of water quality directly observed by satellites. We examined patterns in river color between 1984 and 2018 by building a remote sensing database of surface reflectance, RiverSR, extracted from 234,727 Landsat images covering 108,000 kilometers of rivers > 60 m wide in the contiguous USA. We found 1) broad regional patterns in river color, with 56% of observations dominantly yellow and 38% dominantly green; 2) river color has three distinct seasonal patterns that were synchronous with flow regimes; 3) one third of rivers had significant color shifts over the last 35 years. RiverSR provides the first map of river color and new insights into macrosystems ecology of rivers. 

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3. Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images

   https://doi.org/10.1371/journal.pone.0251258  

   Liao, Shuting; Liu, Li-Yu; Chen, Ting-An; Chen, Kuang-Yu; Hsieh, Fushing (May 2021, PLOS ONE)

   Raja, Gulistan (Ed.)

   Our computational developments and analyses on experimental images are designed to evaluate the effectiveness of chemical spraying via unmanned aerial vehicle (UAV). Our evaluations are in accord with the two perspectives of color-complexity: color variety within a color system and color distributional geometry on an image. First, by working within RGB and HSV color systems, we develop a new color-identification algorithm relying on highly associative relations among three color-coordinates to lead us to exhaustively identify all targeted color-pixels. A color-dot is then identified as one isolated network of connected color-pixel. All identified color-dots vary in shapes and sizes within each image. Such a pixel-based computing algorithm is shown robustly and efficiently accommodating heterogeneity due to shaded regions and lighting conditions. Secondly, all color-dots with varying sizes are categorized into three categories. Since the number of small color-dot is rather large, we spatially divide the entire image into a 2D lattice of rectangular. As such, each rectangle becomes a collective of color-dots of various sizes and is classified with respect to its color-dots intensity. We progressively construct a series of minimum spanning trees (MST) as multiscale 2D distributional spatial geometries in a decreasing-intensity fashion. We extract the distributions of distances among connected rectangle-nodes in the observed MST and simulated MSTs generated under the spatial uniformness assumption. We devise a new algorithm for testing 2D spatial uniformness based on a Hierarchical clustering tree upon all involving MSTs. This new tree-based p -value evaluation has the capacity to become exact. 

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4. Seeing Photons in Color

   https://doi.org/10.1145/3592438  

   Ma, Sizhuo; Sundar, Varun; Mos, Paul; Bruschini, Claudio; Charbon, Edoardo; Gupta, Mohit (August 2023, ACM Transactions on Graphics)

   Megapixel single-photon avalanche diode (SPAD) arrays have been developed recently, opening up the possibility of deploying SPADs as generalpurpose passive cameras for photography and computer vision. However, most previous work on SPADs has been limited to monochrome imaging. We propose a computational photography technique that reconstructs high-quality color images from mosaicked binary frames captured by a SPAD array, even for high-dyanamic-range (HDR) scenes with complex and rapid motion. Inspired by conventional burst photography approaches, we design algorithms that jointly denoise and demosaick single-photon image sequences. Based on the observation that motion effectively increases the color sample rate, we design a blue-noise pseudorandom RGBW color filter array for SPADs, which is tailored for imaging dark, dynamic scenes. Results on simulated data, as well as real data captured with a fabricated color SPAD hardware prototype shows that the proposed method can reconstruct high-quality images with minimal color artifacts even for challenging low-light, HDR and fast-moving scenes. We hope that this paper, by adding color to computational single-photon imaging, spurs rapid adoption of SPADs for real-world passive imaging applications. 

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5. Mid- and Far-infrared Color–Color Relations within Local Galaxies

   https://doi.org/10.3847/1538-4357/ac558a  

   Gregg, Benjamin; Calzetti, Daniela; Heyer, Mark (March 2022, The Astrophysical Journal)

   Abstract We present an extensive archival analysis of a sample of local galaxies, combining multiwavelength data from GALEX, Spitzer, and Herschel to investigate “blue-side” mid-infrared (MIR) and “red-side” far-infrared (FIR) color–color correlations within the observed infrared spectral energy distributions. Our sample largely consists of the KINGFISH galaxies, with the important addition of a select few including NGC 5236 (M83) and NGC 4449. With data from the far-ultraviolet (∼0.15μm) through 500μm convolved to common angular resolution, we measure the photometry of kiloparsec-scale star-forming regions 36″ × 36″ in size. Star formation rates (SFRs), stellar masses, and metallicity distributions are derived throughout our sample. Focusing on thef₇₀/f₅₀₀“FIR” andf₈/f₂₄“MIR” flux density ratios (colors), we find that a subsample of galaxies demonstrate a strong IR color–color correlation within their star-forming regions, while others demonstrate uncorrelated colors. This division is driven by two main effects: (1) the local strength of star formation (SF) and (2) the metal content of the interstellar medium (ISM). Galaxies uniformly dominated by high surface densities of SF (e.g., NGC 5236) demonstrate strong IR color–color correlations, while galaxies that exhibit lower levels of SF and mixed environments (e.g., NGC 5457) demonstrate weaker or no correlation—explained by the increasing effect of varying ISM heating and metal content on the IR colors, specifically in the MIR. We find large dispersion in the SFR–L₈(8μm luminosity) relation that is traced by the metallicity distributions, consistent with extant studies, highlighting its problematic use as an SFR indicator across diverse systems/samples. 

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