scholarly journals Quantitative color profiling of images in a comparative framework using the R package colordistance

2018 ◽  
Author(s):  
Hannah Weller ◽  
Mark Westneat
Keyword(s):  
Profile Analysis ◽  
Color Space ◽  
Distance Matrix ◽  
R Package ◽  
Color Variation ◽  
Statistical Testing ◽  
Color Analysis ◽  
Color Histograms ◽  
Color Profile ◽  
Image Pixels

Color is a central aspect of biology, with important impacts on ecology and evolution. Organismal color may be adaptive or incidental, seasonal or permanent, species- or population-specific, or modified for breeding, defense or camouflage. Thus, measuring and comparing color among organisms provides important biological insights. However, color comparison is limited by color categorization methods, with few universal tools available for quantitative color profiling and comparison. We present a package of R tools for processing images of organisms (or other objects) in order to quantify color profiles, gather color trait data, and compare color palettes in a reproducible way. The package treats image pixels as 3D coordinates in “color space", producing a multidimensional color histogram for each image. Pairwise distances between histograms are computed using earth mover's distance or a combination of more conventional distance metrics. The user sets parameters for generating color histograms, and comparative color profile analysis is performed through pairwise comparisons to produce a color distance matrix for a set of images. The toolkit provided in the colordistance R package can be used for analyses involving quantitative color variation in organisms with statistical testing. We illustrate the use of colordistance with three biological examples: hybrid coloration in butterflyfishes; mimicry in wing coloration in Heliconius butterflies; and analysis of background matching in camouflaging flounder fish. The tools presented for quantitative color analysis may be applied to a broad range of questions in biology and other disciplines.

scholarly journals Quantitative color profiling of digital images with earth mover’s distance using the R package colordistance

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6398 ◽  
Author(s):  
Hannah I. Weller ◽  
Mark W. Westneat
Keyword(s):  
Color Space ◽  
Pairwise Comparison ◽  
Distance Matrix ◽  
R Package ◽  
Comparison Method ◽  
Earth Mover’S Distance ◽  
Color Analysis ◽  
Earth Mover's Distance ◽  
Color Similarity ◽  
Image Pixels

Biological color may be adaptive or incidental, seasonal or permanent, species- or population-specific, or modified for breeding, defense or camouflage. Although color is a hugely informative aspect of biology, quantitative color comparisons are notoriously difficult. Color comparison is limited by categorization methods, with available tools requiring either subjective classifications, or expensive equipment, software, and expertise. We present an R package for processing images of organisms (or other objects) in order to quantify color profiles, gather color trait data, and compare color palettes on the basis of color similarity and amount. The package treats image pixels as 3D coordinates in a “color space,” producing a multidimensional color histogram for each image. Pairwise distances between histograms are computed using earth mover’s distance, a technique borrowed from computer vision, that compares histograms using transportation costs. Users choose a color space, parameters for generating color histograms, and a pairwise comparison method to produce a color distance matrix for a set of images. The package is intended as a more rigorous alternative to subjective, manual digital image analyses, not as a replacement for more advanced techniques that rely on detailed spectrophotometry methods unavailable to many users. Here, we outline the basic functions of colordistance, provide guidelines for the available color spaces and quantification methods, and compare this toolkit with other available methods. The tools presented for quantitative color analysis may be applied to a broad range of questions in biology and other disciplines.

scholarly journals Quantitative color profiling of digital images with earth mover's distance using the R package colordistance

2018 ◽  
Author(s):  
Hannah Weller ◽  
Mark Westneat
Keyword(s):  
Color Space ◽  
Pairwise Comparison ◽  
Distance Matrix ◽  
R Package ◽  
Comparison Method ◽  
Earth Mover’S Distance ◽  
Color Analysis ◽  
Earth Mover's Distance ◽  
Color Similarity ◽  
Image Pixels

Biological color may be adaptive or incidental, seasonal or permanent, species- or population-specific, or modified for breeding, defense or camouflage. Although color is a hugely informative aspect of biology, quantitative color comparisons are notoriously difficult. Color comparison is limited by categorization methods, with available tools requiring either subjective classifications, or expensive equipment, software, and expertise. We present an R package for processing images of organisms (or other objects) in order to quantify color profiles, gather color trait data, and compare color palettes on the basis of color similarity and amount. The package treats image pixels as 3D coordinates in a “color space", producing a multidimensional color histogram for each image. Pairwise distances between histograms are computed using earth mover's distance, a technique borrowed from computer vision that compares histograms using transportation costs. Users choose a color space, parameters for generating color histograms, and a pairwise comparison method to produce a color distance matrix for a set of images. The package is intended as a more rigorous alternative to subjective, manual digital image analyses, not as a replacement for more advanced techniques that rely on detailed spectrophotometry methods unavailable to many users. Here, we outline the basic functions colordistance, provide guidelines for the available color spaces and quantification methods, and compare this toolkit with other available methods. The tools presented for quantitative color analysis may be applied to a broad range of questions in biology and other disciplines.

scholarly journals Quantitative color profiling of digital images with earth mover's distance using the R package colordistance

2018 ◽  
Author(s):  
Hannah Weller ◽  
Mark Westneat
Keyword(s):  
Color Space ◽  
Pairwise Comparison ◽  
Distance Matrix ◽  
R Package ◽  
Comparison Method ◽  
Earth Mover’S Distance ◽  
Color Analysis ◽  
Earth Mover's Distance ◽  
Color Similarity ◽  
Image Pixels

Biological color may be adaptive or incidental, seasonal or permanent, species- or population-specific, or modified for breeding, defense or camouflage. Although color is a hugely informative aspect of biology, quantitative color comparisons are notoriously difficult. Color comparison is limited by categorization methods, with available tools requiring either subjective classifications, or expensive equipment, software, and expertise. We present an R package for processing images of organisms (or other objects) in order to quantify color profiles, gather color trait data, and compare color palettes on the basis of color similarity and amount. The package treats image pixels as 3D coordinates in a “color space", producing a multidimensional color histogram for each image. Pairwise distances between histograms are computed using earth mover's distance, a technique borrowed from computer vision that compares histograms using transportation costs. Users choose a color space, parameters for generating color histograms, and a pairwise comparison method to produce a color distance matrix for a set of images. The package is intended as a more rigorous alternative to subjective, manual digital image analyses, not as a replacement for more advanced techniques that rely on detailed spectrophotometry methods unavailable to many users. Here, we outline the basic functions colordistance, provide guidelines for the available color spaces and quantification methods, and compare this toolkit with other available methods. The tools presented for quantitative color analysis may be applied to a broad range of questions in biology and other disciplines.

scholarly journals Coloration Characteristic and Population Genetic Analysis of Wild-Captured Giant Tiger Shrimp (Penaeus monodon) from Aceh Timur

2018 ◽  
Vol 23 (3) ◽  
pp. 123
Author(s):  
Indriatmoko Indriatmoko ◽  
Dimas A. Hedianto ◽  
Sari Budi Moria ◽  
Didik WH Tjahjo
Keyword(s):  
Penaeus Monodon ◽  
Color Space ◽  
Quantitative Information ◽  
Color Analysis ◽  
Population Genetic Analysis ◽  
Tiger Shrimp ◽  
16S Rdna Pcr ◽  
Aceh Province ◽  
Image Pixels ◽  
Space Data

Giant tiger shrimp (Penaeus monodon) has become a prime commodity in Indonesia which was produced by aquaculture and capture fisheries activities. Aceh Province, in this case mostly represented by Aceh Timur District, was well-known as the center of wild-captured-adult giant tiger shrimp. Several previous investigations had proved for its high-quality shrimp spawner in producing good eggs in quality and quantity under artificial spawning condition. Two main interesting points of wild giant tiger shrimp from Aceh Timur came from their coloration and population clusters. This report was aimed to provide that information pre-preliminary and highlighted quantitative information of coloration characteristic through RGB (Red Green Blue) and CIE Lab color space data analysis, as well as, 16S rDNA-PCR-RFLP genetic comparison among four population clusters in Aceh Timur Waters. The color analysis resulted in significant differences between wild-captured and pond-cultured giant tiger shrimp which produced R value 0.1524±0.0091 and 0.1268±0.0004, respectively. Total pixel analysis through L* a* b* color space has distinguished detailed differentiation between wild-captured and pond-cultured giant tiger shrimp acquired images. It is known that most of the wild-captured image pixels were concentrated in quadrant I (+a, +b) while pond-cultured in quadrant II (-a, +b) and III (-a, -b).Genotyping of represented samples from 4 population clusters, i.e. Aceh Tamiang, Langsa, Peudawa, and Julok produce 2 haplotype composite, AAA and AAB. Among 4 clusters, it was found that Julok has become the only cluster which has a different haplotype composite ratio (1:1) (D 0.0348, V 0,9501) from the others (4:1)(V 0.9504).

scholarly journals Implementation of the Omega (ω) Index to detect large-scale systematic cheating

2019 ◽  
Author(s):  
Alvin Vista
Keyword(s):  
Standardized Testing ◽  
Large Scale ◽  
Type I Error ◽  
R Package ◽  
Statistical Testing ◽  
System Level ◽  
Control Group ◽  
Type I ◽  
Data Contamination ◽  
Cheating Detection

Cheating detection is an important issue in standardized testing, especially in large-scale settings. Statistical approaches are often computationally intensive and require specialised software to conduct. We present a two-stage approach that quickly filters suspected groups using statistical testing on an IRT-based answer-copying index. We also present an approach to mitigate data contamination and improve the performance of the index. The computation of the index was implemented through a modified version of an open source R package, thus enabling wider access to the method. Using data from PIRLS 2011 (N=64,232) we conduct a simulation to demonstrate our approach. Type I error was well-controlled and no control group was falsely flagged for cheating, while 16 (combined n=12,569) of the 18 (combined n=14,149) simulated groups were detected. Implications for system-level cheating detection and further improvements of the approach were discussed.

scholarly journals profileR: An R package for profile analysis

2020 ◽  
Vol 5 (47) ◽  
pp. 1941
Author(s):  
Christopher Desjardins ◽  
Okan Bulut
Keyword(s):  
Profile Analysis ◽  
R Package

scholarly journals Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process

2021 ◽  
Author(s):  
Antoine Bichat ◽  
Christophe Ambroise ◽  
Mahendra Mariadassou
Keyword(s):  
Multiple Testing ◽  
R Package ◽  
Statistical Testing ◽  
Uhlenbeck Process ◽  
Explanatory Variables ◽  
New Associations ◽  
Penalized Estimation ◽  
Ultrametric Tree ◽  
Ornstein Uhlenbeck Process ◽  
The Mean

AbstractStatistical testing is classically used as an exploratory tool to search for association between a phenotype and many possible explanatory variables. This approach often leads to multiple testing under dependence. We assume a hierarchical structure between tests via an Ornstein-Uhlenbeck process on a tree. The process correlation structure is used for smoothing the p-values. We design a penalized estimation of the mean of the Ornstein-Uhlenbeck process for p-value computation. The performances of the algorithm are assessed via simulations. Its ability to discover new associations is demonstrated on a metagenomic dataset. The corresponding R package is available from https://github.com/abichat/zazou.

Plaque Color Analysis by the Conventional Yellow-Color Grading System and Quantitative Measurement Using LCH Color Space

2007 ◽  
Vol 20 (5) ◽  
pp. 324-334 ◽  
Author(s):  
KATSUKI OKADA ◽  
YASUNORI UEDA ◽  
JOTA OYABU ◽  
NOBUYUKI OGASAWARA ◽  
ATSUSHI HIRAYAMA ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Color Space ◽  
Grading System ◽  
Color Analysis ◽  
Color Grading ◽  
Yellow Color

scholarly journals Visual signal evolution along complementary color axes in four bird lineages

2019 ◽  
Author(s):  
Anand Krishnan ◽  
Avehi Singh ◽  
Krishnapriya Tamma
Keyword(s):  
Color Space ◽  
Dietary Factors ◽  
The Body ◽  
Color Variation ◽  
Visual Signals ◽  
Visual Signal ◽  
Relative Role ◽  
Chromatic Contrast ◽  
Present Evidence ◽  
Complementary Color

AbstractAnimal color patterns function in varied behavioral contexts including recognition, camouflage and even thermoregulation. The diversity of visual signals may be constrained by various factors, for example, dietary factors, and the composition of ambient environmental light (sensory drive). How have high-contrast and diverse signals evolved within these constraints? In four bird lineages, we present evidence that plumage colors cluster along a line in tetrachromatic color space. Additionally, we present evidence that this line represents complementary colors, which are defined as opposite sides of a line passing through the achromatic point (putatively for higher chromatic contrast). Finally, we present evidence that interspecific color variation over at least some regions of the body is not constrained by phylogenetic relatedness. Thus, we hypothesize that species-specific plumage patterns within these bird lineages evolve by swapping the distributions of a complementary color pair (or dark and light patches in one group, putatively representing an achromatic complementary axis). The relative role of chromatic and achromatic contrasts in discrimination may depend on the environment that each species inhabits.

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