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

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 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 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).

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

2020 ◽  
Author(s):  
Cameron Hargreaves ◽  
Matthew Dyer ◽  
Michael Gaultois ◽  
Vitaliy Kurlin ◽  
Matthew J Rosseinsky
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Inorganic Crystal Structure Database ◽  
Earth Mover’S Distance ◽  
Chemical Similarity ◽  
Earth Mover's Distance ◽  
Neighbor Search ◽  
The Earth ◽  
Binary Compounds

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

scholarly journals Sustainable Business Model Innovations in the Value Uncaptured Manufacturing Industry: Fitting Gains—Gain Creators

2021 ◽  
Vol 13 (10) ◽  
pp. 5647
Author(s):  
Burhan ◽  
Udisubakti Ciptomulyono ◽  
Moses Singgih ◽  
Imam Baihaqi
Keyword(s):  
Business Model ◽  
Environmental Quality ◽  
Manufacturing Industry ◽  
Design Method ◽  
Pairwise Comparison ◽  
Comparison Method ◽  
Value Proposition ◽  
Sustainable Business ◽  
Sustainability Index ◽  
Index Matrix

Increased manufacturing activity has an impact on environmental quality degradation. Waste generated from manufacturing activities is one of the causes. Previous studies have referred to this waste as value uncaptured. Minimizing value uncaptured is a solution to improve environmental quality. This study aims to reduce value uncaptured by converting it into value captured. This process requires a value proposition design approach because of its advantages. One of the advantages of this approach is that it can improve existing or future products/services. To do so, this research uses a case study of a furniture company. To implement a converting process, a sustainable business model is proposed to solve this problem. This business model combines several methods: value proposition design, house of value and the product sustainability index matrix. Recently, the existing value proposition problem-solving has been using the value proposition design method. This research proposed implementing a house of value to replace the fitting process. The questionnaire is developed to obtain various value uncaptured in the company. To the weight of the value uncaptured, this research utilized the pairwise comparison method. Then, the weights could represent the importance of jobs. Based on the highest weight of these jobs, the alternative gains would be selected. To provide the weight of the gain creators and value captured, the house of value method is developed. Referring to three pillars of sustainability, the value captured should be considered. This research proposed implementing a product sustainability index which in turn produces eco-friendly products. This study produces “eco-friendly products” as sustainability value captured. The sustainability business model could be an alternative policy to minimize the existence of value uncaptured.

Digital color analysis for colorimetric signal processing: towards an analytically justified choice of acquisition technique and color space

2021 ◽  
pp. 130274
Author(s):  
Nikolai Yu. Tiuftiakov ◽  
Andrey V. Kalinichev ◽  
Nadezhda V. Pokhvishcheva ◽  
Maria A. Peshkova
Keyword(s):  
Signal Processing ◽  
Color Space ◽  
Color Analysis ◽  
Acquisition Technique
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