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

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 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 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 Detection of Cardiac Tissues using K-means Analysis Methods in Nuclear Medicine Images

2021 ◽  
Vol 9 (A) ◽  
pp. 1272-1276
Author(s):  
Yousif Abdallah
Keyword(s):  
Image Processing ◽  
Nuclear Medicine ◽  
Nuclear Cardiology ◽  
Detection Rate ◽  
Color Space ◽  
Heart Tissue ◽  
Color Analysis ◽  
Cardiac Tissues ◽  
Analysis Methods ◽  
Cardiac Disorders

BACKGROUND: Nuclear cardiology uses to diagnose the cardiac disorders such as ischemic and inflammation disorders. In cardiac scintigraphy, unraveling closely adjacent tissues in the image are challenging issue. AIM: The aim of the study is to detect of cardiac tissues using K-means analysis methods in nuclear medicine images. This study also aimed to reduce the existence of fleck noise that disturbs the contrast and make its analysis more difficult. METHODS: Thus, digital image processing uses to increase the detection rate of myocardium easily using its color-based algorithms. In this study, color-based K-means was used. The scintographs were converted into color space presentation. Then, each pixel in the image was segmented using color analysis algorithms. RESULTS: The segmented scintograph was displayed in distinct fresh image. The proposed technique defines the myocardial tissues and borders precisely. Both exactness rate and recall reckoning were calculated. The results were 97.3 + 8.46 (p > 0.05). CONCLUSION: The proposed technique offered recognition of the heart tissue with high exactness amount.

scholarly journals Early Smoke Detection on Video Using Wavelet Energy

2017 ◽  
Vol 2 (2) ◽  
pp. 79 ◽  
Author(s):  
Muhammad Zulfiqar Shafar ◽  
Tjokorda Agung Budi Wirayuda ◽  
Febryanti Sthevanie
Keyword(s):  
Spatial Analysis ◽  
Video Processing ◽  
Detection System ◽  
Color Space ◽  
Temporal Analysis ◽  
Processing Technique ◽  
Color Analysis ◽  
Smoke Detection ◽  
Wavelet Energy ◽  
Spatio Temporal

<p>Most of the smoke detection system these days still using sensors that have to receive specific particles before it could give a warning. But, this system takes some time to react and quite difficult to place in spacious room or the outdoor. To overcome this, there is some research that build smoke detection system using many kind video processing technique that could provide early warning. In this research, wavelet energy was used to detect smoke in the video.  To determine candidate blocks in a frame that contain smoke, this research performed background subtraction and color analysis based on HSV color space. Then implementing spatial analysis and spatio-temporal analysis by using wavelet energy method and accumulative motion orientation to detect the smoke. This system using combination of dataset from previous research [1], downloaded from various sources and self-made dataset. Based on testing process using those dataset, this system reaches 91.05% accuracy for block-level and 72.22% accuracy for frame-level.</p><strong>Keywords: </strong>Accumulative motion orientation, smoke detection, spatial analysis, spatio-temporal analysis, video processing, wavelet energy

Relationship between color and physico-chemical properties of cashew apple (Anacardium occidentale L.) at different days of storage

2021 ◽  
Vol 74 (2) ◽  
Author(s):  
Alberto José Luengo Fereira ◽  
Josué David Hernández-Varela
Keyword(s):  
Image Analysis ◽  
Nutritional Value ◽  
Color Space ◽  
Chemical Properties ◽  
Anacardium Occidentale ◽  
Maturity Stage ◽  
Color Analysis ◽  
Cashew Nut ◽  
Physico Chemical ◽  
Cashew Apple

Cashew is a fruiting specimen exported from different countries, including Venezuela, due to its high nutritional value and unique taste. However, only the fruit (cashew nut) is considered the most used part for consumption while the pseudo-fruit (cashew apple) is discarded due to its strong taste and smell. This study is based on the changes that occur in the physical and chemical composition of the pseudo-fruit of cashew (Anacardium occidentale L.) in different days of storage (0, 3, 6, 9, 12) related to the color analysis of the pseudo-fruit. Image analysis was performed using the CIELab color space, which revealed different maturity stages for samples from day 0 to day 12. Nevertheless, antioxidant activity refers to ascorbic acid, and polyphenols content showed a degradation before day 6 of storage. These results prove that cashew apples can be stored for long-term at room temperature (25 °C), but the color and physico-chemical properties suffer some changes decreasing their nutritional value after day 6 of storage. A correlation between image analysis and chemical parameters can be used to evaluate the optimal maturity stage in samples.

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.

Sign in / Sign up

Export Citation Format

Share Document