Development of a Tool to Help Understand Color Spaces and Color Differences

2020 ◽  
Author(s):  
Kazuo Misue
Keyword(s):  
Color Spaces ◽  
Color Differences

scholarly journals QUANTITATIVE COMPARISON OF THE EFFICIENCY OF THE COLOR PROXIMITY CRITERIA OF OBJECTS WITH KNOWN SPECTRA

2021 ◽  
pp. 29-35
Author(s):  
Alexey Galuza ◽  
Olga Kostiuk ◽  
Alla Savchenko ◽  
Anastasiia Boiko
Keyword(s):  
Euclidean Distance ◽  
Color Spaces ◽  
Comparison Result ◽  
Color Differences ◽  
Subsequent Calculation ◽  
Expert Assessments ◽  
Standard Set ◽  
The Given ◽  
Original Approach

The work is devoted to the problem of comparing objects by color. The following statement of the problem is considered: among the set of objects it is necessary to find such an object, the color of which is most similar to the color of the given object. It is assumed that for each object only its spectrum (transmission, reflection, radiation) is known, which is an exhaustive characteristic of the color of the object. In addition, the spectrum of the radiation source is assumed to be known. The use of standard methods for determining color differences has shown that the problem does not have an unambiguous solution. Two approaches to its solution have been proposed: the first is based on the transition from the spectrum to color spaces with the subsequent calculation of the Euclidean distance, and the second is based on a direct comparison of the spectra as functional dependences of the intensity on the wavelength. Within each of the approaches, two criteria for the "similarity" of objects in color are proposed, and an original approach to assessing the effectiveness of these criteria is proposed. This approach is based on the use of expert assessments of the color proximity of glass samples with known transmission spectra from a standard set. For each sample from the set, experts selected the glass closest in color from the remaining ones, after which a generalized opinion of experts was formed. To obtain an assessment of the quality of each of the criteria, for each of them and for each test glass, the remaining samples were ranked in order of increasing color distance to the given test glass. After that, the results of the criteria were compared with the generalized opinion of experts. To make the comparison result "fuzzy", for each test glass it was proposed to consider a set of five glasses closest in color (for each of the criteria). The resulting estimates of the effectiveness of each of the criteria for a set of 89 glasses are obtained and an approach to the construction of more effective complex criteria is proposed.

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

Fusion Based Edge Detection and Segmentation of Color Spaces

2017 ◽  
Author(s):  
Prof. S. H. Jawale ◽  
Prof. A. B. Bavaskar
Keyword(s):  
Edge Detection ◽  
Color Spaces

Measurement of small color differences.

1982 ◽  
Vol 89 (3) ◽  
pp. 281-302 ◽  
Author(s):  
Brian A. Wandell
Keyword(s):  
Color Differences

Revalorized broccoli by-products and mustard improved quality during shelf life of a kale pesto sauce

2021 ◽  
pp. 108201322098310
Author(s):  
Noelia Castillejo ◽  
Ginés Benito Martínez-Hernández ◽  
Francisco Artés-Hernández
Keyword(s):  
Shelf Life ◽  
Antimicrobial Properties ◽  
Titratable Acidity ◽  
Soluble Solids ◽  
Antioxidant Compounds ◽  
Color Differences ◽  
Total Antioxidant ◽  
Sensory Analyses ◽  
Solids Content ◽  
By Products

The effect of revalorized Bimi leaves (B) and/or mustard (M) addition, as supplementary ingredients, to develop an innovative kale (K) pesto sauce was studied. Microbial, physicochemical (color, total soluble solids content -SSC-, pH and titratable acidity –TA-) and sensory quality were studied during 20 days at 5 °C. Bioactive compounds changes (total phenolics, total antioxidant capacity and glucoraphanin contents) were also monitored throughout storage. The high TA and pH changes in the last 6 days of storage were avoided in the K+B pesto when adding mustard, due to the antimicrobial properties of this brassica seed. SSC was increased when B + M were added to the K pesto, which positively masked the kale-typical bitterness. Mustard addition hardly change yellowness of the K pesto, being not detected in the sensory analyses, showing K+B+M pesto the lowest color differences after 20 days of shelf life. The addition of Bimi leaves to the K pesto enhanced its phenolic content while mustard addition did not negatively affect such total antioxidant compounds content. Finally, mustard addition effectively aimed to glucoraphanin conversion to its bioactive products. Conclusively, an innovative kale pesto supplemented with Bimi by-products was hereby developed, being its overall quality well preserved up to 20 days at 5 °C due to the mustard addition.

scholarly journals The Application of Machine Learning for Cultivar Discrimination of Sweet Cherry Endocarp

Agriculture ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Ewa Ropelewska
Keyword(s):  
Sweet Cherry ◽  
Color Space ◽  
Geometric Parameters ◽  
Geometric Features ◽  
Color Spaces ◽  
Discriminative Models ◽  
Sweet Cherries ◽  
Rgb Color Space ◽  
Cultivar Discrimination ◽  
Color Channels

The aim of this study was to evaluate the usefulness of the texture and geometric parameters of endocarp (pit) for distinguishing different cultivars of sweet cherries using image analysis. The textures from images converted to color channels and the geometric parameters of the endocarp (pits) of sweet cherry ‘Kordia’, ‘Lapins’, and ‘Büttner’s Red’ were calculated. For the set combining the selected textures from all color channels, the accuracy reached 100% when comparing ‘Kordia’ vs. ‘Lapins’ and ‘Kordia’ vs. ‘Büttner’s Red’ for all classifiers. The pits of ‘Kordia’ and ‘Lapins’, as well as ‘Kordia’ and ‘Büttner’s Red’ were also 100% correctly discriminated for discriminative models built separately for RGB, Lab and XYZ color spaces, G, L and Y color channels and for models combining selected textural and geometric features. For discrimination ‘Lapins’ and ‘Büttner’s Red’ pits, slightly lower accuracies were determined—up to 93% for models built based on textures selected from all color channels, 91% for the RGB color space, 92% for the Lab and XYZ color spaces, 84% for the G and L color channels, 83% for the Y channel, 94% for geometric features, and 96% for combined textural and geometric features.

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