An analysis of spectral similarity measures

2021 ◽  
Vol 2021 (29) ◽  
pp. 300-305
Author(s):  
Mirko Agarla ◽  
Simone Bianco ◽  
Luigi Celona ◽  
Raimondo Schettini ◽  
Mikhail Tchobanou
Keyword(s):  
Correlation Coefficient ◽  
Linear Correlation ◽  
Rank Order ◽  
Source Code ◽  
Color Space ◽  
Similarity Measures ◽  
Similarity Metrics ◽  
Spectral Measures ◽  
Spectral Similarity ◽  
Different Color

In this paper we analyze the most used measures for the assessment of spectral similarity of reflectance and radiance signals. First of all we divide them in five groups on the basis of the type of errors they measure. We proceed analyzing their mathematical definition to identify unintended behaviors and types of errors they are blind to. Then exploiting the Munsell atlas we analyze the correlation between metrics in terms of both Pearson's Linear Correlation Coefficient (PLCC) and Spearman's Rank Order Correlation Coefficient (SROCC). Finally we analyze the behaviour of the selected metrics with respect to two different color properties: the Chroma and the Lightness computed in the CIE L* a* b* color space. The source code of the spectral measures considered is available at the following link: <ext-link ext-link-type="url" xlink:href="https://celuigi.github.io/spectral-similarity-metrics-comparison/">https://celuigi.github.io/spectral-similarity-metrics-comparison/</ext-link>.

scholarly journals RGB Image Color Space Transformations

2010 ◽  
Author(s):  
Robert Tamburo
Keyword(s):  
Source Code ◽  
Color Space ◽  
Color Spaces ◽  
Different Color ◽  
Rgb Image

This paper describes a set of pixel accessors that transform RGB pixel values to a different color space. Accessors for the HSI, XYZ, Yuv, YUV, HSV, Lab, Luv, HSL, CMY, and CMYK color spaces are provided here. This paper is accompanied with source code for the pixel accessors and test, test images and parameters, and expected output images.Note: Set() methods are incorrect. Will provide revision by 12.17.2010.

scholarly journals Marine Oil Slick Detection Based on Multi-Polarimetric Features Matching Method Using Polarimetric Synthetic Aperture Radar Data

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5176
Author(s):  
Guannan Li ◽  
Ying Li ◽  
Bingxin Liu ◽  
Peng Wu ◽  
Chen Chen
Keyword(s):  
Computational Complexity ◽  
Synthetic Aperture Radar ◽  
Oil Spill ◽  
Similarity Measures ◽  
Synthetic Aperture ◽  
Matching Method ◽  
Polarimetric Synthetic Aperture Radar ◽  
Spectral Similarity ◽  
Oil Spill Detection ◽  
Aperture Radar

Polarimetric synthetic aperture radar is an important tool in the effective detection of marine oil spills. In this study, two cases of Radarsat-2 Fine mode quad-polarimetric synthetic aperture radar datasets are exploited to detect a well-known oil seep area that collected over the Gulf of Mexico using the same research area, sensor, and time. A novel oil spill detection scheme based on a multi-polarimetric features model matching method using spectral pan-similarity measure (SPM) is proposed. A multi-polarimetric features curve is generated based on optimal polarimetric features selected using Jeffreys–Matusita distance considering its ability to discriminate between thick and thin oil slicks and seawater. The SPM is used to search for and match homogeneous unlabeled pixels and assign them to a class with the highest similarity to their spectral vector size, spectral curve shape, and spectral information content. The superiority of the SPM for oil spill detection compared to traditional spectral similarity measures is demonstrated for the first time based on accuracy assessments and computational complexity analysis by comparing with four traditional spectral similarity measures, random forest (RF), support vector machine (SVM), and decision tree (DT). Experiment results indicate that the proposed method has better oil spill detection capability, with a higher average accuracy and kappa coefficient (1.5–7.9% and 1–25% higher, respectively) than the four traditional spectral similarity measures under the same computational complexity operations. Furthermore, in most cases, the proposed method produces valuable and acceptable results that are better than the RF, SVM, and DT in terms of accuracy and computational complexity.

scholarly journals A Novel Approach for Comparison of Reception Performance in a Technique Test and Small-Sided Games

Sports ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 66
Author(s):  
Arne Sørensen ◽  
Vidar Sørensen ◽  
Terje Dalen
Keyword(s):  
Correlation Coefficient ◽  
Rank Order ◽  
Technical Skills ◽  
Quality Score ◽  
Soccer Players ◽  
Moderate Correlation ◽  
Technical Aspects ◽  
Youth Soccer ◽  
Novel Approach

The purpose of this study was to evaluate the correlation between soccer players’ performance of receptions of passes in tests of both isolated technical skills and more match-realistic situations in small-sided games (SSGs). In addition, this study investigated whether the involvement in SSGs (number of receptions) correlated with the quality of receptions in the respective SSGs. The participants were 13 male outfield youth soccer players from teams in the first division of the regional U18 league. The quality of receptions was scored by educated coaches according to set criteria of performance. Statistical analyses of correlations were determined using Spearman’s rank-order correlation coefficient (rs). The main results were (1) a significant correlation in the quality of ball reception between 4vs1 SSGs and 5vs5 SSGs (rs = −0.61, p < 0.01) and (2) a trend towards moderate correlation between the quality of ball reception using a ball projection machine and 5vs5 SSGs (rs = −0.48, p = 0.10). (3) A significant correlation was found between the number of receptions in 5vs5 SSGs and the quality score of receptions in 5vs5 SSGs (rs = −0.70, p < 0.01). The trend towards moderate correlations between 5vs5 SSGs and the isolated technical reception test could imply the importance of training in the technical aspects of ball reception. Moreover, it seems as though the players with the best reception performance are the players who are most involved in SSGs, that is, having the most receptions.

scholarly journals What we talk about when we talk about color

2020 ◽  
Author(s):  
Colin R. Twomey ◽  
Gareth Roberts ◽  
David Brainard ◽  
Joshua B. Plotkin
Keyword(s):  
Visible Light ◽  
Color Space ◽  
Color Perception ◽  
Geographic Location ◽  
Cross Cultural ◽  
Cultural Variation ◽  
Inference Algorithm ◽  
Color Categories ◽  
New Directions ◽  
Different Color

Names for colors vary widely across languages, but color categories are remarkably consistent [1–5]. Shared mechanisms of color perception help explain consistent partitions of visible light into discrete color vocabularies [6–10]. But the mappings from colors to words are not identical across languages, which may reflect communicative needs – how often speakers must refer to objects of different color [11]. Here we quantify the communicative needs of colors in 130 different languages, using a novel inference algorithm. Some regions of color space exhibit 30-fold greater demand for communication than other regions. The regions of greatest demand correlate with the colors of salient objects, including ripe fruits in primate diets. Using the mathematics of compression we predict and empirically test how languages map colors to words, accounting for communicative needs. We also document extensive cultural variation in communicative demands on different regions of color space, which is partly explained by differences in geographic location and local biogeography. This account reconciles opposing theories for universal patterns in color vocabularies, while opening new directions to study cross-cultural variation in the need to communicate different colors.

Comparative Experiment of Color Reproduction Methods

2014 ◽  
Vol 1051 ◽  
pp. 967-970
Author(s):  
Qi Jia ◽  
Xu Liang Lv ◽  
Wei Dong Xu ◽  
Jiang Hua Hu ◽  
Xian Hui Rong
Keyword(s):  
Polynomial Regression ◽  
Rapid Development ◽  
Color Space ◽  
Digital Camera ◽  
Color Difference ◽  
High Tech ◽  
Comparative Experiment ◽  
Color Reproduction ◽  
Different Color ◽  
Time Image

Digital camera which has the advantage of real-time image transferring and easily processing is more and more widely used in the packaging and printing industry with the rapid development of high-tech electronics industry. However, the color in digital camera is not accurate which affect the application. To minimize the color difference between the color in the digital camera and the real color, the color reproduction methods is developing. The field comparative experiment is carried out to compare the performance of color reproduction methods, such as polynomial regression algorithm in different color space, and color checker passport. The results show that fourth order polynomial regression color reproduction in XYZ color space has the best performance.

A selection of remarks on the measurement of correlations between variables of a panel data structure

2021 ◽  
Vol 66 (5) ◽  
pp. 7-25
Author(s):  
Mieczysław Kowerski ◽  
Jarosław Bielak
Keyword(s):  
Panel Data ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Stock Exchange ◽  
Panel Data Analysis ◽  
Correlation Coefficients ◽  
Data Dependencies ◽  
Construction Companies ◽  
Advantages And Disadvantages ◽  
Warsaw Stock Exchange

Many articles featuring panel data modelling tend to begin their considerations with an introduction of the Pearson linear correlation coefficients matrix between the analysed variables. The aim of the article is to prove such an approach unsuitable in the analysis of panel data dependencies. Instead, an attempt has been made to propose a more appropriate measure – a correlation coefficient between the empirical and fitted values of the dependent variable of the estimated panel model (with fixed or random effects) in relation to the variable whose dependency towards the dependent variable is being studied. Pearson’s linear correlation coefficient does not reflect the basic advantage of panel data, which is the ability to provide information about the dependencies of the studied phenomena simultaneously in time and space. The fact that one observation relates to object i during period t and another to object j during period t + 1 is irrelevant for the calculation of the coefficient. Pearson’s coefficient, however, can be used when conducting sub-calculations in panel data analysis. The presented considerations have been illustrated by the calculations of the relationships between the structure of capital and the profitability and size of 17 construction companies listed on the Warsaw Stock Exchange in the years 2009–2018 (170 observations) which created a balanced panel. A specification of the advantages and disadvantages of the proposed solution was formulated on the basis of the calculations.

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