Análisis de la consistencia de bases de datos de diferencias de color

La medición y especificación precisa del color, así como la medida de las diferencias de color entre dos pares de muestras son facetas de suma importancia en diversos ámbitos como el sector industrial, textil, agrícola o en el ámbito de la salud. Esta relevancia se acentúa especialmente en aquellos campos donde el color no solo es un atributo, sino que agrega un valor significativo al producto final. En medicina, la medición del color puede ser esencial para detectar cambios sutiles en la piel o los tejidos, lo que podría indicar problemas de salud. Por ejemplo, en dermatología, la evaluación del color de lunares o lesiones cutáneas puede ayudar a identificar posibles signos de cáncer de piel. En este trabajo, se ha realizado un análisis exhaustivo de varias bases de datos de diferencias de color, utilizando técnicas de análisis de datos mediante lógica difusa. El objetivo final de este análisis ha sido identificar parejas de colores inconsistentes en comparación con otras parejas, con el fin de mejorar la calidad y la consistencia de las bases de datos iniciales. Para lograrlo, se ha implementado una metodología que se basa en la detección de discrepancias entre las diferencias de color visualmente percibidas y las diferencias de color calculadas en comparación con el resto de datos. Se ha explorado una variedad de umbrales, evaluando cómo los diferentes niveles de tolerancia afectan a la detección de inconsistencias. El resultado de este trabajo es la identificación, análisis y eliminación de parejas de colores que se consideran inconsistentes en distintas bases de datos utilizando algoritmos desarrollados en Python. Esta depuración de datos contribuye significativamente a mejorar la calidad de las bases de datos de diferencias de color, lo que a su vez tiene un impacto positivo en diversas aplicaciones en las que la precisión del color es fundamental. Estas bases de datos desempeñan un papel fundamental en el desarrollo y evaluación de nuevas fórmulas de diferencia de color. Su objetivo es lograr que los resultados de estas fórmulas se ajusten de manera más precisa a la percepción visual de la diferencia de color por parte del sistema visual humano.The precise measurement and specification of color, as well as the measurement of color differences between two pairs of samples, are extremely important in various fields such as industry, textiles, agriculture and healthcare. This relevance is especially accentuated in those fields where color is not only an attribute, but adds significant value to the final product. In medicine, color measurement can be essential for detecting subtle changes in skin or tissue, which could indicate health problems. For example, in dermatology, assessing the color of moles or skin lesions can help identify possible signs of skin cancer. In this work, a comprehensive analysis of several color difference databases has been performed using fuzzy logic data analysis techniques. The ultimate goal of this analysis has been to identify inconsistent color pairs compared to other pairs, in order to improve the quality and reliability of the initial databases. To achieve this, a methodology has been implemented that is based on the detection of discrepancies between visually perceived color differences and color differences calculated from color coordinates. A variety of thresholds have been explored, evaluating how different tolerance levels affect the detection of inconsistencies. The result of this work is the identification, analysis and elimination of color pairs that are considered inconsistent in different databases using algorithms developed in Python. This data cleaning contributes significantly to improving the quality of color difference databases, which in turn has a positive impact on various applications where color accuracy is critical. These databases play a key role in the development and evaluation of new color difference formulas. Their goal is to match the results of these formulas more accurately to the visual perception of color difference by the human visual system.Máster en Inteligencia de Negocio y Big Data en Entornos Seguro

Measuring color differences in gonioapparent materials used in the automotive industry

This paper illustrates how to design a visual experiment to measure color differences in gonioapparent materials and how to assess the merits of different advanced color-difference formulas trying to predict the results of such experiment. Successful color-difference formulas are necessary for industrial quality control and artificial color-vision applications. A color- difference formula must be accurate under a wide variety of experimental conditions including the use of challenging materials like, for example, gonioapparent samples. Improving the experimental design in a previous paper [Melgosaet al., Optics Express 22, 3458-3467 (2014)], we have tested 11 advanced color-difference formulas from visual assessments performed by a panel of 11 observers with normal colorvision using a set of 56 nearly achromatic colorpairs of automotive gonioapparent samples. Best predictions of our experimental results were found for the AUDI2000 color-difference formula, followed by color-difference formulas based on the color appearance model CIECAM02. Parameters in the original weighting function for lightness in the AUDI2000 formula were optimized obtaining small improvements. However, a power function from results provided by the AUDI2000 formula considerably improved results, producing values close to the inter-observer variability in our visual experiment. Additional research is required to obtain a modified AUDI2000 color-difference formula significantly better than the current one.This research was supported by the Ministry of Economy and Competitiveness of Spain, research projects FIS2013-40661-P and DPI2011-30090-C02, with European Research Development Fund (ERDF), as well as by the National Science Foundation of China (grant number 61178053)

Parametric effects on the evaluation of threshold chromaticity differences using red printed samples

This paper was published in JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: https://doi.org/10.1364/JOSAA.36.000510. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.[EN] Results from different authors showed deviations of radial orientation in the a*-b* plane (tilt) for the major axes of chromaticity-discrimination ellipses centered around the International Commission on Illumination (CIE) red color center [Color Res. Appl. 3, 149 (1978)], which are not considered by most of the current advanced color-difference formulas (e.g., CIEDE2000). We performed a visual experiment using red printed samples in order to test the influence of the separation between samples (gap) on the mentioned tilt. Our results confirm a counterclockwise tilt of fitted a*-b* ellipses with a magnitude of approximately 36 degrees for samples with no separation, which is similar to that detected by other authors, and a reduction of the mentioned tilt owing to the separation of the samples. We detected a tilt of approximately 22 degrees for samples with a black gap of 0.5 mm and a tilt of approximately 25 degrees for samples with a white gap of 3 mm. Notably, the uncertainty of previous values given by the corresponding credibility intervals of 95% posterior probability is approximately +/- 8 degrees of the mean values. Finally, we study the performance of the most widely used color-difference formulas in the graphic arts sector using our current experimental results, and conclude that the performance of the CAM02-SCD and CAM02-UCS color-difference formulas is significantly better than that of the CIEDE2000 formula.Brusola Simón, F.; Tortajada Montañana, I.; Jorda-Albiñana, B.; Melgosa, M. (2019). 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A Playful Experiential Learning System With Educational Robotics

This article reports on two studies that aimed to evaluate the effective impact of educational robotics in learning concepts related to Physics and Geography. The reported studies involved two courses from an upper secondary school and two courses froma lower secondary school. Upper secondary school classes studied topics ofmotion physics, and lower secondary school classes explored issues related to geography. In each grade, there was an “experimental group” that carried out their study using robotics and cooperative learning and a “control group” that studied the same concepts without robots. Students in both classes were subjected to tests before and after the robotics laboratory, to check their knowledge in the topics covered. Our initial hypothesis was that classes involving educational robotics and cooperative learning are more effective in improving learning and stimulating the interest and motivation of students. As expected, the results showed that students in the experimental groups had a far better understanding of concepts and higher participation to the activities than students in the control groups