![]() ![]() The six tertiary hues are Red-Orange, Yellow-Orange, Yellow-Green, Blue-Green, Blue-Violet, and Red-Violet. Lastly, we obtain tertiary hues by mixing a primary hue with its nearest secondary. Blending two primary hues gives the secondary ones: Green, Orange, and Purple. The primary hues are Red, Yellow, and Cyan. In turn, hues are divided into primary, secondary, and tertiary. Three color-making attributes are used to communicate this subjective experience of color: hue hue purity (expressed as colorfulness, chroma, saturation, or dullness) and lightness or darkness. Humans will, in fact, resort to compound names (reddish-orange, yellowish-green) when confronted with colors located in fuzzy areas between more established terms. The ultimate challenge for color vision algorithms is that there are no well-defined boundaries between color categories, i.e., there are no immediate methods of finding, for example, the limit between orange and red, or between yellow and green. This problem is known in the literature as ’the semantic gap’. The same shade of red could be called “crimson” or “cherry”, depending on the observer therefore, there is a great disparity between linguistic descriptions of color and computer representations. The second problem, influenced by the differences in human perception of color, is that color names are not universal. Thus, it is impossible to create a computer method able to reproduce the infinite possible experiences of color. Furthermore, human perception is also influenced by the phenomenon of color constancy, by which our brains perceive color homogeneously on surfaces, despite possible changes of lights or reflections of brighter colors, in contrast to the pixel-level differences in color detected by computers. The main problem with color vision is that human perception of color is not homogeneous: each person has their own experience of color, influenced by context, culture, education, and lighting. However, several problems exist that render the creation of such a perfect color analysis algorithm difficult. Additionally, the algorithm should be able to locate these color terms within a digital color space, and to use these subspaces for accurate color classification and image segmentation. A color vision algorithm should be able to reproduce the human experience of color perception, and to identify the colors present in an image, giving them a color name or color term. Computer vision is the branch of computer science that deals with the many challenges of color analysis and representation. Color has been represented in various ways, including the traditional color wheel and, in the age of computer vision, using color spaces, such as RGB and CIELAB. Throughout history, there have been many attempts to deal with the complexities of color representation and analysis. Hence, ABANICCO can serve as a foundation for successfully addressing a myriad of challenges in various areas of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging. This empirical evaluation provided evidence of ABANICCO’s accuracy in color analysis, showing how our proposed model offers a standardized, reliable, and understandable alternative for color naming that is recognizable by both humans and machines. The proposed model, “ABANICCO” (AB ANgular Illustrative Classification of COlor), was evaluated through different experiments: its color detection, classification, and naming performance were assessed against the standardized ISCC–NBS color system its usefulness for image segmentation was tested against state-of-the-art methods. This method presents a robust, unsupervised, and unbiased strategy for color naming, based on statistics and color theory. To address these challenges, we propose a novel method combining geometric analysis, color theory, fuzzy color theory, and multi-label systems for the automatic classification of pixels into 12 conventional color categories, and the subsequent accurate description of each of the detected colors. The gap between human color perception, linguistic color terminology, and digital representation are the main challenges for developing methods that properly classify pixels based on color. Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images.
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