# A Machine Vision-Based Algorithm for Color Classification of Recycled Wool Fabrics
## 1 Introduction  
The relationship between business and sustainability, covered by the term \"sustainable business,\" has recently highlighted that the most innovative businesses have started to consistently manage their impact on social and natural ecosystems. On the one hand, it is true that implementing sustainability principles for businesses entails expenses, investments, adjustments, and innovation efforts that do not always result in quick financial gains. On the other hand, a sustainable approach is viewed as a tactical opportunity for growth because it satisfies present-day needs without endangering the ecosystem for future generations. As a result, a significant proportion of forward-thinking businesses are introducing a variety of products with incredibly low environmental impact while simultaneously guaranteeing the same functional and aesthetic features. Furthermore, recovering materials and energy at the end of a product's life is considered a requirement. Paying attention to the development of eco-sustainable systems is a trump card for attracting expanding markets that are now aware of the ecological challenges that society expects in the future, especially in light of the recent focus on environmental sustainability, eco-design, and, above all, the circular economy. In this scenario, the textile and apparel industry is not an exception and, rather, it is in the process of transitioning from conventional production methods to ones that take into account the circular economy, such as efficient use of natural resources, use of renewable energy sources, reduced or nonexistent production of waste, and management of product lifecycles. In the textile district of Prato (Italy), the predominant actions taken with the circular economy and green economy in mind have traditionally been process-related, with particular reference to the mechanical and thermal recycling of textile products. This is due, in part, to the typical organizational structure of small and medium-sized enterprises interconnected within the district. Considering the mechanical recycling of fabrics, one of the core businesses in the district is the creation of new fabrics from regenerated wool. The standard procedure for businesses that recycle wool is to provide the customer with a catalog of colors offered. Depending on the consumer's request, the organization begins to look through its inventory for appropriately colored clothing. Unfortunately, there is always a mismatch between the color of the recycled wool and the desired color. For this reason, two main aspects are considered in current practice: (1) correct classification of the recycled wool in terms of color, and (2) determination of the correct proportion of differently colored fibers to obtain a desired color. Skilled operators typically evaluate both actions manually and visually. Considering proportion assessment, \"computer color matching\" (CCM) describes a range of semi-automated methods in the literature that mainly deal with the spectrophotometric prediction of dyed fabrics and they are particularly focused on the color-mixing model. These techniques can be broadly categorized into two groups: theoretical and ANN-based techniques. The analysis of diffuse reflectance spectra obtained from weakly absorbing samples is typically performed using theoretical methods, which are primarily based on the well-known Kubelka-Munk (K-M) theory [1; 2]. With the help of these techniques, it is possible to predict the spectral reflectance of a mixture of components (colorants) once the absorption (K) and scattering (S) coefficients are measured. By blending pre-dyed fibers with CIELAB color differences under 0.8, a tristimulus-matching algorithm based on the Stearns-Noechel model (and its implementations [3; 4; 5]) allow a reliable prediction (approximately 90%) of the formula for matching a given color standard.