# A Machine Vision-Based Algorithm for Color Classification of Recycled Wool Fabrics
## 5 Conclusions  
This paper presented a simple, yet effective, MV-PNN-based tool for carrying out reliable color sorting for regenerated wool fabrics according to a personalized color classification. First, a set of color classes against which to classify recycled wool fabrics was described. Then, color data collection of a number of fabric samples was retrieved using an appositely devised MV system and color mapping from RGB to RAL(r) values to reduce the complexity of the input data. Such parameters were then used to train a PNN able to properly classify new fabrics. Given the simplicity of the proposed approach, which requires textile companies to implement a low-cost machine vision system and run an appositely devised PNN, the obtained results are encouraging. Better results, in fact, are obtainable by implementing approaches that are more complex, such as those cited in literature. Therefore, the proposed method is a viable approach to move forward from manual to automated classification, taking into account that more studies will be required to use it on other complex datasets. Another advantage of the proposed method is that the PNN was trained to cope with the preferred manual/visual color classification assessed by the pickers. In fact, the company decides how to carry out the classification, and it has expert knowledge of how to successfully classify a regenerated fabric (i.e., not merely based on color appearance). This \"guided\" process leads to more reliable classification results. The system works under some assumptions and simplifications: as already stated, the number of RAL colors used to map each image is limited to four more representative values. Moreover, the likelihood of the estimation is increased by taking into account that an error in classification could be minimized if the class is close enough to the desired class. Of course, one could decide to increase the number of RAL classes and/or to consider inappropriate any deviation from the correct class. In such cases, different results could be obtained in replicating the proposed work by enlarging the number of RAL classes, thus considerably increasing the complexity of the PNN. For instance, the size of the training set remains unaltered using eight RAL classes but the number of non-zero elements for each vector is doubled. However, as explained above, the obtained results are only slightly better and so performing this enlargement is not worthwhile. On the other hand, deciding to consider the classification of a fabric in a different class with respect to the one selected by the pickers as erroneous, even if the class is adjacent, leads to an average reduction of the classification accuracy of 5-6%. In any case, the proposed tool demonstrated its effectiveness in a practical application for the textile industry, once experts are made aware of the possible limitations of this study. Despite the system's demonstrated dependability, it was particularly capable of (1) recognizing new fabrics with a mean error under 10%, and (2) respecting the selection criteria offered by human expertise. Additionally, it offered repeatable selections: a number of acquisitions for the same fabric under various environmental conditions were carried out, and they all produced the same final classification with an average error lower than 0.5%. Future work will address texturized fabrics and regenerated fabrics composed of wool combined with other raw materials. **Author Contributions:** Conceptualization, R.F. ; methodology, R.F. ; software, R.F. and M.S. ; validation, M.S. ; formal analysis, R.F. ; investigation, R.F. ; data curation, M.S. ; writing--original draft preparation, R.F. ; writing--review and editing, R.F. and M.S. ; visualization, R.F. ; supervision, R.F. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are not available.