Classifying carpets based on laser scanner data

Nowadays the quality of carpets is in industry still determined through visual assessment by human experts, although this procedure suffers from a number of drawbacks. Existing computer models for automatic assessment of carpet wear are at this moment not capable of matching the human expertise. Therefore, we present a completely new approach to tackle this problem. A three-dimensional laser scanner is used to obtain a digital copy of the carpet. Due to the specific characteristics of the laser scanner data, new algorithms are developed to extract relevant information from the raw data. These features are used as input to a classifier system that defines a partial ranking over the objects. To this end, ordinal regression and multi-class classification models are applied. Experiments demonstrate that our approach gives rise to promising results with correlations up to 0.77 between extracted features and quality labels. The performance obtained with nested cross-validation, including a C-index of more than 0.95, an accuracy of 76% and only 3% serious errors of a full point, gives rise to a substantial improvement compared to other approaches mentioned in the literature.