Self-supervised classification of surfaces using reflectance transformation imaging

Abstract

Reflectance Transformation Imaging (RTI) is an imaging technique used to analyze objects or surfaces by capturing their appearance under varying illumination directions. This paper proposes two self-supervised learning algorithms to classify surfaces according to their reflectance profiles. The classification problem is addressed using K-means and Self Organizing Map (SOM) neural networks. The proposed methodology is evaluated using both real and synthetic datasets. The primary motivation for our approach is to exploit illumination variation data to enhance surface understanding and detect anomalies. Given the exploratory nature of this task and the lack of ground truth for comparison, a self-supervised method was deemed most suitable. The classification of surfaces using reflectance information has immense applications in fields such as Cultural Heritage (CH) preservation, digitization, and industrial quality control.