# Reflectance Transformation Imaging as a Tool for Computer-Aided Visual Inspection
###### Abstract  
This work investigates the use of Reflectance Transformation Imaging (RTI) rendering for visual inspection. This imaging technique is being used more and more often for the inspection of the visual quality of manufactured surfaces. It allows reconstructing a dynamic virtual rendering of a surface from the acquisition of a sequence of images where only the illumination direction varies. We investigate, through psychometric experimentation, the influence of different essential parameters in the RTI approach, including modeling methods, the number of lighting positions and the measurement scale. In addition, to include the dynamic aspect of perception mechanisms in the methodology, the psychometric experiments are based on a design of experiments approach and conducted on reconstructed visual rendering videos. The proposed methodology is applied to different industrial surfaces. The results show that the RTI approach can be a relevant tool for computer-aided visual inspection. The proposed methodology makes it possible to objectively quantify the influence of RTI acquisition and processing factors on the perception of visual properties, and the results obtained show that their impact in terms of visual perception can be significant. m 2022 26 June 2022 finishing processes in order to control their appearance in the same way that can be carried out for other technical and technological surface features [7; 8; 9]. Strong synergies with other Industry 4.0 axes such as machine learning [10] and deep learning techniques [11] are also possible, or in connection with new manufacturing processes such as additive manufacturing [12; 13] or remanufacturing [14] in a general context, where we seek to achieve more eco-efficient production methods [15]. Indeed, the challenges associated with the inspection and control of the visual quality of products can constitute important levers for the development and control of these processes [16]. For example, the printing process parameters and material properties can have a significant effect on the appearance of 3D printed parts [17]. Many technical and scientific challenges associated with measurement of the appearance are still unsolved [18; 19; 20; 21]. One of the issues concerns the choice of the visual attributes that one wishes to quantify. Indeed, if for the human visual system the appearance of a surface constitutes an inseparable whole [22; 23], this information is complex and multi-physical, and it cannot be quantified or estimated entirely by a unique appearance measurement system, especially since, in the industrial context, time constraints are essential for inspection processes. The implementation of a physical measurement therefore requires determining which components of the appearance we wish to quantify as a priority in relation to the problem and the applications to which we wish to respond. Today, while the development of appearance attribute measurement systems is still recent, and these systems are not widely implemented in the industry, one approach is to not replace but assist sensory experts, such as by providing them with augmented visualizations of the inspected surfaces or by automating certain aspects of the visual inspection process, such as the exploration path, (i.e., the sequence of scene visualization in terms of incidence light angles or the position of the observer). The work presented here is part of these so-called semi-automated approaches, whose main purpose is to assist visual experts [24] and thus objectivity the inspection process. The methodology proposed in this paper is based on an imaging technique which has seen many developments for cultural heritage applications and, more recently, in the industrial field. This approach, namely the Reflectance Transformation Imaging technique (RTI), consists of acquiring a sequence of images of a surface by only varying the direction of illumination, in a similar way to what is carried out during visual sensory analysis.