## 1 Introduction  
Additive manufacturing (AM) processes have reached a high degree of maturity during the last decade. Evolving from a reduced number of applications, mainly related to aesthetic models and conceptual prototypes, AM is currently formed by a growing number of processes capable of achieving small-to-medium batch size productions and industrial-level requirements. This evolution has been sustained on better machines and processes capable of handling diverse materials, but also on reducing the gap between AM parts' quality and that achieved with traditional manufacturing processes. In any case, according to [PERSON], complete industrial adoption of AM is still 5 to 10 years away [1]. There are many reasons behind this gap in development, but a frequently highlighted issue is the difference between dimensional and geometrical accuracy achieved in AM parts, when compared with traditional manufacturing [2]. The usual approach regarding dimensional or geometrical assessment of AM parts is to measure or verify the final 3D part, once it has already been released from the machine [3, 4]. Nevertheless, a tendency involving on-machine measurement (OMM) approaches with integrated measuring devices is currently gaining momentum [5, 6, 7, 8]. These approaches will provide clear advantages when they become fully developed and incorporated to production-level machines [9]. Hence, dimensional inaccuracies would be detected when the part is still being manufactured, allowing the system to apply corrective actions or to stop production. In both cases, savings in terms of cost and time or related to non-conformities would be obtained. Among the technologies that could be incorporated to production machines for quality verification, those not requiring contact between sensor and material, such as image sensors, triangulation lasers, or structured light, have several advantages. Non-contact technologies have nearly no influence upon parts, and are also faster than contact ones. Nevertheless, they are also usually worse in terms of accuracy. Flatbed scanners (FBSs) are optical devices that can be used for digitising flat objects. Although they are usually employed to scan paper sheets or photographs, computer vision techniques also enable them to be used in complex tasks. Among those tasks, several researchers have highlighted the use of FBSs for detecting contours of three-dimensional objects [10,11] and characterising surface defects [7]. This latter work illustrates the possibilities of using FBSs for on-machine verification and closed-loop manufacturing strategies. Contour measurement with FBSs is severely affected by image deformation, which is a common issue to other image capturing devices. Some researchers have related image deformation in FBSs to mechanical errors of the equipment [12]. Accordingly, some works have focused on the possibilities of modelling and adjusting errors to improve measurement reliability [13,14]. Other works have pointed out the relevance of the relative position of the object with respect to the scanner plate or even the resolution, but also indicate that objects could be measured with sufficient accuracy if the sources of measurement variation were quantified and minimised [15]. The works by [PERSON] [16] and [PERSON] [17] have provided a method for adjusting global deformation errors in FBS. Their approach improved the reliability of digital image-based analysis of part contours to a point that even manufacturing errors can be characterised and partially compensated. [PERSON] [18] proposed an alternative method for deformation adjustment and compensation, based on local deformation adjustment (LDA). Results upon a highly contrasted test specimen showed that, under specific test conditions, the characterisation of layer contours with a FBS could approximate the performance of a coordinate measurement machine (CMM). Although some efforts were mainly oriented to adjust image distortion, a feasible characterisation of contours is also affected by additional sources of error.