## 1 Introduction
[PERSON] [19] mentions three factors that are key to contour detection: context, noise, and location. Context refers to the significance of the change in local intensity that indicates that a certain point is part of an edge, and relates this significance to that observed in the neighbourhood. In the case of AM layer contour characterisation, layer-to-background contrast shall be the main factor affecting context. Noise refers to the possibility of misidentifying noisy points as edge points. Accordingly, noise filtering shall be considered as a possible factor influencing contour characterisation. Location refers to the fact that the digital image of an edge reflects a smooth transition between intensity levels, which makes it difficult to fix with high accuracy which point represents the exact location of such transition. This factor shall be affected by the image processing sequence, especially by the method or algorithm used to distinguish between contour and its neighbourhood (edge detector). It shall also be affected by the actual value of the parameter used to make this distinction. According to [PERSON] [20], an edge detector is \"an algorithm that produces a set of edges from an image\", while a contour would be a \"list of edges\" or \"the mathematical curve that models a list of edges\". Previous research has not paid much attention to the influence of the image processing sequence and its specific configuration in the accuracy of the results. In most cases [10,11,16,18], both filtering and edge detection stages as well as their correspondent configurations are directly given, and no discussion about their possible influence on the results is provided. Even more, some works rely on manual measurement of the characteristics in the image, so they did not really use image processing or automatic contour detection algorithms [12,13,14,15,17]. Nevertheless, edge detection is a relevant part of every automated measurement strategy, and the effect of image processing configuration should not be neglected. In their review of contour detection methods, [PERSON] et al. [21] distinguished between region-oriented approaches, edge-and-line oriented approaches, and hybrid approaches. Other authors [22] include other categories, such as deep learning-based approaches. In the present research, analysis was limited to local edge detection methods that use differential operators [21] to search in a digital image for significant changes in local intensity with the objective of extracting a list of possible locations (i,j) of edge points. According to [PERSON] [23], two main approaches to edge detection based on differential operators are commonly used: first derivative approaches and second derivative approaches. First derivative detectors seek for significant changes in a continuous function of image intensity by means of computing a gradient operator (\(\
abla\)) that is defined as a vector (Equation (1)). \[\
abla f=\left[ \begin{array}{c}f_{x}\\ f_{y}\end{array} \right]=\left[ \begin{array}{c}\frac{\partial f}{\partial x} \\ \frac{\partial f}{\partial y}\end{array} \right] \tag{1}\]  
This vector reflects the magnitude of the gradient at a given point G(x,y) and its angle with respect to the coordinate system of reference \(\Theta\)(x,y). Nevertheless, a digital image does not really contain continuous functions of intensity. Instead, it is formed by arrays of discrete samples of ideally continuous functions. Consequently, so-called first derivative edge detectors approximate continuous functions by discrete ones. There are three main edge detectors based on the first derivative approach as proposed by [PERSON] [24], [PERSON] [25] and [PERSON] [26]. These methods provide a new image of identical size as the original one, with each pixel representing the value of the gradient in its exact position on the original image. These operators use two kernels which are convolved with the original image to calculate approximations of the derivatives, in two perpendicular directions.