## 2 Developed Methodology
### Constructing Edge Maps  
As the first step in this process, the input image is converted to grayscale, and oriented edge maps are constructed by applying edge detection operators in horizontal and vertical directions independently (Figure 2). This section presents a review of edge detection algorithms and reports the results of performance evaluation in rammed earth images for this task. The purpose of this study is to evaluate the capability of these methods to separate crack objects from the background. This framework discusses two aspects of detection performance, including performance in crack detection and performance in the identification of architectural features. Shortcomings of existing techniques and relevant details are taken into account. Such an analytical framework is necessary for the selection of an edge detection technique for the problem at hand. Focus is particularly given to find solutions that overcome limitations. The main requirement that is expected from the solution is a reasonably good accuracy in the automatic identification of the presence of multiple cracks and architectural objects in a rammed earth image. Motivated by the observed inconsistencies and inaccuracies to distinguish between cracks and architectural features, this section concentrates on the selection of an edge detection technique, whose main algorithm is inspired by the performance analysis. A broad family of approaches to crack detection relies on modeling cracks as sharp discontinuities in the brightness, which is a characteristic of our approach as well. While this is by no means the only path taken, local edge detection operators appear to be the most widely used. The edge detection method is a simple but effective tool to identify and locate the pixels that correspond to the boundaries of an object in the image, which can be obtained by mask processing techniques [34]. The output of this operation is a binary image with the detected edge pixels. However, several of those pixels may be the result of noise or surface features. Early local approaches, such as the Sobel operator [35], quantifies the presence of a boundary through discontinuity measurements in the local brightness. Another family of methods, such as wavelet transform, aims at representing the image through a collection of sub-bands, more basic images containing different fine-scale and large-scale information of the original image. An example is fast Haar transform (FHT) [36], which produces essentially three directional edge maps, namely horizontal, vertical and diagonal. The most commonly used edge detection algorithms are Sobel [35], Canny [37], Prewitt [38], Laplacian of Gaussian (LoG) [39] and FHT [36]. In this section, these algorithms are applied to extract crack pixels from selected parts of rammed earth images. Depending on the application, these algorithms may produce missing or extra edges on complex surface images. Investigating these algorithms can help in the choice of an edge detection technique over another for automatic crack detection of rammed earth heritage. Edge detection methods usually determine the sharp variation in the gray level intensities based on the calculation of the first and second derivatives of the local intensities. The first and second derivatives of the local intensities are computed by the first and second derivatives of the local intensity. The second derivatives of the local intensity are computed by the first and second derivatives of the local intensity. The second derivatives are computed by the second derivatives of the local intensity. The second derivatives are computed by the second derivatives of the local intensity. The second derivatives are computed by the second derivatives of the local intensity. The second derivatives are computed by the second derivatives of the local intensity. The second derivatives are computed by the second derivatives of the local intensity. The second derivatives are computed by the second derivatives of the local intensity.