## 1 Introduction  
Concrete structures such as roads, bridges, beams and pillars are often subjected to high levels of stress and strain both in onshore and offshore environments. The stress in the concrete structures is caused by continuous loading and unloading; changes in temperature; and effects of weathering, which could result in the propagation of cracks [1, 2]. Early detection and assessment of such failures are crucial for ensuring the safety and cost-effective maintenance of these concrete structures. Crack detection could be done through invasive and non-invasive techniques. Invasive techniques are often time-consuming and expensive and usually involve specialized tools and methods such as infrared light, thermal, ultrasonic testing and testing of concrete samples in the laboratory environment. All the invasive and non-invasive methods require structural experts to analyze and interpret the data [3]. The findings of such methodologies are often subjected to human interpretation and knowledge. With the advent of improved imaging capabilities and increased computational power, non-invasive techniques that utilize image analysis of the concrete structures to investigate the presence and propagation of cracks have gained a lot of momentum. In the last few decades, more than 50 papers tried to address these problems using contemporary image analysis algorithms coupled with different pre-processing and post-processing techniques. A comprehensive report of these methods focused on advantages and disadvantages has been published in [4]. Some recent techniques use conventional pre-processing schemes for crack identification in rough concrete surfaces with a complex pattern of cracks. The crack identification technique proposed in [5] is based on an adaptive PDE-based pre-processing algorithm for concrete crack images. The implementation of this technique is combined with a local maximum-based thresholding scheme. The proposed approach based on automated operation, selective sharpening, and smoothing leads to better crack detection. Some images containing shadows in [5] Figure 8 indicate that illumination correction by the illumination-reflectance model to normalize non-uniform lightning is capable of identifying smooth shadows (images 5 and 6 on the last row) but has problems with high intensity shadows because the outlines of high intensity shadows are identified as cracks (images 2 and 3 on the last row). Another recent study [6] tries to solve the crack identification problem by proposing effective pre-processing algorithms for the enhancement and analysis of digital images of concrete surfaces. This technique works well with nonuniform lightning; however, it would be interesting to further explore if this approach is robust to shadows of variable intensity and shape. It is also shown that the proposed CIEAD-2 technique might not perform well when surfaces are very rough--the segmented image is contaminated with noise. However, this problem is solved with the improved CIEAD-2 technique. Yet another study [7] proposes automated crack segmentation via saturation channel thresholding, area classification, and a fusion of a modified-level set segmentation with Canny edge detection. It is demonstrated that this approach is able to identify cracks with nonuniform lightning (Figure 16, 3 rd row, [7]). However, its applicability for images contaminated by high intensity optical shadows is not investigated. Moreover, the segmentation of images with rough surfaces and uniform illumination might not provide robust results in some cases--rough regions in the background sometimes can be identified as micro-cracks (Figure 18, part g, 2 nd row, [7]). However, this problem might be solved by fine tuning parameters of the proposed concrete crack processing algorithm. This technique as well as techniques in [5; 6] has a benefit that it is not as computationally and structurally complex as methods based on artificial neural networks.