Abstract
This research investigates fundamental problems in object recognition in earthen heritage and addresses the possibility of an automatic crack detection method for rammed earth images. We propose and validate a straightforward support vector machine (SVM)-based bidirectional morphological approach to automatically generate crack and texture line maps through transforming a surface image into an intermediate representation. Rather than relying on the application of the eight connectivity rule to a combination of horizontal and vertical gradient to extract edges, we instruct an edge classifier in the form of a support vector machine from features computed on each direction separately. The model couples a bidirectional local gradient and geometrical characteristics. It constitutes of four elements: (1) bidirectional edge maps; (2) bidirectional equivalent connected component maps; (3) SVM-based classifier and (4) crack and architectural line feature map generation. Relevant details are discussed in each part. Finally, the efficiency of the proposed algorithm is verified in a set of simulations that is satisfactorily conforming to labeled data provided manually for surface images of earthen heritage.
Highlights
Earthen structures are essential elements of world heritage
This paper concentrates on athe of rules a novel support tasks vectorsuch machine (SVM)-based approach for solving a real-world pattern recognition problem by transforming a surface image into an intermediate representation, which in turn can provide a set of mathematical rules for desirable tasks such as crack detection
The basic building block of the established procedure is the computation of an oriented gradient in horizontal and vertical directions at every pixel in an image (Figure 2). This computation proceeds by Connected Components Analysis (CCA) [31], which is equivalent to fitting an ellipse having the same second-moments as the connected component (CC), whose major axis is oriented along direction θ (Figure 3)
Summary
Earthen structures are essential elements of world heritage. Among the most widely documented structural types, rammed earth is significantly spread. This research an attempts to propose an effective automatic crack detection algorithm focused architectural feature map that can describe the real condition of the structure, and avoid rammed earth, by exploiting the strong feature learning capabilities of learning-based approaches. Weautomatic developed an automatic technique that allows the accurate generation of a crack map and Machine learning enhance the ability of condition computers of to understand theand real world through architectural feature mapseeks that tocan describe the real the structure, avoid the creation of methods and techniques to obtain and process high-level information from images. This paper concentrates on athe of rules a novel support tasks vectorsuch machine (SVM)-based approach for solving a real-world pattern recognition problem by transforming a surface image into an intermediate representation, which in turn can provide a set of mathematical rules for desirable tasks such as crack detection. Given the importance of crack detection in the conservation of earthen heritage, we believe these data and this algorithm will help advances in research in the field
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