LAUSR

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.