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.

An SVM-Based Scheme for Automatic Identification of Architectural Line Features and Cracks

Gessica Umili
;
Vito Messina
;
Sabrina Bonetto
;
Anna Maria Ferrero
;
Zeighami Mahshid
First
2020

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.
10
15
1
22
https://www.mdpi.com/2076-3417/10/15/5077
earthen heritage; rammed earth; crack detection; connected component; morphological approach; machine learning; SVM
Gessica Umili; Vito Messina; Sabrina Bonetto; Anna Maria Ferrero; Zeighami Mahshid
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1744956
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