Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A study of crack classification in concrete slabs by using pattern recognition methods
In this study, the system for extracting the characteristics of cracks showing up on concrete slabs through digital images is described and classification based on damage levels is attempted by using these results. First, the linear pattern of cracks is extracted from the digital images of the concrete slabs through image processing techniques. Next, the characteristics such as the projection histograms, that is often applied in the field of optical character recognition, and the feature points in the border expression are extracted. Finally, the digital images of cracks are classified into different damage levels based on the extracted characteristics through the LVQ (Learning Vector Quantization) system. Main results through the numerical calculation are shown as follows: It can be considered that the projection histograms are effective to classify images of cracks by the highest recognition accuracies of 80.9 %. It can be considered that the feature points are effective to classify images of cracks by the highest recognition accuracies of 80.9%. Higher recognition accuracies cannot be obtained by using characteristics combining two kinds of characteristics.
A study of crack classification in concrete slabs by using pattern recognition methods
In this study, the system for extracting the characteristics of cracks showing up on concrete slabs through digital images is described and classification based on damage levels is attempted by using these results. First, the linear pattern of cracks is extracted from the digital images of the concrete slabs through image processing techniques. Next, the characteristics such as the projection histograms, that is often applied in the field of optical character recognition, and the feature points in the border expression are extracted. Finally, the digital images of cracks are classified into different damage levels based on the extracted characteristics through the LVQ (Learning Vector Quantization) system. Main results through the numerical calculation are shown as follows: It can be considered that the projection histograms are effective to classify images of cracks by the highest recognition accuracies of 80.9 %. It can be considered that the feature points are effective to classify images of cracks by the highest recognition accuracies of 80.9%. Higher recognition accuracies cannot be obtained by using characteristics combining two kinds of characteristics.
A study of crack classification in concrete slabs by using pattern recognition methods
Eine Untersuchung zur Rissklassifikation in Betonplatten mittels Mustererkennungsverfahren
Mikumo, Y. (Autor:in) / Hirokane, M. (Autor:in) / Furuta, H. (Autor:in) / Kusunose, Y. (Autor:in) / Yasuda, K. (Autor:in)
2004
7 Seiten, 7 Bilder, 3 Tabellen, 7 Quellen
(nicht paginiert)
Aufsatz (Konferenz)
Datenträger
Englisch
A study of crack classification in concrete slabs by using pattern recognition methods
| British Library Conference Proceedings | 2004
Classification of Cracks in Concrete Slabs using Pattern Recognition Methods
| British Library Conference Proceedings | 2003
Classification of cracks in concrete slabs using pattern recognition methods
| British Library Conference Proceedings | 2004
A study for classification technique of cracks in concrete slabs by using pattern recognition
| British Library Conference Proceedings | 2004
Fatigue crack growth prediction in concrete slabs
| Tema Archiv | 2009