info:eu-repo/semantics/conferenceObject
Crack Detection in Oil Paintings Using Morphological Filters and K-SVD Algorithm
Fecha
2022Registro en:
Rucoba-Calderón, C., Ramos, E. & Gutiérrez-Cárdenas, J. (2022). Crack Detection in Oil Paintings Using Morphological Filters and K-SVD Algorithm. En J. A. Lossio-Ventura, J. Valverde-Rebaza, E. Díaz, D. Muñante, C. Gavidia-Calderon, A.D.B. Valejo & H. Alatrista-Salas (Eds.), Information Management and Big Data: Eighth Annual International Conference, SIMBig 2021, December 1-3, 2021, Proceedings, Communications in Computer and Information Science (vol. 1577, pp. 329-339). Springer. 10.1007/978-3-031-04447-2_22
1865-0929
Communications in Computer and Information Science
Autor
Rucoba Calderón, Carla Valeria
Ramos Ponce, Oscar Efrain
Gutiérrez Cárdenas, Juan Manuel
Institución
Resumen
Cracks in oil paintings constitute an undesirable but unavoidable effect of time, deteriorating the painting quality. This work proposes a crack detection method that supports the physical restoration process of the artworks, providing a fissure map that allows the artist to visualize the pictorial layer and its flaws. This approach applies three image processing techniques to digitized oil paintings: oriented elongated filters, top-hat morphological filters and a K-SVD algorithm. Then, a post-processing stage based on K-Means is performed on the resulting binary maps to eliminate false positives. Finally, a pixel-by-pixel voting technique is applied to combine the binary maps. Our proposed framework has a better performance detecting craquelure when compared to other methods such as ADA Boost and convolutional neural networks. We obtained a recall of 0.8577, a probability of false alarm of 0.0779, a probability of false negatives of 0.1423, an accuracy of 0.7123, and an F1 value of 0.7783, which is amongst the best results for the state-of-the-art techniques.