Staff-line detection and removal using a convolutional neural network
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Título: | Staff-line detection and removal using a convolutional neural network |
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Autor/es: | Calvo-Zaragoza, Jorge | Pertusa, Antonio | Oncina, Jose |
Grupo/s de investigación o GITE: | Reconocimiento de Formas e Inteligencia Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Music staff-line removal | Optical music recognition | Pixel classification | Convolutional neural networks |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | ago-2017 |
Editor: | Springer Berlin Heidelberg |
Cita bibliográfica: | Machine Vision and Applications. 2017, 28(5-6): 665-674. doi:10.1007/s00138-017-0844-4 |
Resumen: | Staff-line removal is an important preprocessing stage for most optical music recognition systems. Common procedures to solve this task involve image processing techniques. In contrast to these traditional methods based on hand-engineered transformations, the problem can also be approached as a classification task in which each pixel is labeled as either staff or symbol, so that only those that belong to symbols are kept in the image. In order to perform this classification, we propose the use of convolutional neural networks, which have demonstrated an outstanding performance in image retrieval tasks. The initial features of each pixel consist of a square patch from the input image centered at that pixel. The proposed network is trained by using a dataset which contains pairs of scores with and without the staff lines. Our results in both binary and grayscale images show that the proposed technique is very accurate, outperforming both other classifiers and the state-of-the-art strategies considered. In addition, several advantages of the presented methodology with respect to traditional procedures proposed so far are discussed. |
Patrocinador/es: | This work was supported by the Spanish Ministerio de Educación, Cultura y Deporte through a FPU Fellowship (Ref. AP2012–0939), the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R supported by EU FEDER funds) and the Instituto Universitario de Investigación Informática (IUII) from the University of Alicante. |
URI: | http://hdl.handle.net/10045/68991 |
ISSN: | 0932-8092 (Print) | 1432-1769 (Online) |
DOI: | 10.1007/s00138-017-0844-4 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © Springer-Verlag Berlin Heidelberg 2017 |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.1007/s00138-017-0844-4 |
Aparece en las colecciones: | INV - GRFIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2017_Calvo-Zaragoza_etal_MachineVisionAppl_final.pdf | Versión final (acceso restringido) | 971,69 kB | Adobe PDF | Abrir Solicitar una copia |
2017_Calvo-Zaragoza_etal_MachineVisionAppl_preprint.pdf | Preprint (acceso abierto) | 1,48 MB | Adobe PDF | Abrir Vista previa |
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