Abstract
This paper demonstrates the application of the Swarm Intelligence (SI) algorithm to recognize the specific patterns that are present in the digital images of handwritten music scores. The application introduced in this paper involves the detection of stafflines using particle swarm. The introduced solution described in this paper is a new approach to the problem, and illustrates how optimization algorithm can be modified and successfully applied in different subjects such as pattern recognition. The developed algorithm can be used as a first stage in Optical Music Recognition (OMR) that is followed by the staffline removal phase. It is worth pointing out, that contrary to most state-of-the-art algorithms, the proposed method does not require a binarization step in the preprocessing stage.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fujinaga, I.: Staff detection and removal. In: George, S. (ed.) Visual Perception of Music Notation: On-Line and Off-Line Recognition, pp. 1–39. Idea Group Inc. (2004)
Bellini, P., Bruno, I., Nesi, P.: Optical music sheet segmentation. In: Proceedings of the First International Conference on Web Delivering of Music, pp. 183–190 (2001)
Rossant, F., Bloch, I.: Robust and adaptive OMR system including fuzzy modeling, fusion of musical rules, and possible error detection. EURASIP J. Appl. Signal Process., 160–160 (2007)
Reed, K.T., Parker, J.R.: Automatic computer recognition of printed music. In: Proceedings of the 13th International Conference on Pattern Recognition, vol. 3, pp. 803–807 (1996)
Roach, J.W., Tatem, J.E.: Using domain knowledge in low-level visual processing to interpret handwritten music: an experiment. In: Baird, Bunke, Yamamoto (eds.) Dorothea Blostein and Henry S. Baird, A Critical Survey of Music Image Analysis, vol. 6, pp. 405–434. Springer (1992)
Miyao, H., Okamoto, M.: Stave extraction for printed music scores using DP matching. Journal of Advanced Computational Intelligence and Intelligent Informatics 8, 208–215 (2004)
Mahoney, J.V.: Automatic analysis of music score images. B.Sc thesis, Department of Computer Science and Engineering, MIT. In: Baird, Bunke, Yamamoto (eds.) Dorothea Blostein and Henry S. Baird, A Critical Survey of Music Image Analysis, in Structured Document Image Analysis, pp. 405–434. Springer (1992)
Cardoso, J.S., Capela, A., Rebelo, A., Guedes, C., Da Costa, J.F.P.: Staff detection with stable paths. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(6), 1134–1139 (2009)
Kaick, O., Hamarneh, G., Zhang, H., Wighton, P.: Contour correspondence via ant colony optimization, pp. 271–280. Computer Society (2007)
Gadat, S., Younes, L.: A stochastic algorithm for feature selection in pattern recognition. Journal of Machine Learning Research 8, 509–547 (2007)
Floreano, D., Mattiussi, C.: Bio-inspired artificial intelligence: theories, methods, and technologies. MIT Press, Cambridge (2008)
Fornés, A., Dutta, A., Gordo, A., Lladós, J.: CVC-MUSCIMA: A Ground-truth of Handwritten Music Score Images for Writer Identification and Staff Removal. International Journal on Document Analysis and Recognition (preprint), doi:10.1007/s10032-011-0168-2
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Piątkowska, W., Nowak, L., Pawłowski, M., Ogorzałek, M. (2012). Stafflines Pattern Detection Using the Swarm Intelligence Algorithm. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_67
Download citation
DOI: https://doi.org/10.1007/978-3-642-33564-8_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33563-1
Online ISBN: 978-3-642-33564-8
eBook Packages: Computer ScienceComputer Science (R0)