[1] E. W. Dijkstra. A note on two problems in connexion with graphs. Numerische Mathematik, 1:269-271, 1959. [ bib ]
[2] Dorothea Blostein and Henry S. Baird. A critical survey of music image analysis. In Baird, Bunke, and Yamamoto (Eds.), editors, Structured Document Image Analysis, pages 405-434. Springer-Verlag, Heidelberg, 1992. [ bib ]
[3] I. Leplumey, J. Camillerapp, and G. Lorette. A robust detector for music staves. In Proceedings of the International Conference on Document Analysis and Recognition, pages 902-905, 1993. [ bib ]
[4] Ichiro Fujinaga. Staff detection and removal. In Susan George, editor, Visual Perception of Music Notation: On-Line and Off-Line Recognition, pages 1-39. Idea Group Inc., 2004. [ bib ]
[5] Ignacy Gawedzki. Optical music scores recognition. Technical report, 2002. [ bib ]
[6] Roma-ancient music optical recognition. http://alfa.ist.utl.pt/~mpinto/roma/. [ bib ]
[7] M. Roth. OMR-optical music recognition. Diploma thesis, Swiss Federal Institute of Tecnology, 1992. [ bib ]
[8] N. P. Carter. Automatic Recognition of Printed Music in the Context of Electronic Publishing. Ph.d. thesis, Departments of Physics and Music, University of Surrey, 1989. [ bib ]
[9] S. Glass. Optical music recognition. B.Sc thesis, 1989. [ bib ]
[10] H. Kato and S. Inokuchi. A recognition system for printed piano music using musical knowledge and constraints. In Proceedings of the International Association for Pattern Recognition Workshop on Syntactic and Structural Pattern Recognition, pages 231-248, 1990. [ bib ]
[11] T. Reed. Optical music recognition. Master's thesis, Department of Computer Science, University of Calgary, Canada, 1995. [ bib ]
[12] Arnaud F. Desaedeleer. Reading sheet music. Master's thesis, Department of Computing, University of London, 2006. [ bib ]
[13] D. Bainbridge. Extensible Optical Music Recognition. Ph.d. thesis, Department of Computer Science, University of Canterbury, Christchurch, NZ, 1997. [ bib ]
[14] Bharath R. Modayur, Visvanathan Ramesh, Robert M. Haralick, and Linda G. Shapiro. Muser: A prototype musical score recognition system using mathematical morphology. Machine Vision and Applications, 6(2-3):140-150, 1993. [ bib ]
[15] J. V. Mahoney. Automatic analysis of music score images. B.Sc thesis, 1982. [ bib ]
[16] D. S. Prerau. Computer pattern recognition of standard engraved music notation. Ph.d. dissertation, Department of Computer Science and Engineering, MIT, 1970. [ bib ]
[17] J. W. Roach and J. E. Tatem. Using domain knowledge in low-level visual processing to interpret handwritten music: an experiment. Pattern Recognition, 21(1):33-44, 1988. [ bib | http ]
Turning handwritten scores into engraved scores consumes a significant portion of music publishing companies' budgets. Pattern recognition is the major bottleneck holding up automation of this process. Human beings who know music can easily read a handwritten score, but without musical knowledge, even people cannot correctly perceive the markings in a handwritten score. This paper reports an experiment in which knowledge of music, a highly structured domain is applied to extract primitive musical features. This experiment shows that if the domain of image processing is well defined, significant improvements in low-level segmentations can be achieved (17 Refs.) recognition; computerised picture processing; expert systems; music

Keywords: handwritten music recognition; character recognition; knowledge based feature extraction; expert systems; primitive musical feature extraction; computerised pattern recognition; domain knowledge; low-level visual processing; image processing; low-level segmentations equipment); C6170 (Expert systems); C7820 (Humanities)
[18] Florence Rossant. A global method for music symbol recognition in typeset music sheets. Pattern Recognition Letters, 23(10):1129-1141, 2002. [ bib | http ]
Keywords: dblp
[19] J. C. Pinto, P. Vieira, and J. M. Sousa. A new graph-like classification method applied to ancient handwritten musical symbols. IJDAR, 6(1):10-22, 2003. [ bib ]
[20] Florence Rossant and Isabelle Bloch. Optical music recognition based on a fuzzy modeling of symbol classes and music writing rules. In ICIP (2), pages 538-541, 2005. [ bib | http ]
Keywords: dblp
[21] Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins. Digital Image processing using MATLAB, pages 405-407. Upper Saddle River, NJ : Pearson/Prentice-Hall, 2004. [ bib ]
[22] Ana Rebelo, Artur Capela, Joaquim F. Pinto da Costa, Carlos Guedes, Eurico Carrapatoso, and Jaime S. Cardoso. A shortest path approach for staff line detection. AxMedis 2007, 2007. [ bib ]
[23] Sergios Theodoridis and Konstantinos Koutroumbas. Pattern recognition, chapter 4.6. Academic Press, second edition, 2003. [ bib ]
[24] Principles of training multi-layer neural network using backpropagation algorithm. http://galaxy.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html. [ bib ]
[25] Simon Haykin. Neural Networks: a comprehensive foundation, pages 156-175; 318-334. Prentice-Hall, Inc, second edition, 1999. [ bib ]
[26] Keinosuke Fukunaga. Intoduction to Statistical Pattern Recognition, pages 286-380. Academic Press, second edition, 1990. [ bib ]
[27] The k-nearest neighbor algorithm. http://en.wikipedia.org/wiki/Nearest\_neighbor\_(pattern\_recognition). [ bib ]
[28] k-nearest neighbors. http://www.statsoft.com/textbook/stknn.html. [ bib ]
[29] Chih-Wei Hsu and Chih-Jen Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13(2):415-425, 2002. [ bib ]
[30] Eddy Mayoraz and Ethem Alpaydim. Support vector machines for multi-class classification. Technical report, 1998. [ bib ]
[31] Vojtech Franc and Václav Hlavác. Multi-class support vector machine. Technical report, 2002. [ bib ]
[32] Teoria dos grafos. http://www.dcc.fc.up.pt/~zp/aulas/9899/me/trabalhos/alunos/Algoritmos_do_Caminho_Minimo_em_Grafos/teoria.html. [ bib ]
[33] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, pages 445-447. The MIT Press, second edition, 2001. [ bib ]

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