Using Diagram Generation Software to Improve Diagram Recognition: A Case Study of Music Notation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets in Approximate Reasoning and Information Systems
Fuzzy Sets in Approximate Reasoning and Information Systems
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A music notation construction engine for optical music recognition
Software—Practice & Experience
Automatic Computer Recognition of Printed Music
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Optical Music Sheet Segmentation
WEDELMUSIC '01 Proceedings of the First International Conference on WEB Delivering of Music (WEDELMUSIC'01)
Adaptive optical music recognition
Adaptive optical music recognition
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Note symbol recognition for music scores
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Stafflines pattern detection using the swarm intelligence algorithm
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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This paper describes a system for optical music recognition (OMR) in case of monophonic typeset scores. After clarifying the difficulties specific to this domain, we propose appropriate solutions at both image analysis level and high-level interpretation. Thus, a recognition and segmentation method is designed, that allows dealing with common printing defects and numerous symbol interconnections. Then, musical rules are modeled and integrated, in order to make a consistent decision. This high-level interpretation step relies on the fuzzy sets and possibility framework, since it allows dealing with symbol variability, flexibility, and imprecision of music rules, and merging all these heterogeneous pieces of information. Other innovative features are the indication of potential errors and the possibility of applying learning procedures, in order to gain in robustness. Experiments conducted on a large data base show that the proposed method constitutes an interesting contribution to OMR.