A Comparative Study of Staff Removal Algorithms

This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization-based approach. We define three different error metrics, compare the algorithms with respec...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2008-05, Vol.30 (5), p.753-766
Main Authors: Dalitz, C., Droettboom, M., Pranzas, B., Fujinaga, I.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Automatic Data Processing - methods
Computer errors
Computer science
control theory
systems
Documentation - methods
Error analysis
Exact sciences and technology
Historic
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image segmentation
Information Storage and Retrieval - methods
Intelligence
Measurement
Music
Music (Optical Recognition)
Optical character recognition software
Ordinary magnetoresistance
Page description languages
Pattern analysis
Pattern Recognition, Automated - methods
Performance evaluation
Pixel classification
Reproducibility of Results
Robustness
Segmentation
Sensitivity and Specificity
Subtraction Technique
Testing
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Summary:This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization-based approach. We define three different error metrics, compare the algorithms with respect to these metrics, and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation, our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal but applicable to other segmentation problems as well.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2007.70749