Automatic Recognition of Printed Music

Carter, Nicholas P.; Bacon, Richard

doi:10.1007/978-3-642-77281-8_21

Cited by 31 publications

(14 citation statements)

References 4 publications

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“…Black runlengths are removed based on their length or line adjacency. The algorithms by Carter and Bacon [17] and Fujinaga [14] fall in this category.…”

Section: Staffline Removalmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study of Staff Removal Algorithms

Dalitz

Droettboom

Pranzas

et al. 2008

IEEE Trans. Pattern Anal. Mach. Intell.

107

112

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Abstract-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.

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“…Black runlengths are removed based on their length or line adjacency. The algorithms by Carter and Bacon [17] and Fujinaga [14] fall in this category.…”

Section: Staffline Removalmentioning

confidence: 99%

“…Carter and Bacon [17] segment the image using a concept known as the line-adjacency graph. Each segment resulting from this analysis is either part of a staffline or not, so once the sections have been found, no further analysis at the pixel level is necessary.…”

Section: Runlength Analysismentioning

confidence: 99%

A Comparative Study of Staff Removal Algorithms

Dalitz

Droettboom

Pranzas

et al. 2008

IEEE Trans. Pattern Anal. Mach. Intell.

107

112

View full text Add to dashboard Cite

show abstract

“…A comprehensive review and comparison of the first attempts considered for this task 70 can be consulted in the work of Dalitz et al (2008), who divided the staff-line removal strategies proposed until then into four categories: the Line Tracking (Randriamahefa et al, 1993;Bainbridge & Bell, 1997), Vector Field (Martin & Bellissant, 1991;Roach & Tatem, 1988), Runlength (Carter & Bacon, 1992;Fujinaga, 2005) and Skeleton (Ng, 2001) is then modeled as a graph so that staff detection is solved as a maximization problem. This strategy was later improved and extended to be used on grayscale 80 scores (Rebelo & Cardoso, 2013).…”

Section: Staff-line Detection and Removalmentioning

confidence: 99%

Staff-line removal with selectional auto-encoders

Gallego

Calvo-Zaragoza

2017

Expert Systems with Applications

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Staff-line removal is an important preprocessing stage as regards most Optical Music Recognition systems. The common procedures employed to carry out this task involve image processing techniques. In contrast to these traditional methods, which are based on hand-engineered transformations, the problem can also be approached from a machine learning point of view if representative examples of the task are provided. We propose doing this through the use of a new approach involving auto-encoders, which select the appropriate features of an input feature set (Selectional Auto-Encoders). Within the context of the problem at hand, the model is trained to select those pixels of a given image that belong to a musical symbol, thus removing the lines of the staves. Our results show that the proposed technique is quite competitive and significantly outperforms the other state-of-art strategies considered, particularly when dealing with grayscale input images.

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“…In [16], Dalitz et al did a comparative study of staff line removal algorithms, and they divided staff line removal approaches into four main categories: Line Tracking [5,17,18], Vector Field [19], Run length [6,14], Skeletonization [13] (See Table 1). In [20], Carter and Bacon used LAG to segment the image sheet to parts. Each part is either part of staff line or not.…”

Section: Related Workmentioning

confidence: 99%

A Robust Staff Line Height and Staff Line Space Estimation for the Preprocessing of Music Score Recognition

Na¹,

Kim²,

Nquyen³

2015

Journal of Internet Computing and Services

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In this paper, we propose a robust pre-processing module for camera-based Optical Music Score Recognition (OMR) on mobile device. The captured images likely suffer for recognition from many distortions such as illumination, blur, low resolution, etc. Especially, the complex background music sheets recognition are difficult. Through any symbol recognition system, the staff line height and staff line space are used many times and have a big impact on recognition module. A robust and accurate staff line height and staff line space are essential. Some staff line height and staff line space are proposed for binary image. But in case of complex background music sheet image, the binarization results from common binarization algorithm are not satisfactory. It can cause incorrect staff line height and staff line space estimation. We propose a robust staff line height and staff line space estimation by using run-length encoding technique on edge image. Proposed method is composed of two steps, first step, we conducted the staff line height and staff line space estimation based on edge image using by Sobel operator on image blocks. Each column of edge image is encoded by run-length encoding algorithm Second step, we detect the staff line using by Stable Path algorithm and removal the staff line using by adaptive Line Track Height algorithm which is to track the staff lines positions. The result has shown that robust and accurate estimation is possible even in complex background cases.

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Automatic Recognition of Printed Music

Cited by 31 publications

References 4 publications

A Comparative Study of Staff Removal Algorithms

A Comparative Study of Staff Removal Algorithms

Staff-line removal with selectional auto-encoders

A Robust Staff Line Height and Staff Line Space Estimation for the Preprocessing of Music Score Recognition

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