A Comparative Study of Staff Removal Algorithms

Dalitz, Christoph; Droettboom, Michael; Pranzas, B.; Fujinaga, Ichiro

doi:10.1109/tpami.2007.70749

Cited by 107 publications

(117 citation statements)

References 19 publications

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“…In our experiments, we have used the benchmark test sets provided by Dalitz [13]. This test set includes 32 ideal music images and a set of deformed images generated from the ideal images [12,13].…”

Section: Resultsmentioning

confidence: 99%

“…This test set includes 32 ideal music images and a set of deformed images generated from the ideal images [12,13]. To evaluate the performance of the proposed method, we have used the pixel based metric [13].…”

Section: Resultsmentioning

confidence: 99%

“…In this method, Segments are horizontally linked based on DP matching. Dalitz et al in [13] improved the methods of Miyao and Okamoto. Dutta et al proposed a method in which a staff-line segment is considered as a horizontal linkage of vertical black runs with uniform height, and neighboring properties are used to validate it [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel staff removal method for printed music image

Tajeripour

Sotoodeh

2012

IEICE Electron. Express

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Abstract:The first phase in optical music recognition (OMR) is staff-removal. The goal of this phase is to remove staffs and keep musical symbols with high precision, which is very important for next phases in OMR including segmentation and classification. In this paper a novel staff-removal method is proposed. In this method we used run-length coding to compress the input image and then the staff-lines are detected and removed using two common features, namely the thickness of lines and the space between them. Afterwards, The nonstaff objects such as long objects, dynamic wage and destroyed texts are removed using projection and morphological operations. Keywords: staff-removal, staff-line, run-length, OMR, morphology, projection Classification: Other communication hardwares & Syst., vol. E79-D, no. 5, pp. 548-554, 1996. [7] F. Rossant and I. Bloch, "Robust and adaptive omr system including fuzzy modeling, fusion of musical rules, and possible error detection," References

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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A novel staff removal method for printed music image

Tajeripour

Sotoodeh

2012

IEICE Electron. Express

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“…Once all staff lines are detected, staff line removal is executed to completely remove staff line pixels. 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.…”

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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“…OMR systems must overcome (Dalitz et al, 2008), since symbol detection and recognition are based on the accuracy of this step. Nevertheless, in a pen-based system the problem is insignificant because the lines in the staff are handled by the system itself and can be removed effortlessly.…”

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confidence: 99%

Recognition of pen-based music notation with finite-state machines

Calvo-Zaragoza

Oncina

2017

Expert Systems with Applications

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This work presents a data-driven statistical approach with which to recognize pen-based music compositions. Unlike previous works, no assumption is made as regards the handwriting notation, but the system is able to adapt to any kind of style obtained from training data. The series of strokes received as input is mapped onto a stochastic representation, which is combined with a formal language that describes musical symbols in terms of stroke primitives.A Probabilistic Finite-State Automaton is then obtained, which defines probabilities over the set of musical sequences. This model is eventually crossed with a semantic language to avoid sequences that do not make musical sense. Finally, a decoding strategy is applied in order to output a hypothesis concerning the musical sequence actually written. Comprehensive experimentation with several decoding algorithms, stroke similarity measures and probability density estimators are tested and evaluated following different metrics of interest to both user-dependent and user-independent scenarios. The results demonstrate that it is possible to learn the handwriting music style, obtaining competitive performances in each case considered.

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A Comparative Study of Staff Removal Algorithms

Cited by 107 publications

References 19 publications

A novel staff removal method for printed music image

A novel staff removal method for printed music image

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

Recognition of pen-based music notation with finite-state machines

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