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Staff line Detection and Removal in the Grayscale Domain

机译:员工线路检测和删除灰度域

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The detection of staff lines is the first step of most Optical Music Recognition (OMR) systems. Its great significance derives from the ease with which we can then proceed with the extraction of musical symbols. All OMR tasks are usually achieved using binary images by setting thresholds that can be local or global. These techniques however, may remove relevant information of the music sheet and introduce artifacts which will degrade results in the later stages of the process. It arises therefore a need to create a method that reduces the loss of information due to the binarization. The baseline for the methodology proposed in this paper follows the shortest path algorithm proposed in [1]. The concept of strong staff pixels (SSP's), which is a set of pixels with a high probability of belonging to a staff line, is proposed to guide the cost function. The SSP allows to overcome the results of the binary based detection and to generalize the binary framework to grayscale music scores. The proposed methodology achieves good results.
机译:员工线路的检测是大多数光学音乐识别(OMR)系统的第一步。其具有重要意义从中可以源于我们可以进行音乐符号的提取。通常使用二进制图像来实现所有OMR任务,通过设置可以是本地或全局的阈值。然而,这些技术可以消除音乐表的相关信息并引入伪像,其将降低过程的后续阶段。因此,它需要创建一种方法,该方法减少了由于二值化而减少信息丢失。本文提出的方法的基线遵循[1]中提出的最短路径算法。提出了强大的员工像素(SSP)的概念,这是一组具有很高的员工线概率的像素,以指导成本函数。 SSP允许克服基于二进制的检测结果并将二进制框架概括为灰度音乐分数。该方法的方法取得了良好的效果。

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