基于局部二进制模式的乐谱谱线检测与删除

Staff Detection and Removal Based on Local Binary Patterns

谱线检测与删除是光学乐谱识别中重要和关键的环节之一。在乐谱中,谱线往往与大多数符号交叉或重叠,即存在像素属于谱线像素同时也属于符号像素的情况,因此删除谱线并且不破坏音乐符号并非易事。研究目标是需要删除仅仅属于谱线的像素,观察乐谱图像可以发现谱线像素与非谱线像素局部纹理存在差异,主要表现为谱线像素的局部纹理与谱线宽度相关,简洁明了,而非谱线像素的局部纹理除了存在仅与自己本身相关的情况,还存在与交叉点相关的情况。因此,采用局部二进制模式通过提取局部纹理特征,获得谱线像素与非谱线像素局部纹理的差异,对谱线与非谱线像素进行检测分类,进而将谱线像素删除。该方法不仅可以删除理想状态下乐谱谱线,对弯曲状态下乐谱谱线同样适用。实验结果证明了该方法在像素误差、片段误差等性能指标上优于现有常用方法。

Staff detection and removal are important and fundamental stages in many optical music recognition（OMR） systems. In scores, staff lines cross or overlap with the majority of symbols, that is, the pixels belong to the staff lines pixels and also belong to the symbol pixels, so it is not easy to remove the staff lines not destroying the music symbol. The purpose is to remove the pixels that only belong to the staff lines. By observing the music image,it can be found that the local texture of the staff pixel is different from that of the non-staff pixel. The local texture of the staff pixel is related to the width of staff line, and the local texture of the non-staff pixel is not only related to its own situation, but also there is a situation associated with the intersection. Therefore, this paper uses the local binary pattern to extract the local texture feature, and obtains the difference of the local texture between the staff line pixel and the non-staff line pixel. Then this paper detects and classifies the pixels of staff line and non-staff line,and removes the staff line pixels. The method proposed in this paper can not only delete the music line under the ideal state, but also apply to the curve of the music score. And the experimental results show that the proposed method is better than the existing methods on pixel error and segment error.