Boosted Stable Path for Staff-Line Detection Using Order Statistic Downscaling and Coarse-to-Fine Technique
Hoang-Nam Bui, In Seop Na, Guee-Sang Lee, Hyung-Jeong Yang, Soo-Hyung Kim · 2014
Staff-line detection is the key component in any Optical Music Recognition (OMR) system. The state-of-the-art Stable Path method has the powerful capability on skewed and distorted music sheets. However, the naive cost function calculation and graph-traversing for shortest paths is time consuming. In this paper we present a novel method to overcome this challenge. A coarse-to-fine technique is applied for accelerating the speed of staff-line detection. First, coarse-level staff-line detection is performed on a 2D order-statistic-based scaled binary image to estimate staff-line positions. Second, we estimate staff-line boundaries by interpolation and translation of coarse detection results. Finally, fine-level staff-line detection is applied for refining the result from the first step. Experiments show that our Boosted Stable Path technique can impressively speed-up the naive method, hence a user-friendly mobile OMR application is possible.