Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2

connected component labeling Related Abstracts

2 Automatic Music Score Recognition System Using Digital Image Processing

Authors: Yuan-Hsiang Chang, Li-Der Jeng, Zhong-Xian Peng

Abstract:

Music has always been an integral part of human’s daily lives. But, for the most people, reading musical score and turning it into melody is not easy. This study aims to develop an Automatic music score recognition system using digital image processing, which can be used to read and analyze musical score images automatically. The technical approaches included: (1) staff region segmentation; (2) image preprocessing; (3) note recognition; and (4) accidental and rest recognition. Digital image processing techniques (e.g., horizontal /vertical projections, connected component labeling, morphological processing, template matching, etc.) were applied according to musical notes, accidents, and rests in staff notations. Preliminary results showed that our system could achieve detection and recognition rates of 96.3% and 91.7%, respectively. In conclusion, we presented an effective automated musical score recognition system that could be integrated in a system with a media player to play music/songs given input images of musical score. Ultimately, this system could also be incorporated in applications for mobile devices as a learning tool, such that a music player could learn to play music/songs.

Keywords: Image Processing, connected component labeling, morphological processing, optical musical recognition

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1 Image Segmentation of Visual Markers in Robotic Tracking System Based on Differential Evolution Algorithm with Connected-Component Labeling

Authors: Chen-Chien Hsu, Wei-Yen Wang, Shu-Yu Hsu

Abstract:

Color segmentation is a basic and simple way for recognizing the visual markers in a robotic tracking system. In this paper, we propose a new method for color segmentation by incorporating differential evolution algorithm and connected component labeling to autonomously preset the HSV threshold of visual markers. To evaluate the effectiveness of the proposed algorithm, a ROBOTIS OP2 humanoid robot is used to conduct the experiment, where five most commonly used color including red, purple, blue, yellow, and green in visual markers are given for comparisons.

Keywords: Humanoid Robot, Differential Evolution, color segmentation, connected component labeling

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