Application of Gray Projection Algorithm to the Quantization of Width between Textures of Instrument

Application of Gray Projection Algorithm to the Quantization of Width between Textures of Instrument

Wentao Gao, Yinglai Huang, Peng Zhao, Yue Sun, Sainan Niu
Copyright: © 2018 |Pages: 15
DOI: 10.4018/JITR.2018070106
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Abstract

This article describes how in the production process of musical instruments, the width between textures in the panel determine their quality directly. In traditional production process, people observe the texture by eyes to determine whether the panel qualified. This method is inefficient and has a large error, which restricts the industrial production of musical instruments. This article proposes a method of quantitative analysis of panel's texture by using digital image processing technology and gray projection algorithm. Since the method is a non-contact one and the key parameters of the panel texture will be acquired, then the selector can determine the suitable panel. Thus, damage to a panel caused by the contact type when manually selected can be avoided. Experiments show that the method has better accuracy which avoids the errors caused by manual measurement and greatly improves the efficiency of panel selection. Therefore, it is of great significance for industrial production of traditional instruments.
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2. Theoretical Basis

Texture is a ubiquitous visual phenomenon, generally, it is believed that a texture is a change or repetition of an image's grayscale or color in space (Liu, 2008). Because of the different types of instrument’s panels and the way they are cut, the panels showing a unique texture pattern in different sections. The texture of the instrument is closely related to the sound quality of the instrument, so it is necessary to carry out targeted testing of the key parameters of the panel’s texture. The texture of instrument’s panel can be considered as a pattern which is produced by the change of the gray scale in the space, and it reflects a trend of the change of the pixel gray level in the image.

Through the above analysis of the texture, we can see that the gray values of the image will change dramatically in the positions of textures. Based on it, firstly, the image of the instrument’s panel is denoised and grayscale processed, and then through the formula to calculate the gray value of each pixel of image and draw the image of the overall and local gray projection curves. As the gray value of the pixel at the texture changes more clearly, the valley of the curve is marked in the gray scale projection curve, and the distance between two adjacent troughs is the width of two adjacent textures of the instrument’s panel, and finally achieves a quantitative analysis of musical instrument panel’s textures. Experiments show that the proposed method in this paper is more scientific and more accurate than the traditional method.

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