使用哈尔小波方法和灰度共生矩阵进行肉质纹理图像分类

IF 3.8 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
K. Kiswanto, H. Hadiyanto, Eko Sediyono
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引用次数: 0

摘要

这项研究旨在研究如何利用图像处理和纹理分析,找到一种更可靠、更有效的解决方案,根据肉的纹理来识别和分类肉类。所使用的方法包括特征提取、Haar 小波和灰度共现矩阵(GLCM)(角度为 0°、45°、90° 和 135°),并辅以对比度矩阵、相关性矩阵、能量矩阵、同质性矩阵和熵矩阵。测试结果表明,k-NN 算法在识别新鲜肉(99%)、冷冻肉(99%)和腐肉(96%)的纹理方面表现出色,准确率很高。GLCM 算法的结果很好,尤其是在新鲜肉(183.21)和腐肉(115.79)的纹理图像上。Haar 小波的结果低于 k-NN 算法和 GLCM,但这种方法对识别新鲜肉(89.96)的纹理图像仍然有用。这项研究成果有望大大提高今后根据肉的纹理识别和分类的准确性和效率,减少人为误差,并有助于及时评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Meat Texture Image Classification Using the Haar Wavelet Approach and a Gray-Level Co-Occurrence Matrix
This research aims to examine the use of image processing and texture analysis to find a more reliable and efficient solution for identifying and classifying types of meat, based on their texture. The method used involves the use of feature extraction, Haar wavelet, and gray-level co-occurrence matrix (GLCM) (with angles of 0°, 45°, 90°, and 135°), supported by contrast, correlation, energy, homogeneity, and entropy matrices. The test results showed that the k-NN algorithm excelled at identifying the texture of fresh (99%), frozen (99%), and rotten (96%) meat, with high accuracy. The GLCM provided good results, especially on texture images of fresh (183.21) and rotten meat (115.79). The Haar wavelet results were lower than those of the k-NN algorithm and GLCM, but this method was still useful for identifying texture images of fresh meat (89.96). This research development is expected to significantly increase accuracy and efficiency in identifying and classifying types of meat based on texture in the future, reducing human error and aiding in prompt evaluation.
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来源期刊
Applied System Innovation
Applied System Innovation Mathematics-Applied Mathematics
CiteScore
7.90
自引率
5.30%
发文量
102
审稿时长
11 weeks
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