支持向量机
人工智能
直方图
棱锥(几何)
计算机科学
稳健性(进化)
模式识别(心理学)
定向梯度直方图
分割
特征提取
边缘检测
计算机视觉
图像(数学)
图像处理
数学
基因
生物化学
化学
几何学
标识
DOI:10.56042/ijftr.v45i1.22046
摘要
In order to effectively improve the detection probabilityfor different types of fabrics and defects, a fabric defectdetection method based on pyramid histogram of edge orientationgradients (PHOG) and support vector machine (SVM) has beenproposed. The algorithm combines fabric texture statisticalmethod and machine learning method. It has two main parts,namely the feature extraction and classification. The detectionprocess mainly includes image segmentation, PHOG featureextraction, SVM model training and detection classification. Thesimulation results show that, based on the detection rate and thefalse alarm rate, the algorithm has a good detection andclassification effect, has a certain robustness, and can be appliedto the actual production department.
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