Purpose

The purpose of this paper is to select the optimal feature parameters to further improve the identification accuracy of cashmere and wool.

Design/methodology/approach

To increase the accuracy, the authors put forward a method selecting optimal parameters based on the fusion of morphological feature and texture feature. The first step is to acquire the fiber diameter measured by the central axis algorithm. The second step is to acquire the optimal texture feature parameters. This step is mainly achieved by using the variance of secondary statistics of these two texture features to get four statistics and then finding the impact factors of gray level co-occurrence matrix relying on the relationship between the secondary statistic values and the pixel pitch. Finally, the five-dimensional feature vectors extracted from the sample image are fed into the fisher classifier.

Findings

The improvement of identification accuracy can be achieved by determining the optimal feature parameters and fusing two texture features. The average identification accuracy is 96.713% in this paper, which is very helpful to improve the efficiency of detector in the textile industry.

Originality/value

In this paper, a novel identification method which extracts the optimal feature parameter is proposed.

中文翻译：

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两种特征融合的羊绒和羊毛识别方法

目的

本文的目的是选择最优的特征参数，进一步提高羊绒和羊毛的识别精度。

设计/方法/方法

为了提高准确率，作者提出了一种基于形态特征和纹理特征融合的最优参数选择方法。第一步是获取通过中心轴算法测量的纤维直径。第二步是获取最优纹理特征参数。这一步主要是利用这两个纹理特征二次统计的方差得到四个统计量，然后依靠二次统计值与像素间距的关系找到灰度共生矩阵的影响因素。最后，将从样本图像中提取的五维特征向量输入到 Fisher 分类器中。

发现

通过确定最优特征参数，融合两个纹理特征，可以提高识别精度。本文平均识别准确率为96.713%，对提高纺织行业检测仪的效率很有帮助。

原创性/价值

本文提出了一种提取最优特征参数的新识别方法。