基于自适应域值分割与力矩的棉花异性纤维分类方法

Classification method of adaptive threshold segmentation algorithm and moment for foreign fibers in cotton

为能够准确统计出棉花中所含异性纤维的重量和数目,提出一种机器视觉与图像处理技术,对棉花异性纤维进行检测分类。在提取棉花异性纤维原始图像的基础上,采用灰度处理和滤波技术完成图像的预处理,采用自适应域值技术来完成棉花异性纤维图像分割,在分割出的二值化图像基础上,采用挖空内点法和邻域搜索法进行轮廓提取,提出以异性纤维轮廓的面积与周长平方之比作为力矩,对棉花异性纤维进行分类。通过对300个棉花异性纤维样本图像进行了试验,分类准确率可以达到96%。结果表明该技术和分类力矩可以准确的对棉花异性纤维进行初分类。

This paper reports tests and classification of the foreign fibers in cotton using machine vision and image processing technology in order to get numbers and weight of the foreign fiber.With gray-scale transforming and filtering technology,the authors pre-treated the original extracted image of the foreign fibers in cotton and presented image segmentation method based on self-adaptation threshold partition algorithm.The segmented binary images were processed for contour extraction and classified which takes the ratio of area of foreign fiber contour to perimeter square as the moment and adopts interior pixel points scooping method and neighborhood search method.The tests included 300 foreign fiber cotton images and the classification accuracy rate reached 96%.The results show this method is effective and accurate.