基于机器视觉的鞋面特征点自动识别改进方法

针对目前人工识别鞋面特征点方法实时性差,效率低,成本高的问题,提出一种基于机器视觉的鞋面特征点自动识别改进方法。首先,采用改进中值滤波法对采集图像进行预处理消除噪声干扰;其次,运用提出的自适应阈值分割法提取特征点关键区域;最后通过图像形态学处理和计算最小外接圆完成特征点的自动识别。为验证该方法的可靠性,在光强变化和非常规条件下对大量鞋面样本进行分组实验,并与传统一维和二维Otsu算法的检测结果进行对比。结果表明,该方法在多种复杂环境下具有更好的识别精度和鲁棒性,识别成功率在93%以上,且检测时间不超过0.5 s,可满足工业生产中的精度和实时性需求。

Improvement recognition method of vamp's feature points based on machine vision

Focusing on the problems of poor real-time,low efficiency and high cost of artificial recognition in vamps feature points,an improved method was proposed to automatically recognize the feature points of vamps by machine vision technology. Firstly,an improved median filter was used for preprocessing the grabbed images to eliminate noise interference. Secondly,by using the proposed adaptive threshold segmentation method, key regions of feature points were extracted. Finally,by morphological image processing and calculating the minimum circumscribed circle, the automatic identification of feature points was completed. In order to verify the reliability of the proposed method,group experiments were carried out on a large number of vamps samples under the condition of light intensity change and clutter,and the results were compared with the conventional one-dimensional and two-dimensional Otsu algorithm. The experimental results show that this method has better recognition accuracy and robustness in a variety of complex environments, the recognition success rate is above 93%, and the detection time is shorter than 0.5 s,which meets the demand of precision and real-time in industrial production.