基于机器视觉的鞋面特征点自动识别改进方法
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摘要
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。
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.
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.
引文
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[12] 张丽, 陈志强, 高文焕,等. 均值加速的快速中值滤波算法[J]. 清华大学学报(自然科学版), 2004, 44(9):1157-1159.ZHANG Li, CHEN Zhiqiang, GAO Wenhuan, et al. Mean-based fast median filter[J]. Journal of Tsinghua University (Natural Science Edition), 2004, 44(9):1157-1159.
[2] 李文羽, 程隆棣. 基于机器视觉和图像处理的织物疵点检测研究新进展[J]. 纺织学报, 2014, 35(3):158-164.LI Wenyu, CHENG Longdi. New progress of fabric defect detection based on computer vision and image processing [J]. Journal of Textile Research, 2014, 35(3):158-164.
[3] 张开玉, 梁凤梅. 基于改进SURF的图像配准关键算法研究[J]. 科学技术与工程, 2013, 13(10):2875-2879.ZHANG Kaiyu, LIANG Fengmei. Research on the key algorithm for image matching based on improved SURF[J]. Science Technology and Engineering, 2013, 13(10):2875-2879.
[4] SINHA S N, FRAHM J M, POLLEFEYS M, et al. Feature tracking and matching in video using programm able graphics hardware E[J]. Machine Vision & Applications, 2011, 22(1):207-217.
[5] 章毓晋. 图象分割评价技术分类和比较[J]. 中国图象图形学报, 1996, 1(2): 151-158.ZHANG Yujin.A classification and comparison of evalution techniques for image segmentation[J]. Journal of Image and Graphic, 1996, 1(2):151-158.
[6] 张连宽, PAUL Weckler, 肖德琴. 作物叶面图像自动分割方法[J]. 江苏大学学报(自然科学版), 2016, 37(6):663-669.ZHANG Liankuan, PAUL Weckler, XIAO Deqin. Automtic leaf surface region segmentation from crop image[J]. Journal of Jiangsu University(Natural Science Edition), 2016, 37(6):663-669.
[7] 李伟涛, 彭道黎, 吴见. 基于改进边缘分割算法的幼苗信息提取[J]. 农业机械学报, 2014, 45(4):259-263.LI Weitao, PENG Daoli, WU Jian. Extraction of seedlings information based on improved edge segmentation algorithm[J]. Transactions of The Chinese Society for Agricultural Machinery, 2014, 45(4):259-263.
[8] OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems Man & Cybernetics, 2007, 9(1):62-66.
[9] CHEN Q, ZHAO L, LU J, et al. Modified two-dimensional Otsu image segmentation algorithm and fast realisation[J]. Iet Image Processing, 2012, 6(4):426-433.
[10] 赵于前, 杨元, 王琨. 基于模糊集理论的迭代多值化图像分割[J]. 光电子·激光, 2009, 20(10):1403-1409.ZHAO Yuqian, YANG Yuan, WANG Kun. Iterative multi-level image segmentation based on fuzzy set theory[J]. Journal of Optoelectronics Laser, 2009, 20(10):1403-1409.
[11] KO S J, LEE S J. Center weighted median filter and their applications to image enhancement[J]. IEEE Transactions on Circuits and System, 1991, 38(1):984-993.
[12] 张丽, 陈志强, 高文焕,等. 均值加速的快速中值滤波算法[J]. 清华大学学报(自然科学版), 2004, 44(9):1157-1159.ZHANG Li, CHEN Zhiqiang, GAO Wenhuan, et al. Mean-based fast median filter[J]. Journal of Tsinghua University (Natural Science Edition), 2004, 44(9):1157-1159.