【作者】 龙世忠；

【导师】 石美红；

【作者基本信息】 西安工程大学 ， 计算机应用技术， 2008， 硕士

【摘要】 在纺织品生产中,质量控制与检测是非常重要的一环,织物疵点检测又是其中最为主要的组成部分。长期以来,织物疵点的检测大都由人工来完成,这种方法不仅存在着劳动强度大,效率低,漏检率高,检测结果易受检验人员主观因素影响等弊端,而且长时间专心工作,对人体健康也很不利。据统计,人工检测一般只能检测出40%-60%的疵点。随着计算机技术、数字图像技术及信息化技术的发展,基于图像处理的织物疵点检测技术得到了人们的广泛关注。然而如何提高疵点检测能力一直是国内外学者研究的热点和难点问题。由于小波变换引入了尺度因子,具有在时频两域中表征信号局部特征的能力,常被人们誉为“数学显微镜”。因此,将小波应用于疵点分割的研究已被证实具有重要的应用价值。本文在深入分析了小波理论的基础上,着重研究和分析了它在织物疵点分割上的应用,提出了基于自适应小波的织物疵点自动分割方法,其主要工作包括:(1)针对织物图像的纹理特点,基于小波正交约束条件,利用小波分解得到的小波系数提取纹理特征,提出了以疵点区和背景区的能量均值比作为目标函数构造小波的方法,实现了基于自适应小波的织物疵点分割;(2)针对织物疵点边缘分割的问题,研究和分析了“最佳”边缘检测滤波器设计的准则,提出了以边缘和非边缘之间的模值均值比、最优检测和定位精度的积为优化目标函数构造小波的方法,实现了自适应双正交小波的织物疵点的边缘分割。经实验结果表明,与已有的方法相比,本文方法具有较高的疵点分割能力,同时具有良好自适应性和鲁棒性。本文最后总结了全文工作,并给出了本课题今后的建议研究方向。更多还原

【Abstract】 Defect inspection is a vital step for quality assurance in fabric production. For a long time, fabric defect detection is primarily performed by human vision, by which there is high rate of missed detection, low efficiency and low reliability. That manual defect inspection ratio only is 40% -60% is indicated based on statistic. With the development of computer and digital image processing technology,computer vision technology has been increasingly applied to detect and classify fabric defects automatically to replace the hand-counting method. But how to improve the efficiency and reliability of fabric inspection has been a focal point in fabric inspection research, and it remains challenging. Wavelet analysis, which has the characteristic of preserving and exhibiting a locality and multi-scale of position-frequency representation for analyzing localized features on an image, often be called as "mathematical microscope". Therefore, scholars pay high attention to the research of fabric defect segmentation based on wavelet, which has been proven to play an important role in defect inspection.By analyzing wavelet transform theory and methods of fabric defect segmentation based on wavelet in depth, approaches of automatically segmenting fabric defect based on adaptive wavelet are proposed, which include: (1) In view of features of different textures distribution between a defect-free fabric and a defective fabric, according to the restrictions of orthogonal wavelet, fabric texture feature is extracted, and the fabric defect segmentation based on an adaptive wavelet is performed by a target function of average energy ratio between them; (2) Aiming at edge segmentation of fabric defect, a design criteria of "best" edge detection filter is researched and analyzed, a approach of fabric defect inspection based on an adaptive bi-orthogonal wavelet is presented by choosing a optimal target function of maximal average modulus ratio between the edge and non-edge. The experimental results show that these approaches have a good ability to detect fabric defect and robustness, compared with the existing methods. Finally, this paper summarizes done works, and gives the recommendations on the future research direction in brief.更多还原