基于局部统计特征与整体显著性分析的织物疵点检测方法

Fabric defect detection method based on local statistical characteristics and overall significance analysis

Abstract

The invention discloses a fabric defect detection method based on local statistical characteristics and overall significance analysis. The fabric defect detection method includes local texture and gray statistical characteristic extraction, visual saliency map generation and visual saliency map segmentation. Firstly, an image is subjected to blocking, and local texture and gray statistical characteristics of image blocks are extracted; then, K other image blocks are randomly selected as for each current image block, the contrast ratio between statistical characteristics of the current image block and statistical characteristics of other image blocks is calculated, and visual saliency maps are generated based on overall significance analysis; finally, the saliency maps are segmented according to the optimal threshold iteration segmentation algorithm to acquire the fabric defect detection result. By means of the method, fabric texture statistical characteristics and gray statistical characteristics are comprehensively taken into consideration, and high detection precision is achieved; training samples are not needed, and the self-adaptability is strong; the calculation speed is high and on-line detection is facilitated.
本发明公开了一种基于局部统计特征与整体显著性分析的织物疵点检测方法,包括局部纹理和灰度统计特征提取、视觉显著图生成和视觉显著图分割三部分。首先对图像进行分块,提取图像块的局部纹理和灰度统计特征;其次针对每个当前图像块,随机选取K个其它图像块,计算当前图像块与其它图像块统计特征之间的对比度,完成基于整体显著性分析生成视觉显著图;最后采用基于迭代最优阈值分割算法对显著图进行分割,得到织物疵点检测结果。本方法综合考虑织物纹理统计特征和灰度统计特征,具有较高的检测精度;且本方法不需要训练样本,自适应能力强;计算速度较快,适合在线检测。

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