新的基于图像显著性区域特征的织物疵点检测算法

鉴于织物疵点类型的多样性和传统人工检测方法的低效率，为更有效地检测织物疵点，提出一种新的基于图像显著性特征的织物疵点检测方法——SGE。将原织物图分成相同两份:一份利用改进的基于频率的显著性区域(FSR)方法提取区域特征，粗定位疵点位置。另一份先Gabor滤波，取Gabor模图为输出特征；再利用基于像素的显著性区域(PSR)方法进行区域特征提取，细定位疵点位置;然后利用最大熵分别对粗细定位的疵点图进行分割，再融合;最后描绘轮廓,计算周长和面积,去除孤立点,得最终检测结果。采用OpenCV算法库，选取了4种具有代表的织物疵点图片进行验证。实验结果表明，这种粗细定位疵点的方法能够获得较好的检测结果，无需事先学习，能够满足实时性要求。

New approach of fabric defects detection based on saliency region feature

Concerning the diversity of fabric defect type and low efficiency of traditional artificial detection methods,in order to detect the fabric defect more effectively,a new approach,SGE,based on saliency region feature for fabric defect detection was studied.In this approach,the original image was divided into two parts,one was used to extracted the saliency region feature of fabric defect by improved Frequency-tuned Saliency Region(FSR) method roughly,another was used to employ the Gabor filter with the amplitude as an output characteristic,and to extract the saliency region feature of fabric defect by Pixel Saliency Region(PSR) method accurately,then by using maximum entropy to segment the saliency region respectively and to fuse the sub-images.The result was got by calculating perimeter and area of the contours to remove the isolated points.The experiment selected four types of typical fabric defect images and OpenCV library was used.The experimental result shows that the algorithm,without prior learning,meets the real-time requirement.