Abstract

Recently, saliency maps are widely used as an image feature to improve results of segmentation or classification of defects. In this paper we tackle the problem of defect detection in fabric images. We propose a defect extraction method in textile images based on a modified anisotropic diffusion filter combined with a saliency image feature. The classical anisotropic diffusion models consider only the image gradient information of a diffused pixel. Consequently, they cannot identify defect edges confused with background texture. Since the defects in the neighborhood of the image generally have larger saliency intensity value than the background, the proposed diffusion model incorporates both local gradient magnitude and a modified saliency map. The aim is to preserve defect edges while effectively removing the textured background. Moreover a new diffusion function is proposed that permits to carry on adaptively a smoothing in faultless regions and a sharpening process at defect boundaries. To confirm the effectiveness of our algorithm, we compare tests and results with different methods on textile images including different kinds of defects and fabrics. Results show that our method outperforms the other considered algorithms and can accurately extracts defects.

摘要

最近，显着图被广泛用作图像特征以改善缺陷的分割或分类结果。在本文中，我们解决了织物图像中的缺陷检测问题。我们提出了一种基于改进的各向异性扩散滤波器结合显着图像特征的纺织品图像缺陷提取方法。经典的各向异性扩散模型只考虑扩散像素的图像梯度信息。因此，他们无法识别与背景纹理相混淆的缺陷边缘。由于图像邻域中的缺陷通常具有比背景更大的显着强度值，所提出的扩散模型结合了局部梯度幅度和修改后的显着图。目的是在有效去除纹理背景的同时保留缺陷边缘。此外，提出了一种新的扩散函数，该函数允许在无缺陷区域中自适应地进行平滑，并在缺陷边界处进行锐化过程。为了确认我们算法的有效性，我们在纺织品图像上比较了不同方法的测试和结果，包括不同类型的缺陷和织物。结果表明，我们的方法优于其他考虑的算法，并且可以准确地提取缺陷。