基于PCNN的织物疵点边缘检测

由于纱线的螺旋性、粗细不匀和织物的柔性形变,使得织物的纹理带有较大的不规则性。 用基于特征或模型的分割方法识别织物纹理图像的疵点,效率较低,准确性较差。针对这个问题,提 出了一种基于PCNN的算法,它利用织物表面疵点区域的灰度强度不同于织物表面图像的灰度强度, 根据PCNN神经元是否点火,来获取织物疵点信息;然后将所提取的特征点按作用范围膨胀,并用 CANNY算子分割出织物疵点,提取织物疵点边缘。实验证明这种方法能有效地获取织物疵点特征, 并得到较为理想的边缘检测效果。

Edge detection for textile defects based on PCNN

Textile texture has much anomaly,because of yarn helix structure,different size of yarn and supple transform for textile itself. The methods for detecting the textile defects with feature and model to segment were low efficiency and not good enough in precision. A method of feature extraction of the textile defects by using Pulsed Coupled Neural Network(PCNN) was put forward to overcome these problems. The model and properties of PCNN was analyzed. According to different gray intensity between the field of textile defects and the field of normal textile, feature of textile defects were extracted for PCNN firing or not. After dilated, the textile defects' edge were extracted with CANNY operator. Experiment shows that the method can much better get the feature of the textile defects and has a much better edge detection result of the textile defects.