对织物表面疵点自动识别方法进行了探讨。
It studied the automatic identifying approach of the fabric surface defects.
减少绢丝表面疵点是绢纺行业质量管理工作的主攻目标。
It is shown that to diminish the apparent defects on spun silk yarn is a priority in waste silk spinning.
在纺织服装工业生产检验过程中，对于纺织品面料表面疵点的检验和识别研究在国内外已进行了相当长的时间。
In the process of the production and checkout of textile and garment industry, the textile fabric surface defects inspection and recognition have been conducted for a long time at home and abroad.
目前国内针对塑料薄膜表面的疵点检测基本上还是由人工视觉完成。
At present, the defect inspection of the pellicle is basically done by human vision in China.
在氟塑料薄膜生产中，薄膜表面产生的疵点是影响薄膜质量的关键因素。
In the production of fluoride plastic film, defects on the surface of the film are the key factors that affect the quality.
由于待检测薄膜表面的大部分区域没有疵点，故采用粗检测—细检测两步策略的图像处理算法。
There are no defects in most areas, so this article chooses two-step strategy of rough detection and fine detection.
根据织物表面图像的灰度强度和织物疵点图像的灰度强度的不同，运用PCNN对织物疵点进行自动检测。
A method of detecting the textile defects by using Pulsed Coupled Neural Network (PCNN) is proposed according to the difference of gray intensity between normal fabric images and textile defects.
根据织物表面图像的灰度强度和织物疵点图像的灰度强度的不同，运用PCNN对织物疵点进行自动检测。
A method of detecting the textile defects by using Pulsed Coupled Neural Network (PCNN) is proposed according to the difference of gray intensity between normal fabric images and textile defects.
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