Detection of surface defects is one of the most important issues in the field of image processing and machine vision. In this article, a method for detecting surface defects based on energy changes in co-occurrence matrices is presented. The presented method consists of two stages of training and testing. In the training phase, the co-occurrence matrix operator is first applied on healthy images and then the amount of output energy is calculated. In the following, according to the changes in the amount of energy, a suitable feature vector is defined, and with the help of it, a suitable threshold for the health of the images is obtained. Then, in the test phase, with the help of the calculated quorum, the defective parts are distinguished from the healthy ones. In the results section, the mentioned method has been applied on stone and ceramic images and its detection accuracy has been calculated and compared with some previous methods. Among the advantages of the presented method, we can mention high accuracy, low calculations and compatibility with all types of levels due to the use of the training stage. The proposed approach can be used in medical applications to detect abnormalities such as diseases. So, the performance is evaluated on 2d-hela dataset to classify cell phenotypes. The proposed approach provides about 89.56 percent accuracy on 2d-hela.
翻译:表面缺陷的检测是图像处理和机器视觉领域最重要的问题之一。在本篇文章中,介绍了一种基于共生基质能量变化的表面缺陷检测方法。介绍的方法由培训和测试的两个阶段组成。在培训阶段,共生矩阵操作员首先应用健康图像,然后计算出输出能量量。以下,根据能量量的变化,界定了合适的特性矢量,并在它的帮助下,为图像的健康确定了适当的阈值。然后,在测试阶段,在计算到的法定人数的帮助下,将有缺陷的部分与健康部分区分开来。在结果部分,上述方法已应用于石块和陶瓷图像,其检测准确性已经计算,并与以前的一些方法进行了比较。在介绍的方法的优点中,我们可以提到由于使用培训阶段而具有很高的准确性、低的计算率和与所有等级的兼容性。拟议方法可用于医疗应用,以检测疾病等异常性。因此,对性能进行了区分。在计算结果部分中,对2-HEL-56方法的性能进行了评估,以2-HER5方法提供了2类分类的精确度。