摘要
Researches on pattern recognition have been tremendously performed in various fields because of its wide use in both machines and human beings. Previously, traditional methods used to study pattern recognition problems were not strong enough to recognize patterns accurately as compared to optimization algorithms. In this study, we employ both traditional based methods to detect the edges of each pattern in an image and apply convolutional neural networks to classify the right and wrong pattern of the cropped part of an image from the raw image. The results indicate that edge detection methods were not able to detect clearly the patterns due to low quality of the raw image while CNN was able to classify the patterns at an accuracy of 84% within 1.5 s for 10 epochs.
Researches on pattern recognition have been tremendously performed in various fields because of its wide use in both machines and human beings. Previously, traditional methods used to study pattern recognition problems were not strong enough to recognize patterns accurately as compared to optimization algorithms. In this study, we employ both traditional based methods to detect the edges of each pattern in an image and apply convolutional neural networks to classify the right and wrong pattern of the cropped part of an image from the raw image. The results indicate that edge detection methods were not able to detect clearly the patterns due to low quality of the raw image while CNN was able to classify the patterns at an accuracy of 84% within 1.5 s for 10 epochs.
