Image recognition performance enhancements using image normalization

When recognizing a specific object in an image captured by a camera, we extract local descriptors to compare it with or try direct comparison of images through learning methods using convolutional neural networks. The more the number of objects with many features, the greater the number of images used in learning, the easier it is to compare features. It also makes it easier to detect if the image contains the feature, thus helping generate accurate recognition results. However, there are limitations in improving the recognition performance when the feature of the object to be recognized in the image is significantly smaller than the background area or when the area of the image to be learned is insufficient. In this paper, we propose a method to enhance the image recognition performance through feature extraction and image normalization called the preprocessing process, especially useful for electronic objects with few distinct recognition characteristics due to functional/material specificity.