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.

中文翻译：

---

使用图像归一化增强图像识别性能

当识别由相机捕获的图像中的特定对象时，我们提取局部描述符以将其与图像进行比较，或者尝试通过使用卷积神经网络的学习方法对图像进行直接比较。具有许多特征的对象越多，学习中使用的图像越多，则比较特征就越容易。它还使检测图像是否包含该功能变得更加容易，从而有助于生成准确的识别结果。然而，当图像中的待识别对象的特征明显小于背景区域时或当要学习的图像的区域不足时，在提高识别性能方面存在限制。在本文中，