论文标题
图像识别增强的几何表面图像预测
Geometric Surface Image Prediction for Image Recognition Enhancement
论文作者
论文摘要
这项工作提出了一种从照片中预测几何表面图像以帮助图像识别的方法。为了识别对象，训练模型或对预训练模型进行微调需要几个来自不同条件的图像。在这项工作中，与其颜色图像相比，引入了几何表面图像，以更好地表示，以克服照明条件。从颜色图像预测表面图像。为此，几何表面图像及其颜色照片首先通过生成对抗网络（GAN）模型训练。然后使用训练的发电机模型来预测输入颜色图像的几何表面图像。对护身符识别的案例研究的评估表明，在不同的照明条件下，预测的几何表面图像比其颜色图像较少的歧义性含糊不清，并且可以有效地用于协助图像识别任务。
This work presents a method to predict a geometric surface image from a photograph to assist in image recognition. To recognize objects, several images from different conditions are required for training a model or fine-tuning a pre-trained model. In this work, a geometric surface image is introduced as a better representation than its color image counterpart to overcome lighting conditions. The surface image is predicted from a color image. To do so, the geometric surface image together with its color photographs are firstly trained with Generative Adversarial Networks (GAN) model. The trained generator model is then used to predict the geometric surface image from the input color image. The evaluation on a case study of an amulet recognition shows that the predicted geometric surface images contain less ambiguity than their color images counterpart under different lighting conditions and can be used effectively for assisting in image recognition task.
