论文标题
使用图像细分方法将卷积神经网络应用于极稀疏的图像数据集
Applying convolutional neural networks to extremely sparse image datasets using an image subdivision approach
论文作者
论文摘要
目的：这项工作的目的是证明卷积神经网络（CNN）可以通过原始图像数据集的细分应用于极度稀疏的图像库。方法：创建了传统数码相机的图像数据集，并从文献中获得了扫描电子显微镜（SEM）测量。细分图像数据集，并在细分数据集的一部分上训练CNN模型。结果：CNN模型能够利用所提出的图像细分方法来分析极度稀疏的图像数据集。此外，有可能直接评估给定的API或外观占主导地位的各个区域。
Purpose: The aim of this work is to demonstrate that convolutional neural networks (CNN) can be applied to extremely sparse image libraries by subdivision of the original image datasets. Methods: Image datasets from a conventional digital camera was created and scanning electron microscopy (SEM) measurements were obtained from the literature. The image datasets were subdivided and CNN models were trained on parts of the subdivided datasets. Results: The CNN models were capable of analyzing extremely sparse image datasets by utilizing the proposed method of image subdivision. It was furthermore possible to provide a direct assessment of the various regions where a given API or appearance was predominant.
