Boundary learning by using weighted propagation in convolution network

In material science, image segmentation is of great significance for quantitative analysis of microstructures. Here, we propose a novel Weighted Propagation Convolution Neural Network based on U-Net (WPU-Net) to detect boundary in poly-crystalline microscopic images. We introduce spatial consistency into network to eliminate the defects in raw microscopic image. And we customize adaptive boundary weight for each pixel in each grain, so that it leads the network to preserve grain's geometric and topological characteristics. Moreover, we provide our dataset with the goal of advancing the development of image processing in materials science. Experiments demonstrate that the proposed method achieves promising performance in both of objective and subjective assessment. In boundary detection task, it reduces the error rate by 7%, which outperforms state-of-the-art methods by a large margin.

Automated summary

This paper proposes a novel Weighted Propagation Convolution Neural Network based on U-Net (WPU-Net) for boundary detection in poly-crystalline microscopic images. By introducing spatial consistency and adaptive boundary weight, the method achieves promising performance in image segmentation.