Boundary Learning by Using Weighted Propagation in Convolution Network

• URL: http://arxiv.org/abs/1905.09226v3
• Date: Thu, 10 Jul 2025 03:33:27 GMT
• Title: Boundary Learning by Using Weighted Propagation in Convolution Network
• Authors: Wei Liu, Jiahao Chen, Chuni Liu, Xiaojuan Ban, Boyuan Ma, Hao Wang, Weihua Xue, Yu Guo,
• Abstract summary: We introduce spatial consistency into network to eliminate defects in raw microscopic image.<n>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.<n>In boundary detection task, it reduces the error rate by 7%, which outperforms state-of-the-art methods by a large margin.
• Score: 11.3458350422287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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