Abstract
Image based object fragments rejoining can avoid touching and damaging objects, and be applied to recover fragments of oracle bones, artifacts, paper money, calligraphy and painting files. However, traditional methods are insufficient in terms of judging whether two images’ texture are rejoinable. In this paper, we propose a deep rejoining model (DRM) for automatic rejoining of oracle bone fragment images. In our model, an edge equal distance rejoining method (EEDR) is used to locate the matching position of the edges of two fragment images and crop the target area image (TAI), then a convolution neural network (CNN) is used to evaluate the similarity of texture in TAI. To improve the performance of similarity evaluation, a maximum similarity pooling (MSP) layer is proposed in CNN, and the fully connected layer outputs the two-class probability of whether the rejoining is eligible or not. Our experiments show that DRM achieved state-of-the-arts performance in rejoining oracle bone fragment images and has stronger adaptability.
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Acknowledgements
This work has been supported by the National Natural Science Foundation of China (62106007, 61806007), Department of Science and Technology of Henan Province (212102310549) and Anyang Normal University Science and Technology Research Project (2021C01GX012).
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Zhang, Z., Wang, YT., Li, B., Guo, A., Liu, CL. (2022). Deep Rejoining Model for Oracle Bone Fragment Image. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13189. Springer, Cham. https://doi.org/10.1007/978-3-031-02444-3_1
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