Deep Rejoining Model for Oracle Bone Fragment Image

Zhang, Zhan; Wang, Yun-Tian; Li, Bang; Guo, An‐Yuan; Liu, Cheng‐Lin

doi:10.1007/978-3-031-02444-3_1

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…On the basis of edge contour guided Resnet50, a multi-scale fusion strategy is introduced to enhance the shallow detail features of bone marker images. While preserving the deep feature R4, additional fusion features of different scales are incorporated with dimensions being (56, 56, 256), (28,28,512), (14,14,1024), and (7,7,2048), respectively. The aforementioned four feature maps undergo adaptive average pooling layers to obtain features with dimensions (1, 1, 256), (1, 1, 512), (1, 1, 1024), and (1, 1, 2048).…”

Section: Edge Contour-guided Feature Fusion Residual Networkmentioning

confidence: 99%

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Research on Rejoining Bone Stick Fragment Images: A Method Based on Multi-Scale Feature Fusion Siamese Network Guided by Edge Contour

He,

Wang,

Liu

et al. 2024

Applied Sciences

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The rejoining of bone sticks holds significant importance in studying the historical and cultural aspects of the Han Dynasty. Currently, the rejoining work of bone inscriptions heavily relies on manual efforts by experts, demanding a considerable amount of time and energy. This paper introduces a multi-scale feature fusion Siamese network guided by edge contour (MFS-GC) model. Constructing a Siamese network framework, it first uses a residual network to extract features of bone sticks, which is followed by computing the L2 distance for similarity measurement. During the extraction of feature vectors using the residual network, the BN layer tends to lose contour detail information, resulting in less conspicuous feature extraction, especially along fractured edges. To address this issue, the Spatially Adaptive DEnormalization (SPADE) model is employed to guide the normalization of contour images of bone sticks. This ensures that the network can learn multi-scale boundary contour features at each layer. Finally, the extracted multi-scale fused features undergo similarity measurement for local matching of bone stick fragment images. Additionally, a Conjugable Bone Stick Dataset (CBSD) is constructed. In the experimental validation phase, the MFS-GC algorithm is compared with classical similarity calculation methods in terms of precision, recall, and miss detection rate. The experiments demonstrate that the MFS-GC algorithm achieves an average accuracy of 95.5% in the Top-15 on the CBSD. The findings of this research can contribute to solving the rejoining issues of bone sticks.

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Section: Edge Contour-guided Feature Fusion Residual Networkmentioning

confidence: 99%

“…In such cases, the texture and text style of the fracture region provide more accurate high-level information. Zhang et al [13,14] proposed a deep rejoining model for automatically rejoining oracle bone fragment images. The edge equidistant reconnection method is utilized to match and locate the edges of the two fragmented images.…”

Section: Introductionmentioning

confidence: 99%

Research on Rejoining Bone Stick Fragment Images: A Method Based on Multi-Scale Feature Fusion Siamese Network Guided by Edge Contour

He,

Wang,

Liu

et al. 2024

Applied Sciences

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“…Oracle bone fragment image sets were obtained by scanning oracle bone description books, and the scanner's type was Tengdahanlong OS12002. In this paper, we established a positive and negative sample image set of a local region image of an oracle bone fragment; the image set includes approximately 116,000 unrejoinable local region images that were achieved by the EEM algorithm from the ZLC oracle bone fragment image set (from institute of history of the Chinese Academy of Social Sciences), and it includes 23,000 rejoinable local region images that were cut from the Bingbian, Huadong image set et al [23]. The train set and test set account for 75% and 25% of the whole data set, respectively, as shown in Figure 8.…”

Section: Experiments Verification 41 Experiments Platform and Datasetmentioning

confidence: 99%

Internal Similarity Network for Rejoining Oracle Bone Fragment Images

Zhang

Guo

2022

Symmetry

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Rejoining oracle bone fragments plays an import role in studying the history and culture of the Shang dynasty by its characters. However, current computer vision technology has a low accuracy in judging whether the texture of oracle bone fragment image pairs can be put back together. When rejoining fragment images, the coordinate sequence and texture features of edge pixels from original and target fragment images form a continuous symmetrical structure, so we put forward an internal similarity network (ISN) to rejoin the fragment image automatically. Firstly, an edge equidistant matching (EEM) algorithm was given to search similar coordinate sequences of edge segment pairs on the fragment image contours and to locally match the edge coordinate sequence of an oracle bone fragment image. Then, a target mask-based method was designed in order to put two images into a whole and to cut a local region image by the local matching edge. Next, we calculated a convolution feature gradient map (CFGM) of the local region image texture, and an internal similarity pooling (ISP) layer was proposed to compute the internal similarity of the convolution feature gradient map. Finally, ISN was contributed in order to evaluate a similarity score of a local region image texture and to determine whether two fragment images are a coherent whole. The experiments show that the correct judgement probability of ISN is higher than 90% in actual rejoining work and that our method searched 37 pairs of correctly rejoined oracle bone fragment images that have not been discovered by archaeologists.

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