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A Classification Method of Oracle Materials Based on Local Convolutional Neural Network Framework.

Shanxiong Chen, Han Xu, Gao Weizhe

    IEEE Computer Graphics and Applications
    |February 23, 2020
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    Summary
    This summary is machine-generated.

    This study introduces a new AI method for classifying oracle bone materials, improving accuracy in distinguishing tortoise and animal bones. This advances oracle bone morphology studies by automating a complex, experience-based classification process.

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    Area of Science:

    • Archaeology
    • Material Science
    • Computer Science

    Background:

    • Oracle bone material classification is crucial for morphology studies but traditionally relies on expert experience.
    • Existing methods require extensive training and knowledge accumulation, limiting accessibility and consistency.

    Observation:

    • Distinctive features like "shield grain" and "tooth grain" are present on oracle bone rubbings.
    • These features allow for the division of oracle bone images into multiple, distinct regions.

    Findings:

    • A multiregional convolutional neural network (CNN) was developed to classify oracle bone rubbings.
    • The CNN effectively extracts and fuses features from multiple regions, achieving accurate classification of tortoise shell and animal bone materials.

    Implications:

    • This AI-driven approach automates and enhances the accuracy of oracle bone material classification.
    • It contributes to the objective study of oracle bone morphology and provides a valuable tool for researchers.