Fragments Adjacency Prediction: A Contour Based Approach Using Transformer Models with Rotary Positional Encoding

This paper introduces an automated deep learning method for predicting adjacency relationships between 2D fragments, with potential applications in archaeology and other domains requiring fragment pair identification. Such tasks often require the identification of fragment matching within highly irregular, eroded datasets, where conventional techniques struggle with scalability and generalization. To address these computational challenges, we propose a transformer-based neural network architecture that leverages contour-based features and integrates Rotary Positional Encoding (RoPE) to enhance spatial relationship modeling. Unlike traditional approaches that rely on exhaustive pairwise comparisons or similarity matrices, our method directly predicts adjacency relationships, significantly improving computational efficiency. Given the scarcity of real-world datasets, we developed a synthetic data generation framework capable of simulating diverse fragment patterns to enhance model robustness and mitigate overfitting. Experimental evaluations, by achieving an accuracy of 82%, demonstrate that accurate adjacency predictions can be achieved using minimal input features, highlighting the effectiveness of contour-based representations. While our model has been validated on synthetic datasets, its performance can be further refined through fine-tuning with real-world archaeological and forensic fragment data. This work lays the foundation for future advancements incorporating additional modalities such as texture and color, ultimately contributing to more reliable and scalable fragment matching systems for practical applications.

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