Art is widely recognized as a reflection of civilization and mosaics represent an important part of cultural heritage. Mosaics are an ancient art form created by arranging small pieces, called tesserae, on a surface using adhesive. Due to their age and fragility, they are prone to damage, highlighting the need for digital preservation. This paper addresses the problem of digitizing mosaics by segmenting the tesserae to separate them from the background within the broader field of Image Segmentation in Computer Vision. We propose a method leveraging Segment Anything Model 2 (SAM 2) by Meta AI, a foundation model that outperforms most conventional segmentation models, to automatically segment mosaics. Due to the limited open datasets in the field, we also create an annotated dataset of mosaic images to fine-tune and evaluate the model. Quantitative evaluation on our testing dataset shows notable improvements compared to the baseline SAM 2 model, with Intersection over Union increasing from 89.00% to 91.02% and Recall from 92.12% to 95.89%. Additionally, on a benchmark proposed by a prior approach, our model achieves an F-measure 3% higher than previous methods and reduces the error in the absolute difference between predicted and actual tesserae from 0.20 to just 0.02. The notable performance of the fine-tuned SAM 2 model together with the newly annotated dataset can pave the way for real-time segmentation of mosaic images.

翻译：艺术被广泛视为文明的反映，而马赛克是文化遗产的重要组成部分。马赛克是一种古老的艺术形式，通过使用粘合剂将称为镶嵌块的小片排列在表面上制成。由于其年代久远和脆弱性，马赛克极易受损，这凸显了数字保存的必要性。本文在计算机视觉图像分割的更广泛领域内，通过分割镶嵌块将其与背景分离，解决了马赛克数字化的问题。我们提出了一种利用Meta AI的Segment Anything Model 2（SAM 2）的方法，这是一个优于大多数传统分割模型的基础模型，用于自动分割马赛克。由于该领域公开数据集有限，我们还创建了一个带标注的马赛克图像数据集，用于微调和评估模型。在我们测试数据集上的定量评估显示，与基线SAM 2模型相比有显著改进，交并比从89.00%提高到91.02%，召回率从92.12%提高到95.89%。此外，在先前方法提出的基准测试中，我们的模型实现的F值比先前方法高出3%，并将预测与实际镶嵌块之间的绝对差异误差从0.20降低到仅0.02。微调后的SAM 2模型的显著性能以及新标注的数据集，可以为马赛克图像的实时分割铺平道路。