Tessella-oriented segmentation and guidelines estimation of ancient mosaic images

Automatic segmentation and analysis of ancient mosaic images can help archeologists and experts build digital collections and automatically compare mosaics by means of image database indexing and content-based retrieval tools. However, ancient mosaics are characterized by low contrast colors, irregular tessella shape, orientation and positioning, making automatic segmentation difficult. In this work we propose a tessella-oriented strategy whose first step consists in isolating tessellas from its cemented network by computing the watershed transformation of a criterion image generated to exhibit the cement network as watershed crests. Then a simple k-means algorithm is used to classify tessellas and segment mosaic images with more accuracy than with a pixeloriented strategy. Additionally, we propose a method to automatically get the main directional guidelines of mosaics by estimating tessella orientation. This is done by minimizing a contextual energy computed from gray-level means of neighboring tessellas and orientation of their borders. Several examples of cartographies show the effectiveness of the method.