This problem is divided into two subtasks: the classification of archaeological fragments into similar groups and reconstruction each group into the original objects. To solve this problem, a method has been proposed, which exploits the color and texture properties of the surfaces of the fragments. Furthermore, the reconstruction of archaeological fragments in 3D geometry is an important problem in pattern recognition. Therefore, this research has implemented the algorithms to reconstruct real datasets using Neural Networks. The challenge of this work is to reconstruct the objects without previous knowledge about the part that should start the assembly; this greatly helps to avoid the presence of gaps created due to missing artifact fragments. The study utilized the geometric features of the fragments as important features to reconstruct the objects by classifying their fragments using a Neural Network model.
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