The aim of our work is to develop a method for the automated assembly of broken objects with surface texture from their pieces. The task of reassembling has great importance in the fields of anthropology, failure analysis, forensics, art restoration, and reconstructive surgery. It also appears heavily in archaeology. The fact that performing reconstruction of archaeological objects from fragments manually is very time-consuming; this fact motivates automatic techniques for reassembly of fragments. In general, reconstruction of objects can be regarded as a puzzle- solving problem, which contains many problems endemic to pattern recognition, computer vision, feature extraction, boundary matching, and optimization fields. Our proposed approach is based on defining a fast and robust method that finds the best transformation of pieces that maximizes matching and continuity of textures of fragments while satisfying the geometrical constraints. After the acquisition and preprocessing of the data, the first step is the prediction of the pixel values in a band around the border of the pieces. The prediction algorithm automatically fills in this extension region using information in the central part. The main idea in extending the picture/texture on the fragment outwards is that the correlation between the features of the predicted region and its true neighboring piece is significantly higher than alternative pairings. We propose to use FFT Shift Theory to find a solution that will maximize the correlation between the predicted parts of a piece and other pieces.