The reconstruction of archaeological objects is a very challenging problem and solving this problem is important. Occasionally, archaeological workers suffer when trying to match object fragments together, especially when there is a presence of significant gaps in the fragments, or even in the case of similarity, where fragments are mixed with fragments of other objects. The main theme of this study is a proposed method for the reconstruction of pottery from archaeological fragmented pots and vases, depending on the use of a polynomial function. In any case, there is an important fact that should be mentioned: The assembly of any object will rely on the edges of the fragment firstly, then the color and texture. Therefore, this study has adopted the edges of the fragments as a condition when reconstructing the objects, by exploiting the edges of the fragments as an important feature, mainly due to the fact that edges of the fragments are lines, corners and curves. A Canny filter was used to identify the edges of the fragments. In addition, for the purpose of obtaining the vector of coefficient for the set of edges, a polynomial function algorithm was applied. Lastly, the experiments shows that the algorithm is effective, especially when applying the correlation coefficient formula in the classification phase by using the data set which consists of 56 pieces and each one has edges at a rate of 3-5 cm. The experimental results achieved a high success rate that means the proposed system may produce high performance to recognize and match the edges by using a polynomial function to extract features and to classify them by using a correlation coefficient.
Feature selection, the process of representing an object in the least dimensions, is one of the most important and difficult steps in pattern recognition. Therefore, meticulous selection of important features for classification is required. In this study, we propose a method based on Multidimensional Scaling (MDS) to reduce the dimensions of ancient ceramic fragment features. This method focuses on selecting the most important features based on the density of the grayscale image and texture. Finally, we use the Euclidean distance equation to classify objects into similar groups. With a database containing more than 300 images, the experiment achieved an impressive 90% success rate in accurately categorizing fragments as either similar or non-similar. These results demonstrate the effectiveness and promise of the proposed approach for image classification tasks, emphasizing the potential of statistical methods and image processing techniques for addressing complex computer vision challenges.
The reconstruction of archaeological fragments in 3D geometry is an important problem in pattern recognition and computer vision. Therefore, we implement an algorithm with the help of a 3D model to perform reconstruction from the real datasets using the slope features. This approach avoids the problem of gaps created through the loss of parts of the artifacts. Therefore, the aim of this study is to assemble the object without previous knowledge about the form of the original object. We utilize the edges of the fragments as an important feature in reconstructing the objects and apply multiple procedures to extract the 3D edge points. In order to assign the positions of the unknown parts that are supposed to match, the contour must be divided into four parts. Furthermore, to classify the fragments under reconstruction, we apply a backpropagation neural network. We test the algorithm on several models of ceramic fragments. It achieves highly accurate results in reconstructing the objects into their original forms, in spite of absent pieces.
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