Journal of Computer Science

A POLYNOMIAL FUNCTION IN THE AUTOMATIC RECONSTRUCTION OF FRAGMENTED OBJECTS

Nada A. Rasheed and Md Jan Nordin

DOI : 10.3844/jcssp.2014.2339.2348

Journal of Computer Science

Volume 10, Issue 11

Pages 2339-2348

Abstract

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.

Copyright

© 2014 Nada A. Rasheed and Md Jan Nordin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.