An Overview of Methods for Automatic Reassembly of Fragmented Objects
Dimitris Arabadjis (National Technical University of Athens, Greece), Michael Exarhos (National Technical University of Athens, Greece), Fotios Giannopoulos (National Technical University of Athens, Greece), Solomon Zannos (National Technical University of Athens, Greece), Panayiotis Rousopoulos (National Technical University of Athens, Greece) and Constantin Papaodysseus (National Technical University of Athens, Greece)
Copyright © 2012.
OnDemand Chapter PDF Download
Download link provided immediately after order completion
List Price: $37.50
Instant access upon order completion.
DOI: 10.4018/978-1-60960-786-9.ch008|Cite Chapter
In this chapter the authors outline some research works characteristic for the application of Signal Processing and Pattern Analysis techniques to the automatic reconstruction / reassembly of fragmented archaeological objects. The studies described in the chapter cover in their application cases a variety of archaeological objects, ranging from documents and wall-paintings to pots and sculptures. Moreover there are distinct approaches in the treatment of these application cases, with some works focusing on the development of a reconstruction methodology of general purpose, while others aim to develop a complete system to treat a specific application problem. The methodologies developed in these studies are outlined in the chapter so as to retain the basic technical elements of each approach that compile the proposed reconstruction algorithmic scheme.
One of the most important parts in the disciplines of Archaeology and History is the extraction of information from excavated finds. These finds that come to light are of huge importance, as they reveal details not only about very important events in human history, but also for the everyday life in Ancient Times, the social structures of cities and civilizations that may not even exist anymore and their social activities. Unfortunately, due to the wear of time and other special conditions (natural disasters or human intervention), most of these finds are, as a rule, unearthed in pieces. For example, in the Greek island of Thera, frescos (wall-paintings) of great archaeological and historical importance have been excavated in thousands of pieces, preventing scholars of getting direct access to a great source of information. Similarly, in the celebrated archaeological sites of Mycenae and Tiryns thousands of wall-paintings’ fragments have been unearthed so far, starting from the excavations of Schliemann. Greece is clearly not the only country in which important archaeological finds are excavated fragmented; on the contrary, very important finds are excavated broken in various locations in Egypt, Italy, Syria, Jordan, Israel, etc.
As a result, a great effort is being undertaken by the scientific community for the reassembly of such fragmented finds. This procedure demands special trained staff, a considerable amount of money and consumes a lot of time. Hence, in the past years, a lot of attempts have been made for the development of various automated systems that could contribute to this effort. The role of these systems is to point out possible matches of fragments to scholars and the dedicated personnel, in order to assist them in the reconstruction of the finds. These systems must propose all possible matches, trying, in the same time, to give as less false positive matches as possible. In the present chapter, a number of publications on the subject are presented.
The basic algorithmic scheme of the reconstruction methods presented here consists of 3 basic sub-processes:
Preprocessing Stage: In the preprocessing of fragments digital representations (photographs or 3D scanning representations) necessary information should be extracted that will be used as input in the subsequent fragments matching process. In this stage the methods usually evaluate the features selected to describe fragments characteristics used to trace possible matching between fragments.
Matching Error Evaluation: Based on the features extracted in the preprocessing, in this stage matching error measures are usually developed so as to obtain matching estimations from fragments features variations.
Matching Decision: After calculating matching estimates between fragments, in this stage the methods evaluate virtual reconstruction of matching fragments islands. Consistency of this reconstruction is depended on the strictness of the matching criteria and the way inconsistent pairwise matching decisions are lifted.