An Overview of Methods for Automatic Reassembly of Fragmented Objects

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.

Writer Identification in Old Handwritten Music Scores

Writer identification in handwritten text documents is an active area of study, whereas the identification of the writer of graphical documents is still a challenge. The main objective of this work is the identification of the writer in old music scores, as an example of graphic documents. The writer identification framework proposed combines three different writer identification approaches. The first one is based on the use of two symbol recognition methods, robust in front of hand-drawn distortions. The second one generates music lines and extracts information about the slant, width of the writing, connected components, contours and fractals. The third approach generates music texture images and computes textural features. The high identification rates obtained demonstrate the suitability of the proposed ensemble architecture. To the best of our knowledge, this work is the first contribution on writer identification from images containing graphical languages.

A Digital Investigation Manifesting use of Geometric Stencils for the Drawing of Akrotiri Thera Prehistoric Wall Paintings

The present chapter deals with the problem of determining the method used to draw several celebrated and beautiful wall-paintings belonging to the Late Bronze Age (c. 1630 B.C.), that were excavated at Akrotiri, Thera, Hellas (Greece). First, the authors process the wall paintings’ digital images in order to extract the contour of their main thematic entities. Subsequently, a number of fundamental definitions are given and the main hypothesis is stated, namely that geometrical stencils were used for the drawing of the considered wall paintings. A first estimation of the probable one stoke parts of the contour is undertaken, based on curvature considerations and minimization of corresponding error functions. Next, they select families of geometrical curves as potential prototypes of the employed stencils. The selection is based on archaeological and historical criteria. A novel exhaustive curve fitting method is introduced that offers unambiguously optimal matching of two digital curves. Taking into consideration the previous stages, the exact values of the stencils’ parameters are determined. Finally, the hypothesis that stencils were used for the drawing of the considered wall paintings is supported substantially by a visual representation of the one stroke parts together with the corresponding stencil segments that generated them.

Functional Form, Elasticity and Lexical Richness

One may ask whether these theoretical notions have any practical importance. For instance, does the specification of the elasticity produce appropriate indexes of lexical richness estimates? Apparently the answer is yes, as evidenced by the empirical results reported in this study.

Machine Vision Schemes towards Detecting and Estimating The State Of Corrosion

The systematic analysis of corrosion damage on cultural heritage objects is an aspect of multidisciplinary interest. The application of computer-aided approaches in corrosion control has recently become a challenging issue. However, the majority of researches attain to estimate the decay presence by evaluating colour and texture alterations. This work is geared towards investigating non-destructive detection and quantification of stone degradation by using machine vision schemes. The contribution of the current work is 4-fold. Thus, (1) several detection schemes were developed; each handling in a different way the background in-homogeneity (2) Numerous statistical metrics were introduced to quantify corrosion damage. These metrics mainly consider the decay areas size, spatial distribution, shape and darkness. (3) The potential of several monitoring modalities in determining corrosion attributes is studied, and (4) the corroded areas’ shape features are considered in association with the cleaning and structural state that they represent.

Automatic Identification of the Writer of Ancient Greek Inscriptions, Employing Methods of Computer Engineering and Mathematics

Automatic handwriting identification/classification is a major problem in graphology analysis. In this chapter the authors present an automated writer identification system applied to ancient Greek inscriptions. The need of such a system which classifies the hands that carved the inscriptions is important, because it helps scholars to date these inscriptions and deduce proper historical conclusions. The proposed system consists of two different and quasi complementary approaches. The first approach is based on pattern recognition methods in order to help us compute an ideal representative (platonic prototype) of each letter symbol in every inscription. Next, pair wise comparisons, based on statistical criteria, are made and the final decision for the classification of the inscriptions to the corresponding hands, is taken. The second approach also uses statistical criteria to accept/reject statistical hypotheses; nevertheless this methodology employs geometric characteristics of all letters in hand and computes specific values in order to make the decision for the writer identification. Both methods were applied to 33 ancient Athenian inscriptions of the classical era and offered 100% correct classification into 8 different hands. The combination of the application of both approaches and the fact that their results are consistent in themselves and agree with prominent epigraphists’ opinion, show that the system may substantially contribute to ancient inscriptions’ dating, in a robust and reliable manner.

Applications of Computational and Model-Based Statistical Methodologies in Archaeology

Quantitative Archaeology had a rapid development in the past few decades due to the parallel development of methodologies in Physics, Chemistry and Geology that can be implemented in archaeological findings and produce measurements on a number of variables. Those measurements form the data, the basis for a statistical analysis, which in turn can provide us with objective results and answers, within the prediction or estimation framework, about the archaeological findings. Exploratory statistical analysis was almost exclusively used initially for analyzing such data mainly because of their simplicity. The simplicity originates from the fact that exploratory techniques do not rely on any distribution assumption and conduct a non-parametric statistical analysis. However the recent development of the statistical methodology and the computing software allows us to make use of more sophisticated statistical techniques and obtain more informative results. We explore and present applications of three such techniques. The finite mixture approach for model based clustering, the latent class model and the Bayesian mixture of normal distributions with unknown number of components. All three methods can be used for identifying sub-groups in the sample and classify the items.

Automatic Classification of Decorative Patterns in the Minoan Pottery of Kamares Style

An important feature of the Minoan culture is the pottery of Kamares style, that documents the Cretan cultural production between the first half of the 2nd millennium BC. This high level painted production, characterized by the combination of several diverse motifs, presents an enormous decorative repertoire. The extraordinary variety of combinations between elementary motifs according to a complex visual syntax makes interesting the automatic identification of the motifs, particularly upon potsherds. A complete pipeline to accomplish this task is still a challenge to Computer Vision and Pattern Recognition. Starting from a digital image ROI identification, motif extraction, robust contour detection should be performed to obtain a bag of digital shapes. In a second phase each of the extracted shapes has to be classified according to prototypes in a database produced by an expert. The co-occurrence of the different shapes in a specimen will, in turn, be used to help the archaeologists in the cultural and even chronological setting.