Classification and reconstruction algorithms for the archaeological fragments

Rasheed, Nada A.; Nordin, Jan

doi:10.1016/j.jksuci.2018.09.019

Cited by 19 publications

(15 citation statements)

References 10 publications

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“…Several experiments are conducted using the dataset obtained from the website (Ceramic Sherd Database, 2010) for evaluating the proposed approach. The results indicate that the proposed approach achieved an accuracy rate of 96.1% (Rasheed & Nordin, 2018). Kalasarinis & Koutsoudis (2019) presented a pipeline to generate the missing parts of the fragmented vessels' main bodies.…”

Section: Performed Studies From 2010 To 2019mentioning

confidence: 91%

“…This knowledge helps to manage the presence of gaps due to losing parts of the artifact. To overcome this problem, Rasheed & Nordin (2018) presented an approach to classify fragments and to reconstruct the original object from them. The classification is done based on color and texture features.…”

Section: Performed Studies From 2010 To 2019mentioning

confidence: 99%

“…Step 2. Feature Extraction An important part of pattern recognition of an object to be classified is the feature extraction, where the feature identifiers (o feature vector) is a list of descriptions that includes sufficient information to identify a pattern (Rasheed & Nordin, 2018). In the literature, a variety of features were defined.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…In the unsupervised ones, there is no previous information about the original objects. So, the classification is conducted based on the similarities between features that describe the fragments to be analyzed and the corresponding ones of pottery databases (Cohen et al, 2016;Maiza & Gaildrat, 2005;Rasheed & Nordin, 2018;Toler-Franklin et al, 2010). Figure 5 shows the usage percentages of the two classification approaches used in the analyzed methods.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…The use of the cost function has also been investigated (Papaioannou et al, 2002;Rasheed & Nordin, 2015). It should be noted that, in recent years, attention in using a machine learning system for matching is increasing (Banterle et al, 2017;Rasheed & Nordin, 2018). The used reconstruction methods in the analyzed literature are shown in Figure 6: the most commonly used approaches are Meta-heuristic optimization and Similarity Analysis.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

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Review of computer-based methods for archaeological ceramic sherds reconstruction

Eslami

Angelo

Stefano

et al. 2020

Virtual archaeol. rev.

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Potteries are the most numerous finds found in archaeological excavations; they are often used to get information about the history, economy, and art of a site. Archaeologists rarely find complete vases but, generally, damaged and in fragments, often mixed with other pottery groups. By using the traditional manual method, the analysis and reconstruction of sherds are performed by a skilled operator. Reviewed papers provided evidence that the traditional method is not reproducible, not repeatable, time-consuming and its results have great uncertainties. To overcome the aforementioned limits, in the last years, researchers have made efforts to develop computer-based methods for archaeological ceramic sherds analysis, aimed at their reconstruction. To contribute to this field of study, in this paper, a comprehensive analysis of the most important available publications until the end of 2019 is presented. This study, focused on pottery fragments only, is performed by collecting papers in English by the Scopus database using the following keywords: “computer methods in archaeology", "3D archaeology", "3D reconstruction", "automatic feature recognition and reconstruction", "restoration of pottery shape relics”. The list is completed by additional references found through the reading of selected papers. The 53 selected papers are divided into three periods of time. According to a detailed review of the performed studies, the key elements of each analyzed method are listed based on data acquisition tools, features extracted, classification processes, and matching techniques. Finally, to overcome the actual gaps some recommendations for future researches are proposed.Highlights:<ul><li>The traditional manual method for reassembling sherds is very time-consuming and costly; it also requires a great deal effort from skilled archaeologists in repetitive and routine activities.</li><li>Computer-based methods for archaeological ceramic sherds reconstruction can help archaeologists in the above-mentioned repetitive and routine activities.</li><li>In this paper, the state-of-the-art computer-based methods for archaeological ceramic sherds reconstruction are reviewed, and some recommendations for future researches are proposed.</li></ul>

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Section: Performed Studies From 2010 To 2019mentioning

confidence: 91%

Section: Performed Studies From 2010 To 2019mentioning

confidence: 99%

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%