Virtual reconstruction of archaeological vessels using convex hulls of surface markings

Cohen, Fernand S.; Zhang, Zhongchuan; Jeppson, Patrice L.

doi:10.1109/cvprw.2010.5543528

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…The proposed method was applied to two sets of images: one captured by a Nikon camera and the other obtained from the region of Independence National Historical Park in Philadelphia, Pennsylvania, the National Constitution Center was established between 2002 and 2003. During the excavation process, more than one million artifacts were unearthed, dating back to the late 1700s and early 1800s, as stated in [30][31]. Each image had dimensions of 210 × 300 pixels.…”

Section: Image Acquisitionmentioning

confidence: 99%

Selecting the Important Features to Classify the Archaeological Fragments by Using Statistical Tools

Rasheed¹,

Qasim²,

Barla³

2023

INDJCSE

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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.

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Section: Image Acquisitionmentioning

confidence: 99%

Selecting the Important Features to Classify the Archaeological Fragments by Using Statistical Tools

Rasheed¹,

Qasim²,

Barla³

2023

INDJCSE

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“…Most of the recently proposed methods fall into the category of boundary matching, which used the boundaries of the fragment for matching. Cohen et al (2010) suggested a surface-based matching method which is derived from exploited surface markings. Their work includes four steps.…”

Section: Performed Studies From 2010 To 2019mentioning

confidence: 99%

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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“…is standardized being a factory made or largely factory made (early) mass-produced item of the industrial age. Methods researched at Drexel University to automate the process of artifact mending of the NCC site assemblage have pursued different avenues of computational modeling including surface intrinsic properties and surface markings for 3D alignment [9,10] and classification of archaeological fragments using texture and color descriptors [28]. Recent work by McBride et al [21] proposed a curve matching method to automate the assembly of ceramic fragments that belong to the same vessel.…”

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confidence: 99%

A Semantic Analysis Approach to Thin-Shell Ceramic Fragment Classification

Aryafar

Jeppson

2013

International Journal of Heritage in the Digital Era

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In this paper we introduce the Independence National Historical Park dataset, a freely-available collection of thin-shell ceramic fragments, images and metadata for these images. We describe the contents of this dataset and its possible uses in vessel and decorative pattern classification of thin-shell ceramic artifacts. We will employ computer vision algorithms to help automate the ceramic classifications used in archaeological vessel reconstructions. This methodology promises significant time and cost savings for the laboratory mending of ceramic objects, freeing scarce human resources to focus on the analyses of objects needed to produce new history insights. We propose a vector space model of pyramid histogram of visual words (PHOW) that can, in turn, be used by a supervised learning schema for decorative pattern classification. Inspired by explicit semantic analysis of textual documents using term frequency-inverse document frequency (tf-idf), a tf-idf model is proposed to represent thin-shell ceramic artifacts. The effectiveness of this representation is then demonstrated in decorative pattern classification using various machine learning classification algorithms, including support vector machines (SVMs) and k-nearest neighbor clustering.

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Virtual reconstruction of archaeological vessels using convex hulls of surface markings

Cited by 8 publications

References 12 publications

Selecting the Important Features to Classify the Archaeological Fragments by Using Statistical Tools

Selecting the Important Features to Classify the Archaeological Fragments by Using Statistical Tools

Review of computer-based methods for archaeological ceramic sherds reconstruction

A Semantic Analysis Approach to Thin-Shell Ceramic Fragment Classification

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