A novel method for estimating the complete 3D shape of pottery with axial symmetry from single potsherds based on principal component analysis

Zvietcovich, Fernando; Navarro, Luis A.; Saldana, Julio; Castillo, Luis Jaime; Castaneda, Benjamín

doi:10.1016/j.daach.2016.05.001

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…In archaeology, for pottery from early times, most of the pottery cannot be preserved intact to this day due to weather and natural environment, and more of the pottery is in the form of shards in our is research. Considering the featured study of pottery shards, literature [ 8 – 11 ] proposed different local feature extraction methods, mainly around the shape and contour of pottery shards, and feature mapping with the data set to obtain pottery sources. Researchers in the literature [ 12 – 17 ] also established a separate pottery color database and material features database to compensate for the lack of color features and material features in the previous data set.…”

Section: Related Workmentioning

confidence: 99%

Mask R-CNN-Oriented Pottery Display and Identification System

Wei

2022

Computational Intelligence and Neuroscience

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Traditional pottery identification methods are time consuming and costly. In order to cater to more pottery industry needs, we propose a Mask R-CNN-based pottery identification method to build an automatic pottery identification system. We first improve the loss function of Mask R-CNN by using generalized intersection over union loss function, through the pattern of migration learning, to compensate for the disadvantage of a small collective amount of pottery data. For different types of pottery, we use the mask algorithm to enhance the features of the outer contour of the pottery. In addition, we use the minimum external matrix algorithm to accurately extract the outer contour bit pose features of pottery to improve the model’s accuracy in recognizing the outer contour of pottery. To meet the testing conditions of pottery, with the support of potters and archaeologists, we make our pottery data set according to pottery categories. The experimental results prove that our method performs best in the comprehensive recognition accuracy of pottery, with the recognition accuracy above 90%. The recognition accuracy is also the best in pottery color decoration and grain decoration, and the grain recognition accuracy stays above 87%, which is better than other pottery recognition methods.

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Section: Related Workmentioning

confidence: 99%

Mask R-CNN-Oriented Pottery Display and Identification System

Wei

2022

Computational Intelligence and Neuroscience

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“…These descriptors (such as radius, tangent and curvature), although easy to calculate, do not show a clear correspondence with the elements of knowledge generally used by archaeologists to analyze and classify archaeological finds. In direct matching methods, finally, classification is based on a direct comparison of the longitudinal profile, extracted from the 3D model, with those of a reference database [ 16 , 17 , 18 , 19 , 20 ].…”

Section: Archaeological Feature Recognitionmentioning

confidence: 99%

A 3D Informational Database for Automatic Archiving of Archaeological Pottery Finds

Angelo

Stefano

Guardiani

et al. 2021

Sensors

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From archaeological excavations, huge quantities of material are recovered, usually in the form of fragments. Their correct interpretation and classification are laborious and time-consuming and requires measurement, analysis and comparison of several items. Basing these activities on quantitative methods that process 3D digital data from experimental measurements allows optimizing the entire restoration process, making it faster, more accurate and cheaper. The 3D point clouds, captured by the scanning process, are raw data that must be properly processed to be used in automatic systems for the analysis of archeological finds. This paper focuses on the integration of a shape feature recognizer, able to support the semantic decomposition of the ancient artifact into archaeological features, with a structured database, able to query the large amount of information extracted. Through the automatic measurement of the dimensional attributes of the various features, it is possible to facilitate the comparative analyses between archaeological artifacts and the inferences of the archaeologist and to reduce the routine work. Here, a dedicated database has been proposed, able to store the information extracted from huge quantities of archaeological material using a specific shape feature recognizer. This information is useful for making comparisons but also to improve the archaeological knowledge. The database has been implemented and used for the identification of pottery fragments and the reconstruction of archaeological vessels. Reconstruction, in particular, often requires the solution of complex problems, especially when it involves types of potsherds that cannot be treated with traditional methods.

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“…Integrating virtual reconstruction of pottery and designs with other techniques like D-stretch on pottery (González et al 2019) and reflectance transformation imaging with basketry (Kotoula et al 2019), archaeologists developed techniques to visualize designs that had degraded. Other studies matched 3D models derived from photogrammetry with principal component analysis, allowing the complete estimated reconstruction of ceramic vessels (Zvietcovich et al 2016).…”

Section: Object-based Photogrammetrymentioning

confidence: 99%

“…There is nothing wrong with such papers when they serve as proof-of-concept, but they do not always contribute directly to the accumulation of archaeological knowledge” (2014:2). In another review on digital archaeology, Zubrow offers a similar reflection: “There is a tendency to use digital technological solutions simply because one has the ‘toys’ available” (2016:22).…”

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confidence: 99%

The Digital Revolution to Come: Photogrammetry in Archaeological Practice

Magnani

Douglass²,

Schroder

et al. 2020

Am. Antiq.

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The three-dimensional (3D) revolution promised to transform archaeological practice. Of the technologies that contribute to the proliferation of 3D data, photogrammetry facilitates the rapid and inexpensive digitization of complex subjects in both field and lab settings. It finds additional use as a tool for public outreach, where it engages audiences ranging from source communities to artifact collectors. But what has photogrammetry's function been in advancing archaeological analysis? Drawing on our previous work, we review recent applications to understand the role of photogrammetry for contemporary archaeologists. Although photogrammetry is widely used as a visual aid, its analytical potential remains underdeveloped. Considering various scales of inquiry—graduating from objects to landscapes—we address how the technology fits within and expands existing documentation and data visualization routines, while evaluating the opportunity it presents for addressing archaeological questions and problems in innovative ways. We advance an agenda advocating that archaeologists move from proof-of-concept papers toward greater integration of photogrammetry with research.

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A novel method for estimating the complete 3D shape of pottery with axial symmetry from single potsherds based on principal component analysis

Cited by 7 publications

References 8 publications

Mask R-CNN-Oriented Pottery Display and Identification System

Mask R-CNN-Oriented Pottery Display and Identification System

A 3D Informational Database for Automatic Archiving of Archaeological Pottery Finds

The Digital Revolution to Come: Photogrammetry in Archaeological Practice

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