Using Image Feature Extraction to Identification of Ancient Ceramics Based on Partial Differential Equation

Niu, Chuanbao; Zhang, Mingzhu

doi:10.1155/2022/3276776

Cited by 10 publications

(14 citation statements)

References 20 publications

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“…In fact, these principal components are the result of linear combination of original feature vectors. Therefore, after the principal components of bend features are calculated, we may carry out feature selection according to visualization results of new feature vectors [18,19] . Here, we choose the first and the second principal components with cumulative contribution of 97.7% for analysis.…”

Section: Pca-based Feature Selectionmentioning

confidence: 99%

PCA-based bend feature selection applied in defect detection of ice cream stick

Zhao

Bai

Gao

et al. 2023

International Conference on Optical and Photonic Engineering (icOPEN 2022)

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The ice cream stick is a kind of wood product with a plane surface. For the qualified stick it has the nearly flat surface. However, due to slender and thin shape the bend defect is prone to occur during machining of ice cream stick. At present, manual technique is often used to identify the bend defect in the industrial fields, which holds many problems such as low efficiency, low automation and unreliable detection result. In the paper, the PCA-based bend feature selection method is proposed which is applied in defect detection of ice cream stick. At first, the 3D data of object surface is obtained based on the three-dimensional (3D) measurement principle of line structured light. Then four kinds of bend features are designed according to the shape characteristics of the light stripe, which include variation coefficient, correlation coefficient, determination coefficient and straightness metric. At last, the bend feature selection is operated based on the PCA-based method. The research results provide valuable reference for the engineering application in the intelligent defect detection.

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Section: Pca-based Feature Selectionmentioning

confidence: 99%

PCA-based bend feature selection applied in defect detection of ice cream stick

Zhao

Bai

Gao

et al. 2023

International Conference on Optical and Photonic Engineering (icOPEN 2022)

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“…Therefore, the background features are relatively complex, so the image feature extraction method with generalized features must be selected. The feature extraction of images can play a very important role in the protection of cultural relics, the development of archaeological excavation, the protection and restoration of cultural relics, the construction of smart museums, the online service of cultural relics, and the extensive use of convolutional neural networks [13]. The literature introduces the way to extract and identify the characteristics of Yaozhou kiln ceramic ware.…”

Section: Related Workmentioning

confidence: 99%

Technological Innovation in the Recognition Process of Yaozhou Kiln Ware Patterns Based on Image Classification

Liu

2023

Preprint

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The Chinese traditional culture embodies the wisdom and strength of the people of all ethnic groups in China. The superb and exquisite workmanship of Yaozhou kiln ceramics, as well as the decorative patterns on the surface of porcelain and the rich categories of porcelain, fully reflect the great charm of Chinese traditional culture, which also has high historical research value, artistic appreciation value, and collection value. In the process of making Yaozhou Kiln ceramics, craftsmen selected raw materials according to local characteristics, and studied the rich and varied patterns of porcelain in terms of porcelain molding methods, decoration techniques and baking temperatures. In the process of the development of computer vision technology, image classification is the main content to improve the level of technology development, and it is also related to the efficiency of pattern recognition. Yaozhou's porcelain design method not only endows porcelain with material value, but also expresses people's spiritual sustenance by using the patterns on the porcelain surface. Decorations can not only become an important element of porcelain decoration, but also link national culture together and promote cultural transmission. The main principle of image classification is to automatically classify an image according to the visual characteristics, so that the image has a unique classification mark, which is convenient for the system to recognize. This paper uses image classification technology to extract and analyze the characteristics of Yaozhou kiln ceramics, mainly from the shape of porcelain, the type of ornamentation and inscriptions of its three features for image recognition and classification, and discusses the use of advanced technology to replace traditional experts to identify porcelain ornamentation.

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“…As already anticipated, the majority of them (20 out of 22 ones, that is 90.9%) clearly indicated the dataset used. For the other two papers (9.1%), ID5 and ID7, the label "Not provided" is used, primarily focusing on the image processing methods [20,21].…”

Section: Datasets For Ancient Ceramics Classificationmentioning

confidence: 99%

“…The dataset solely included motifs found in the Sukhothai ceramics that had been produced from Sukhothai kilns. ID5 [20] Not provided ID6 [26] This study obtained nine different thin samples of ancient ceramics, from three different sites. They placed each sample under an optical microscope, moved the sample to some interesting position, and rotated the microscope stage, using cross-polarized lighting to acquire a video.…”

Section: Datasets For Ancient Ceramics Classificationmentioning

confidence: 99%

Finding on Machine Learning Approaches for Classification of Ancient Ceramics - A Systematic Literature Review

Ling,

Delnevo,

Mirri

2023

Preprint

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Ceramics appraisal is a hot topic in the field of cultural relic collection, dating back to prehistoric times. Traditionally, there are primarily two types of ceramics appraisal methods, which are experience-based methods and technology-based methods. In practice, both methods would cause high costs and be time-consuming. This paper presents the results of a systematic literature review of 22 empirical studies that used machine and deep learning algorithms to classify and identify ancient ceramics, encompassing data collection processes to build datasets, feature extraction of ancient ceramics images, and the selection of machine learning algorithms. Major findings included that there has been a growing number of research projects on the use of machine and deep learning algorithms for the classification of ancient ceramics.

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Using Image Feature Extraction to Identification of Ancient Ceramics Based on Partial Differential Equation

Cited by 10 publications

References 20 publications

PCA-based bend feature selection applied in defect detection of ice cream stick

PCA-based bend feature selection applied in defect detection of ice cream stick

Technological Innovation in the Recognition Process of Yaozhou Kiln Ware Patterns Based on Image Classification

Finding on Machine Learning Approaches for Classification of Ancient Ceramics - A Systematic Literature Review

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