Ceramic Surface Image Feature Extracting and Classifying Algorithms Based on Artificial Neural Networks

Yang, Liuqing

doi:10.1109/isdea.2014.66

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…For instance, Mu [7] and others have studied AI-aided recognition of ancient ceramics, consequently, machines instead of experts, can intuitively recognize ancient ceramics. Yang [8] has proposed a feature extraction and classification algorithm for ceramic surface images based on artificial neural networks and extracted shape features for separated surface defects.…”

Section: Introductionmentioning

confidence: 99%

A Recognition Method of Ceramic Microcosmic Images Based on SURF and Blockchain

Wang¹,

Wang²,

Xu³

2023

Journal of Computers

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<p>Ceramics have gradually occupied a more significant proportion in the art market and daily life in recent years. Therefore, the identification and anti-counterfeiting of ceramics have become more important with the continuous improvement of counterfeit ceramics. However, it is difficult for traditional ceramic identification and anti-counterfeiting technology to make instant, accurate and efficient identifications. Hence, based on the speed-ed up robust feature (SURF) algorithm, this paper proposes to take the microscopic surface features of ceramic images as the unique identifier for ceramic. In addition, blockchain was combined with distributed storage to ensure the security and reliability of these micro-characteristic data. At any time, ceramic images to be identified can be compared and verified with these images stored on the blockchain, and hence to determine the authenticity of the ceramics. Experimental results show that the proposed method has a high recognition rate and good robustness to problems. Compared with the traditional feature extraction methods, the efficiency and accuracy of proposed algorithm have been improved. The matching similarity rate between most imitations and genuine products using the proposed algorithm will not exceed 15%, thus accurately identifying imitations to achieve the anti-counterfeiting of ceramics.</p> <p> </p>

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Section: Introductionmentioning

confidence: 99%

A Recognition Method of Ceramic Microcosmic Images Based on SURF and Blockchain

Wang¹,

Wang²,

Xu³

2023

Journal of Computers

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“…For example, Mu et al (Mu, Wang, et al, 2019) has studied the artificial intelligence-assisted recognition of ancient ceramics in which three main ancient visual features of ceramics were converted into machine vision features: shape as object outline, decoration as image texture, and inscriptions as handwritten Chinese characters. Yang (Yang, 2014) has proposed an algorithm for feature extraction and classification of ceramic surface images based on artificial neural networks for extracting the shape feature of surface defects.…”

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confidence: 99%

The Recognition of Microscopic Images of Ceramics Incorporating Blockchain Technology

Qiu

Cheng

2022

International Journal of Cognitive Informatics and Natural Intelligence

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Having summarized the previous research on ceramic identification and the anti-counterfeiting, the authors propose a ceramic identification system that combines computer vision algorithms with blockchain technology. The system uses irregular pores on microscopic images of ceramic surfaces as image features, and it applies the SIFT(Scale-invariant feature transform) algorithm to extract feature. The images and feature vector sets are then stored by IPFS(Inter-planetary File System). When a consumer needs to authenticate a ceramic product, it is only necessary to take a microscopic image of the specified location, and then the SIFT algorithm will compare the picture with the data stored in the IPFS network, and was previously obtained through the records on a blockchain network, the matching result then determines whether the photographed ceramic is one of those already recorded. Experimental show that the matching results can be used as a strong basis for identifying the origin of ceramic products.

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