A Novel Automatic Image Stitching Algorithm for Ceramic Microscopic Images

Li, Kaiyue; Ding, Guofu

doi:10.1109/icalip.2018.8455766

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…In recent years, with the increasing popularity of deep learning, more and more deep learning models have been applied to affine shape estimation, orientation assignment and feature description in the process, such as LIFT (Learned invariant feature transform) (Yi, Trulls, et al, 2016), Quad-Networks (Savinov, Seki, et al, 2017). Generally, these models outperform traditional handcrafted algorithms in some aspects and can be said to be considerably potential, but the handcrafted methods are not yet obsolete, such as SIFT(Scale Invariant Feature Transform) (Lowe, 2004), ORB (Oriented FAST and Rotated BRIEF) (Rublee, Rabaud, et al, 2011) and other algorithms and their improvements still play an important role in various fields such as Gupta et al (Gupta, Thakur, et al, 2020), Zhou et al (Zhou, Wu,et al, 2020), Yang et al (Yang, Huang,et al, 2021), including the application in ceramic microscopic images (Li, Ding, 2018).…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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Using microscopic imaging and ensemble deep learning to classify the provenance of archaeological ceramics

Wang,

Xiao,

Liu

2024

Sci Rep

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Considering the substantial inaccuracies inherent in the traditional manual identification of ceramic categories and the issues associated with analyzing ceramics based on chemical or spectral features, which may lead to the destruction of ceramics, this paper introduces a novel provenance classification of archaeological ceramics which relies on microscopic features and an ensemble deep learning model, overcoming the time consuming and require costly equipment limitations of current standard methods, and without compromising the structural integrity and artistic value of ceramics. The proposed model includes the following: the construction of a dataset for ancient ceramic microscopic images, image preprocessing methods based on Gamma correction and CLAHE equalization algorithms, extraction of image features based on three deep learning architectures—VGG-16, Inception-v3 and GoogLeNet, and optimal fusion. This latter is based on stochastic gradient descent (SGD) algorithm, which allows optimal fitting of the fusion model parameters by freezing and unfreezing model layers. The experiments employ accuracy, precision, recall and F1 score criteria to offer a comprehensive of the classification outcomes. Under 5-fold cross-validation and independent testing, the proposed fusion-based model performed excellently after comparing above three typical deep learning model. The predictive results of the ensemble deep learning are very stable at about 0.9601, 0.9615, 0.9607 and 0.9583 in precision, recall, F1-score, and accuracy on the independent testing dataset, respectively. This indicates that our model is robust and reliable. Furthermore, we use correspondence analysis to explore the distribution of the ceramic microscopic images from different kilns. This method can be applied in the field of ceramic cultural relic identification, contributing to improved diagnostic accuracy and efficiency, and providing new ideas and methods for related research areas.

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Automatic Satellite Image Stitching Based on Speeded Up Robust Feature

Megha

Rajkumar

2021

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)

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A Novel Automatic Image Stitching Algorithm for Ceramic Microscopic Images

Cited by 5 publications

References 13 publications

The Recognition of Microscopic Images of Ceramics Incorporating Blockchain Technology

The Recognition of Microscopic Images of Ceramics Incorporating Blockchain Technology

Using microscopic imaging and ensemble deep learning to classify the provenance of archaeological ceramics

Automatic Satellite Image Stitching Based on Speeded Up Robust Feature

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