A method for extracting ancient ceramic patterns based on artificial intelligence

AIP Advances, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 1, 2025

To address missing features and reflections in the extraction process of ancient ceramic patterns, a pattern method combining sharpening-smoothing whale-type k-means algorithm is proposed. By analyzing reflection phenomenon images, image enhancement designed. It effectively improves detail texture expression. In addition, by characteristics graphic ceramics, constructed to achieve accurate extraction. The experimental results show that accuracy this reaches 99.319%. F1 Score, MIoU, Recall are 93.13%, 93.84%, 87.15%, respectively. This demonstrates superior performance robustness Meanwhile, it provides reliable technical support for digital protection cultural heritage academic research.

Language: Английский

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Employing the Concept of Stacking Ensemble Learning to Generate Deep Dream Images Using Multiple CNN Variants

Intelligent Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 200488 - 200488

Published: Jan. 1, 2025

Language: Английский

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A Hybrid Artistic Model Using Deepy-Dream Model and Multiple Convolutional Neural Networks Architectures

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 101443 - 101459

Published: Jan. 1, 2023

The significant increase in drug abuse cases prompts developers to investigate techniques that mimic the hallucinations imagined by addicts and abusers, addition increasing demand for use of decorative images resulting from computer technologies. This research uses Deep Dream Neural Style Transfer technologies solve this problem. Despite significance researches on technology, there are several limitations existing studies, including image quality evaluation metrics. We have successfully addressed these issues improving diversifying types generated images. enhancement allows more effective simulating hallucinated Moreover, high-quality can be saved dataset enlargement, like augmentation process. Our proposed deepy-dream model combines features five convolutional neural network architectures: VGG16, VGG19, Inception v3, Inception-ResNet-v2, Xception. Additionally, we generate implementing each architecture as a separate model. employed autoencoder another method. To evaluate performance our models, utilize normalized cross-correlation structural similarity indexes values obtained those two measures 0.1863 0.0856, respectively, indicating performance. When considering content image, metrics yield 0.8119 0.3097, respectively. Whiefor style corresponding measure 0.0007 0.0073,

Language: Английский

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Generating Various Deep Dream Images Through Maximizing the Loss Function of Particular Layers Using Inception-v3 and Inception-ResNet-V2 Models

Iraqi Journal of Science, Journal Year: 2024, Volume and Issue: unknown, P. 3468 - 3483

Published: June 30, 2024

Recently, Deep Learning (DL) has been used in a new technology known as the Dream (DD) to produce images that resemble dreams. It is utilized mimic hallucinations drug users or people with schizophrenia experience. Additionally, DD sometimes incorporated into decoration. This study produces using two deep-CNN model architectures (Inception-ResNet-V2 and Inception-v3). starts by choosing particular layers each (from both lower upper layers) maximize their activation function, then detect several iterations. In iteration, gradient computed compute loss present resulting images. Finally, total presented, final deep dream image visualized. The output of models different, even for same there are some variations, layers' values Inception-v3 significantly higher comparison levels' values. case Inception-ResNet-V2, convergent.

Language: Английский

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Automate facial paralysis detection using vgg architectures

International Journal of Current Innovations in Advanced Research, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 8

Published: Feb. 9, 2024

Facial Paralysis (FP) is a debilitating condition that affects individuals worldwide by impairing their ability to control facial muscles and resulting in significant physical emotional challenges. Precise prompt identification of FP crucial for appropriate medical intervention treatment. With the advancements deep learning techniques, specifically Convolutional Neural Networks (CNNs), there has been growing interest utilising these models automated detection. This paper investigates effectiveness CNN architectures identify patients with paralysis. The proposed method leveraged depth simplicity Visual Geometry Group (VGG) capture intricate relationships within images accurately classify on YouTube Palsy (YFP) dataset. dataset consists 2000 categorised into non-injured individuals. Data augmentation techniques were used improve robustness generalisation approach proposed. model features extraction module VGG network classification Softmax classifier. performance evaluation metrics include accuracy, recall, precision F1-score. Experimental results demonstrate VGG16 scored an accuracy 88.47% recall 83.55%, 92.15% F1-score 87.64%. VGG19 attained level 81.95%, 72.44%, 88.58% 79.70%. outperformed terms precision, indicate are effective identifying

Language: Английский

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Improving pose estimation accuracy for large hole shaft structure assembly based on super-resolution

Review of Scientific Instruments, Journal Year: 2023, Volume and Issue: 94(6)

Published: June 1, 2023

Image resolution is crucial to visual measurement accuracy, but on the one hand, cost of increasing acquisition device prohibitive, and other image inevitably decreases when photographing objects at a distance, which particularly common in assembly large hole shaft structures for pose measurement. In this study, deep learning-based method super-resolution images proposed, including dataset new learning network structure, designed enhance perception edge information through core structure improve efficiency while improving effect super-resolution. A series experiments have proven that highly accurate efficient can be applied automatic structures.

Language: Английский

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Prediction of physical realizations of the coordinated universal time with gated recurrent unit

Review of Scientific Instruments, Journal Year: 2024, Volume and Issue: 95(1)

Published: Jan. 1, 2024

Coordinated Universal Time (UTC), produced by the Bureau International des Poids et Mesures (BIPM), is official worldwide time reference. Given that there no physical signal associated with UTC, realizations of called UTC(k), are very important for demanding applications such as global navigation satellite systems, communication networks, and national defense security, among others. Therefore, prediction differences UTC-UTC(k) to maintain accuracy stability UTC(k) timescales. In this paper, we report first use a deep learning (DL) technique Gated Recurrent Unit (GRU) predict sequence H futures values ten different published on monthly Circular T document BIPM used training samples. We utilize multiple-input, multiple-output strategy. After process where about 300 past difference used, (H = 6) can be predicted using p (typically values. The model has been tested data from When comparing GRU results other standard DL algorithms, found approximation good performance in predicting According our results, error typically 1 ns. frequency instability timescale main limitation reducing prediction.

Language: Английский

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A Robust Approach for Ulcer Classification/Detection in WCE Images

International Journal of Online and Biomedical Engineering (iJOE), Journal Year: 2024, Volume and Issue: 20(06), P. 86 - 102

Published: April 12, 2024

Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature the patients. It uses remote imaging techniques to explore various segments of gastrointestinal (GI) tract, particularly hard-to-reach small intestine, making it an effective alternative traditional endoscopic techniques. However, physicians face significant challenge when comes analyzing large number images due effort time required. therefore imperative implement aided-diagnostic systems capable automatically detecting suspicious areas subsequent assessment. In this paper, we present novel approach identify tract abnormalities from WCE images, with particular focus on ulcerated areas. Our involves use Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes challenges faced image acquisition, such as variations in illumination contrast, rotation, noise. Using machine learning algorithms, conducted experiments extensive Kvasir-Capsule dataset, subsequently compared our results recent relevant studies. Noteworthy fact that achieved accuracy 97.04% SVM (RBF) classifier 96.77% RF classifier.

Language: Английский

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Deep network fault diagnosis for imbalanced small-sized samples via a coupled adversarial autoencoder based on the Bayesian method

Review of Scientific Instruments, Journal Year: 2024, Volume and Issue: 95(5)

Published: May 1, 2024

Deep network fault diagnosis methods heavily rely on abundant labeled data for effective model training. However, small-sized samples and imbalanced often lead to insufficient features, resulting in accuracy degradation even instability the model. To address this challenge, paper introduces a coupled adversarial autoencoder (CoAAE) based Bayesian method. This aims solve issue of by generating fake integrating them with original ones. Within CoAAE framework, probability density distribution is captured using an encoder are generated random sampling from decoding them. process interaction between classifier obtain prior encoder’s parameters. The parameters updated through decoder’s reconstruction process, leading posterior distribution. Concurrently, decoder trained enhance its ability reconstruct accurately. imbalance samples, parallel employed. shares weights extraction layer encoder, enabling it learn joint fault-related normal samples. evaluate effectiveness proposed augmentation method, experiments were conducted bearing database Case Western Reserve University ResNet18 as deep learning representative. results demonstrate that can effectively augment datasets outperform other advanced methods.

Language: Английский

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The algorithm for extracting surface defects from ZrO2 ceramic bearing balls using shearlet transform image enhancement

AIP Advances, Journal Year: 2024, Volume and Issue: 14(5)

Published: May 1, 2024

To solve the problems of noise coverage defect and low contrast between background ZrO2 ceramic bearing balls, a surface extraction algorithm based on shearlet transform image enhancement for balls is proposed. According to shape characteristics acquisition platform built collect analyze images. Gaussian filtering weakens scatter-particle in image, threshold corrects coefficient generated by transform. After transform, relatively low-frequency high-frequency parts appear. The part reflects edge information defects, texture defects. Thus, integrity ensured, an enhanced obtained. gray histogram observed. optimal selected segmentation method, process defects being completely extracted from realized. Experimental results showed that rates pits, scratches, cracks balls’ images are 95.00%, 92.50%, respectively.

Language: Английский

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