Journal of Imaging最新文献_第4页

Research on Digital Orthophoto Production Technology for Indoor Murals in the Context of Climate Change and Environmental Protection. 气候变化与环境保护背景下室内壁画数字正射影制作技术研究。

IF 2.7

Journal of Imaging Pub Date : 2025-04-30 DOI: 10.3390/jimaging11050140

Xiwang Zhou, Yongming Yang, Dingfei Yan

{"title":"Research on Digital Orthophoto Production Technology for Indoor Murals in the Context of Climate Change and Environmental Protection.","authors":"Xiwang Zhou, Yongming Yang, Dingfei Yan","doi":"10.3390/jimaging11050140","DOIUrl":"10.3390/jimaging11050140","url":null,"abstract":"In response to the urgent need for the sustainable conservation of cultural heritage against the backdrop of climate change and environmental degradation, this study proposes a low-cost, non-destructive digital recording method for murals based on close-range photogrammetry. By integrating non-metric digital cameras, total stations, and spatial coordinate transformation models, high-precision digital orthophoto generation for indoor murals was achieved. Experimental results show that the resolution error of this method is 0.02 mm, with root mean square errors (RMSE) of 3.51 mm and 2.77 mm in the X and Y directions, respectively, meeting the precision requirements for cultural heritage conservation. Compared to traditional laser scanning technology, the energy consumption of the equipment in this study is significantly reduced, and the use of chemical reagents is avoided, thereby minimizing the carbon footprint and environmental impact during the recording process. This provides a green technological solution to address climate change. Additionally, the low-cost nature of non-metric cameras offers a feasible option for cultural heritage conservation institutions with limited resources, promoting equity and accessibility in heritage protection amid global climate challenges. This technology provides sustainable data support for long-term monitoring, virtual restoration, and public digital display of murals while also offering rich data resources for virtual cultural tourism, public education, and scientific research. It demonstrates broad application potential in the context of climate change and environmental protection, contributing to the green transformation and sustainable development of cultural tourism.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12112716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements. 儿童图像和身体测量的年龄限制匿名数据集。

IF 2.7

Journal of Imaging Pub Date : 2025-04-30 DOI: 10.3390/jimaging11050142

Hezha H MohammedKhan, Cascha Van Wanrooij, Eric O Postma, Çiçek Güven, Marleen Balvert, Heersh Raof Saeed, Chenar Omer Ali Al Jaf

{"title":"ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements.","authors":"Hezha H MohammedKhan, Cascha Van Wanrooij, Eric O Postma, Çiçek Güven, Marleen Balvert, Heersh Raof Saeed, Chenar Omer Ali Al Jaf","doi":"10.3390/jimaging11050142","DOIUrl":"10.3390/jimaging11050142","url":null,"abstract":"Precisely estimating a child's body measurements and weight from a single image is useful in pediatrics for monitoring growth and detecting early signs of malnutrition. The development of estimation models for this task is hampered by the unavailability of a labeled image dataset to support supervised learning. This paper introduces the \"Age-Restricted Anonymized\" (ARAN) dataset, the first labeled image dataset of children with body measurements approved by an ethics committee under the European General Data Protection Regulation guidelines. The ARAN dataset consists of images of 512 children aged 16 to 98 months, each captured from four different viewpoints, i.e., 2048 images in total. The dataset is anonymized manually on the spot through a face mask and includes each child's height, weight, age, waist circumference, and head circumference measurements. The dataset is a solid foundation for developing prediction models for various tasks related to these measurements; it addresses the gap in computer vision tasks related to body measurements as it is significantly larger than any other comparable dataset of children, along with diverse viewpoints. To create a suitable reference, we trained state-of-the-art deep learning algorithms on the ARAN dataset to predict body measurements from the images. The best results are obtained by a DenseNet121 model achieving competitive estimates for the body measurements, outperforming state-of-the-art results on similar tasks. The ARAN dataset is developed as part of a collaboration to create a mobile app to measure children's growth and detect early signs of malnutrition, contributing to the United Nations Sustainable Development Goals.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12112377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep-Learning Approaches for Cervical Cytology Nuclei Segmentation in Whole Slide Images. 基于深度学习的宫颈细胞学全片核分割方法。

IF 2.7

Journal of Imaging Pub Date : 2025-04-29 DOI: 10.3390/jimaging11050137

Andrés Mosquera-Zamudio, Sandra Cancino, Guillermo Cárdenas-Montoya, Juan D Garcia-Arteaga, Carlos Zambrano-Betancourt, Rafael Parra-Medina

{"title":"Deep-Learning Approaches for Cervical Cytology Nuclei Segmentation in Whole Slide Images.","authors":"Andrés Mosquera-Zamudio, Sandra Cancino, Guillermo Cárdenas-Montoya, Juan D Garcia-Arteaga, Carlos Zambrano-Betancourt, Rafael Parra-Medina","doi":"10.3390/jimaging11050137","DOIUrl":"10.3390/jimaging11050137","url":null,"abstract":"Whole-slide imaging (WSI) in cytopathology poses challenges related to segmentation accuracy, computational efficiency, and image acquisition artifacts. This study aims to evaluate the performance of deep-learning models for instance segmentation in cervical cytology, benchmarking them against state-of-the-art methods on both public and institutional datasets. We tested three architectures-U-Net, vision transformer (ViT), and Detectron2-and evaluated their performance on the ISBI 2014 and CNseg datasets using panoptic quality (PQ), dice similarity coefficient (DSC), and intersection over union (IoU). All models were trained on CNseg and tested on an independent institutional dataset. Data preprocessing involved manual annotation using QuPath, patch extraction guided by GeoJSON files, and exclusion of regions containing less than 60% cytologic material. Our models achieved superior segmentation performance on public datasets, reaching up to 98% PQ. Performance decreased on the institutional dataset, likely due to differences in image acquisition and the presence of blurred nuclei. Nevertheless, the models were able to detect blurred nuclei, highlighting their robustness in suboptimal imaging conditions. In conclusion, the proposed models offer an accurate and efficient solution for instance segmentation in cytology WSI. These results support the development of reliable AI-powered tools for digital cytology, with potential applications in automated screening and diagnostic workflows.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12112741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising. 基于非局部去噪的Swin变压器压缩感知方法。

IF 2.7

Journal of Imaging Pub Date : 2025-04-29 DOI: 10.3390/jimaging11050139

Xiuying Li, Haoze Li, Hongwei Liao, Zhufeng Suo, Xuesong Chen, Jiameng Han

{"title":"SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising.","authors":"Xiuying Li, Haoze Li, Hongwei Liao, Zhufeng Suo, Xuesong Chen, Jiameng Han","doi":"10.3390/jimaging11050139","DOIUrl":"10.3390/jimaging11050139","url":null,"abstract":"In the era of the Internet of Things (IoT), the rapid growth of interconnected devices has intensified the demand for efficient data acquisition and processing techniques. Compressive Sensing (CS) has emerged as a promising approach for simultaneous signal acquisition and dimensionality reduction, particularly in multimedia applications. In response to the challenges presented by traditional CS reconstruction methods, such as boundary artifacts and limited robustness, we propose a novel hierarchical deep learning framework, SwinTCS, for CS-aware image reconstruction. Leveraging the Swin Transformer architecture, SwinTCS integrates a hierarchical feature representation strategy to enhance global contextual modeling while maintaining computational efficiency. Moreover, to better capture local features of images, we introduce an auxiliary convolutional neural network (CNN). Additionally, for suppressing noise and improving reconstruction quality in high-compression scenarios, we incorporate a Non-Local Means Denoising module. The experimental results on multiple public benchmark datasets indicate that SwinTCS surpasses State-of-the-Art (SOTA) methods across various evaluation metrics, thereby confirming its superior performance.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12112192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of a Novel Conditional Noise Predictor for Image Super-Resolution Reconstruction Based on DDPM. 基于DDPM的图像超分辨率重建条件噪声预测器设计。

IF 2.7

Journal of Imaging Pub Date : 2025-04-29 DOI: 10.3390/jimaging11050138

Jiyan Zhang, Hua Sun, Haiyang Fan, Yujie Xiong, Jiaqi Zhang

{"title":"Design of a Novel Conditional Noise Predictor for Image Super-Resolution Reconstruction Based on DDPM.","authors":"Jiyan Zhang, Hua Sun, Haiyang Fan, Yujie Xiong, Jiaqi Zhang","doi":"10.3390/jimaging11050138","DOIUrl":"10.3390/jimaging11050138","url":null,"abstract":"Image super-resolution (SR) reconstruction is a critical task aimed at enhancing low-quality images to obtain high-quality counterparts. Existing denoising diffusion models have demonstrated commendable performance in handling image SR reconstruction tasks; however, they often require thousands-or even more-diffusion sampling steps, significantly prolonging the training duration for the denoising diffusion model. Conversely, reducing the number of diffusion steps may lead to the loss of intricate texture features in the generated images, resulting in overly smooth outputs despite improving the training efficiency. To address these challenges, we introduce a novel diffusion model named RapidDiff. RapidDiff uses a state-of-the-art conditional noise predictor (CNP) to predict the noise distribution at a level that closely resembles the real noise properties, thereby reducing the problem of high-variance noise produced by U-Net decoders during the noise prediction stage. Additionally, RapidDiff enhances the efficiency of image SR reconstruction by focusing on the residuals between high-resolution (HR) and low-resolution (LR) images. Experimental analyses confirm that our proposed RapidDiff model achieves performance that is either superior or comparable to that of the most advanced models that are currently available, as demonstrated on both the ImageNet dataset and the Alsat-2b dataset.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12112433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Minimizing Bleed-Through Effect in Medieval Manuscripts with Machine Learning and Robust Statistics. 用机器学习和鲁棒统计最小化中世纪手稿中的漏损效应。

IF 2.7

Journal of Imaging Pub Date : 2025-04-28 DOI: 10.3390/jimaging11050136

Adriano Ettari, Massimo Brescia, Stefania Conte, Yahya Momtaz, Guido Russo

{"title":"Minimizing Bleed-Through Effect in Medieval Manuscripts with Machine Learning and Robust Statistics.","authors":"Adriano Ettari, Massimo Brescia, Stefania Conte, Yahya Momtaz, Guido Russo","doi":"10.3390/jimaging11050136","DOIUrl":"10.3390/jimaging11050136","url":null,"abstract":"Over the last decades, plenty of ancient manuscripts have been digitized all over the world, and particularly in Europe. The fruition of these huge digital archives is often limited by the bleed-through effect due to the acid nature of the inks used, resulting in very noisy images. Several authors have recently worked on bleed-through removal, using different approaches. With the aim of developing a bleed-through removal tool, capable of batch application on a large number of images, of the order of hundred thousands, we used machine learning and robust statistical methods with four different methods, and applied them to two medieval manuscripts. The methods used are (i) non-local means (NLM); (ii) Gaussian mixture models (GMMs); (iii) biweight estimation; and (iv) Gaussian blur. The application of these methods to the two quoted manuscripts shows that these methods are, in general, quite effective in bleed-through removal, but the selection of the method has to be performed according to the characteristics of the manuscript, e.g., if there is no ink fading and the difference between bleed-through pixels and the foreground text is clear, we can use a stronger model without the risk of losing important information. Conversely, if the distinction between bleed-through and foreground pixels is less pronounced, it is better to use a weaker model to preserve useful details.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12112446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence. 使用弱依赖自定义特征和具有可解释人工智能的机器学习模型的乳房病变检测。

IF 2.7

Journal of Imaging Pub Date : 2025-04-28 DOI: 10.3390/jimaging11050135

Simona Moldovanu, Dan Munteanu, Keka C Biswas, Luminita Moraru

{"title":"Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence.","authors":"Simona Moldovanu, Dan Munteanu, Keka C Biswas, Luminita Moraru","doi":"10.3390/jimaging11050135","DOIUrl":"10.3390/jimaging11050135","url":null,"abstract":"This research proposes a novel strategy for accurate breast lesion classification that combines explainable artificial intelligence (XAI), machine learning (ML) classifiers, and customized weakly dependent features from ultrasound (BU) images. Two new weakly dependent feature classes are proposed to improve the diagnostic accuracy and diversify the training data. These are based on image intensity variations and the area of bounded partitions and provide complementary rather than overlapping information. ML classifiers such as Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting Classifiers (GBC), and LASSO regression were trained with both customized feature classes. To validate the reliability of our study and the results obtained, we conducted a statistical analysis using the McNemar test. Later, an XAI model was combined with ML to tackle the influence of certain features, the constraints of feature selection, and the interpretability capabilities across various ML models. LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) models were used in the XAI process to enhance the transparency and interpretation in clinical decision-making. The results revealed common relevant features for the malignant class, consistently identified by all of the classifiers, and for the benign class. However, we observed variations in the feature importance rankings across the different classifiers. Furthermore, our study demonstrates that the correlation between dependent features does not impact explainability.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12112174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets. 双语手语识别：基于yolov11的孟加拉语和英语字母模型。

IF 2.7

Journal of Imaging Pub Date : 2025-04-27 DOI: 10.3390/jimaging11050134

Nawshin Navin, Fahmid Al Farid, Raiyen Z Rakin, Sadman S Tanzim, Mashrur Rahman, Shakila Rahman, Jia Uddin, Hezerul Abdul Karim

{"title":"Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets.","authors":"Nawshin Navin, Fahmid Al Farid, Raiyen Z Rakin, Sadman S Tanzim, Mashrur Rahman, Shakila Rahman, Jia Uddin, Hezerul Abdul Karim","doi":"10.3390/jimaging11050134","DOIUrl":"10.3390/jimaging11050134","url":null,"abstract":"Communication through sign language effectively helps both hearing- and speaking-impaired individuals connect. However, there are problems with the interlingual communication between Bangla Sign Language (BdSL) and English Sign Language (ASL) due to the absence of a unified system. This study aims to introduce a detection system that incorporates these two sign languages to enhance the flow of communication for those who use these forms of sign language. This study developed and tested a deep learning-based sign-language detection system that can recognize both BdSL and ASL alphabets concurrently in real time. The approach uses a YOLOv11 object detection architecture that has been trained with an open-source dataset on a set of 9556 images containing 64 different letter signs from both languages. Data preprocessing was applied to enhance the performance of the model. Evaluation criteria, including the precision, recall, mAP, and other parameter values were also computed to evaluate the model. The performance analysis of the proposed method shows a precision of 99.12% and average recall rates of 99.63% in 30 epochs. The studies show that the proposed model outperforms the current techniques in sign language recognition (SLR) and can be used in communicating assistive technologies and human-computer interaction systems.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12112066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Review on Document Image Binarization. 文献图像二值化研究综述。

IF 2.7

Journal of Imaging Pub Date : 2025-04-26 DOI: 10.3390/jimaging11050133

Bilal Bataineh, Mohamed Tounsi, Nuha Zamzami, Jehan Janbi, Waleed Abdel Karim Abu-Ain, Tarik AbuAin, Shaima Elnazer

{"title":"A Comprehensive Review on Document Image Binarization.","authors":"Bilal Bataineh, Mohamed Tounsi, Nuha Zamzami, Jehan Janbi, Waleed Abdel Karim Abu-Ain, Tarik AbuAin, Shaima Elnazer","doi":"10.3390/jimaging11050133","DOIUrl":"10.3390/jimaging11050133","url":null,"abstract":"In today's digital age, the conversion of hardcopy documents into digital formats is widespread. This process involves electronically scanning and storing large volumes of documents. These documents come from various sources, including records and reports, camera-captured text and screen snapshots, official documents, newspapers, medical reports, music scores, and more. In the domain of document analysis techniques, an essential step is document image binarization. Its goal is to eliminate unnecessary data from images and preserve only the text. Despite the existence of multiple techniques for binarization, the presence of degradation in document images can hinder their efficacy. The objective of this work is to provide an extensive review and analysis of the document binarization field, emphasizing its importance and addressing the challenges encountered during the image binarization process. Additionally, it provides insights into techniques and methods employed for image binarization. The current paper also introduces benchmark datasets for evaluating binarization accuracy, model training, evaluation metrics, and the effectiveness of recent methods.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12112497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling the Ultimate Meme Recipe: Image Embeddings for Identifying Top Meme Templates from r/Memes. 揭开终极模因配方：从r/模因中识别顶级模因模板的图像嵌入。

IF 2.7

Journal of Imaging Pub Date : 2025-04-23 DOI: 10.3390/jimaging11050132

Jan Sawicki

{"title":"Unveiling the Ultimate Meme Recipe: Image Embeddings for Identifying Top Meme Templates from r/Memes.","authors":"Jan Sawicki","doi":"10.3390/jimaging11050132","DOIUrl":"10.3390/jimaging11050132","url":null,"abstract":"Meme analysis, particularly identifying top meme templates, is crucial for understanding digital culture, communication trends, and the spread of online humor, as memes serve as units of cultural transmission that shape public discourse. Tracking popular templates enables researchers to examine their role in social engagement, ideological framing, and viral dynamics within digital ecosystems. This study explored the viral nature of memes by analyzing a large dataset of over 1.5 million meme submissions from Reddit's r/memes subreddit, spanning from January 2021 to July 2024. The focus was on uncovering the most popular meme templates by applying advanced image processing techniques. Apart from building an overall understanding of the memesphere, the main contribution was a selection of top meme templates providing a recipe for the best meme template for the meme creators (memesters). Using Vision Transformer (ViT) models, visual features of memes were analyzed without the influence of text, and memes were grouped into 1000 clusters that represented distinct templates. By combining image captioning and keyword extraction methods, key characteristics of the templates were identified, highlighting those with the most visual consistency. A deeper examination of the most popular memes revealed that factors like timing, cultural relevance, and references to current events played a significant role in their virality. Although user identity had limited influence on meme success, a closer look at contributors revealed an interesting pattern of a bot account and two prominent users. Ultimately, the study pinpointed the ten most popular meme templates, many of which were based on pop culture, offering insights into what makes a meme likely to go viral in today's digital culture.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12112496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0