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检索条件"任意字段=2023 International Conference on Image Processing and Computer Vision, IPCV 2023"

共 16198 条记录，以下是121-130 订阅

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VISUAL ANALYTICS FOR THE MARRIAGE NETWORK IN THE GORYEO DYNASTY, Korea 17

VISUAL ANALYTICS FOR THE MARRIAGE NETWORK IN THE GORYEO DYNA...

引用

17th international conference on Interfaces and Human computer Interaction 2023, IHCI 2023, the 17th international conference on computer Graphics, Visualization, computer vision and image processing 2023, CGVCVIP 2023 and 16th international conference on Game and Entertainment Technologies 2023, GET 2023

作者： Mun, Seongmin Lee, Hyunju Lee, Sangkuk Department of Humanities Research Institute Ajou University Ajou University 206 World cup-ro Korea Republic of Department of History Ajou University Ajou University 206 World cup-ro Korea Republic of

ISBN: (纸本)9789898704498

Political marriage has been a historically productive and important matter to maintain or extend social power. This kind of relationship is also found in Korean history. For instance, in the history of the Goryeo dynasty (i.e., AD 918~ 1392 in Korea), the royal families of the Goryeo dynasty used marriage as a political strategy to maintain or extend their power. Qualitative studies have found that royal families used marriage as a means of maintaining political power, but there is still a lack of quantitative research supporting the outcomes of these studies. For this reason, we adopt computational approaches to advance our understanding of the role of political marriage in the Goryeo dynasty, in Korea. In addition, to simulate the change of power by their origin (i.e., region), we made a visualization system to explore the marriage network of royal families. We note two major findings of this current study. First, the marriage strategies of royal families are divided into three types: (1) exogamous marriages, (2) endogamous marriages, and (3) the combination of exogamous marriages and endogamous marriages. The royal family of Goryeo expanded their external relationship through exogamous marriages and reproduced their power through intra-family marriages. Second, we found that the power of a region was greater when the king was born through wedlock in that region, as opposed to when many marriages were made with the royal family. As a whole, the success of our computational approaches adds to the quantitative evidence for the historical strategies of political marriage in the Goryeo dynasty, in Korea. © 2023 Proceedings of the international conferences on Interfaces and Human computer Interaction 2023, IHCI 2023;computer Graphics, Visualization, computer vision and image processing 2023, CGVCVIP 2023;and Game and Entertainment Technologies 2023, GET 2023. All rights reserved.

关键词： computer games

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Enhancing computer vision Performance: A Hybrid Deep Learning Approach with CNNs and vision Transformers 8th

Enhancing Computer Vision Performance: A Hybrid Deep Learnin...

引用

8th international conference on computer vision and image processing (CVIP)

作者： Sardar, Abha Singh Ranjan, Vivek Maulana Azad Natl Inst Technol Dept Comp Sci & Engn Bhopal Madhya Pradesh India

ISBN: (纸本)9783031581731;9783031581748

This article explores the growing prominence of deep learning algorithms in computer vision tasks, focusing on the strengths and weaknesses of Convolutional Neural Networks and vision Transformers (ViTs). Convolutional Neural Network (CNNs) have dominated computer vision tasks since their inception due to their ability to identify features irrespective of their location, scale, or orientation. However, their efficiency is limited, particularly in managing long-range dependencies. Conversely, vision Transformers (ViTs), while high performing, are "data-hungry" and require substantial training data to reach their full potential, posing a significant obstacle in areas with limited data availability such as healthcare and plant pathology. To address these limitations, we propose a hybrid approach that integrates the strengths of both CNNs and ViTs, aiming to create a robust model that is efficient with a range of data sizes. Testing on the Plant Disease and Tomato Leaf Disease Classification datasets demonstrates the efficacy of our model, with a marked improvement in F1 score, accuracy, and a significant reduction in loss compared to the base CNN. These findings demonstrate the potential of the suggested method in identifying plant diseases, making a significant contribution to advancements in agricultural technology. This research initiates a crucial discussion on balancing performance and practical data constraints in the fast-evolving field of deep learning.

关键词： Convolutional Neural Networks (CNNs) vision Transformers (ViTs) image classification Plant Disease Limited data

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Research on image retrieval based on artificial intelligence technology 2

Research on image retrieval based on artificial intelligence...

引用

2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Yang, Zhiqiang Liu, Hengzhi Yuan, Yili Hou, Yingying Fu, Bin Wang, Rui Zhang, Li Jiang, Tengguang Shandong Electrical Engineering and Equipment Group Co. Ltd Jinan China

ISBN: (纸本)9798350331417

image retrieval holds significant importance within the realm of computer vision. This paper introduces SCDNet, a novel network model, leveraging selected feature aggregation for enhanced image retrieval. Using Thangka images as an illustrative example, the proposed model aims to address issues such as inadequate semantic feature extraction and image feature distortions due to background or noise. SCDNet firstly uses VGG16 network to extract semantic features from different convolutional layers of Thangka images, then passes through feature screening module, feature aggregation module, and rearrangement module, thereby completing the retrieval. Our experimental validation on homemade Thangka image dataset and publicly available fine-grained image dataset, demonstrating the superiority of SCDNet compared with other retrieval algorithms. © 2023 IEEE.

关键词： component feature aggregation feature extractiont retrieval SCDNett

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MosaiQ: Quantum Generative Adversarial Networks for image Generation on NISQ computers

MosaiQ: Quantum Generative Adversarial Networks for Image Ge...

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IEEE/CVF international conference on computer vision (ICCV)

作者： Silver, Daniel Patel, Tirthak Cutler, William Ranjan, Aditya Gandhi, Harshitta Tiwari, Devesh Northeastern Univ Boston MA 02115 USA Rice Univ Houston TX USA

ISBN: (纸本)9798350307184

Quantum machine learning and vision have come to the fore recently, with hardware advances enabling rapid advancement in the capabilities of quantum machines. Recently, quantum image generation has been explored with many potential advantages over non-quantum techniques;however, previous techniques have suffered from poor quality and robustness. To address these problems, we introduce MosaiQ a high-quality quantum image generation GAN framework that can be executed on today's Near-term Intermediate Scale Quantum (NISQ) computers.

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image Classification of Stroke Blood Clot Origin

Image Classification of Stroke Blood Clot Origin

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2023 international conference on Signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Darapaneni, Narayana Sudha, B.G. Reddy, Anwesh Abdul Karim, A.B. Marothu, Dhanalakshmi Kulkarni, Shirish Menon, Deepak Das Great Learning Bangalore India PES University Bangalore India

ISBN: (纸本)9798350312126

The field of computer vision is constantly expanding and evolving, and it has seen tremendous growth in recent years. computer vision includes image classification as a fundamental component. The critical components for making the best decisions are image categorization and interpretation. This study intends to examine several etiology clots labels, such as Cardiac Embolic and Large Artery Atherosclerosis (CE & LAA), for researchers and practitioners of medical image analysis (particularly of blood clot origin). An analysis of the accuracy and processing speed of various image classification methods using neural network topologies. This report also describes the available medical data set and explains the performance measures of the techniques that are currently accessible. Some of the Deep Learning architectures, including CNN, VGG-16, Efficient-Net, and Res-Net, are studied in the article and discuss the trends with challenges in the application of medical image analysis. © 2023 IEEE.

关键词： Deep learning

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A Comprehensive Survey on image Filtering & Inpainting for Improved image Quality 21

A Comprehensive Survey on Image Filtering & Inpainting for I...

引用

21st OITS international conference on Information Technology, OCIT 2023

作者： Patil, Kalpana Bendre, Varsha Pimpri Chinchwad College of Engineering Pune Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9798350358230

computer vision has significantly impacted information technology over the last few years. The image processing process lays the groundwork for computer vision with important components, including image filtering and image inpainting. Digital image processing is one of the broadest areas for study. It has several applications in military, medicine, space, automobile, security systems and many others. To increase the effectiveness of the system's noise suppression structural similarity index, universal image quality index, texture detection, mean square error, peak signal-to-noise ratio, and many other metrics. The evolutionary examination of several image-filtering methods is included in this paper. Among these methods, the most significant are median filtering, pixel similarity weighted frame averaging, bilateral filtering, anisotropic diffusion, and Gaussian filtering. This study focuses on an evolutionary analysis of image inpainting methods based on parallel pipelines, convolution neural networks and generative adversarial networks. The analysis of numerous works on improving image quality reveals that the hybrid model of image-filtering and image inpainting for image quality improvement is the promising solution for improved quality of image required for computer vision applications. © 2023 IEEE.

关键词： image quality

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Enhancing Adversarial Transferability on vision Transformer by Permutation-Invariant Attacks

Enhancing Adversarial Transferability on Vision Transformer ...

引用

IEEE international conference on Multimedia and Expo (ICME)

作者： Deng, Haoyu Fang, Yanmei Huang, Fangjun Sun Yat Sen Univ Sch Cyber Sci & Technol Shenzhen Campus Shenzhen Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China

ISBN: (纸本)9798350390155;9798350390162

vision Transformers (ViTs) have demonstrated remarkable performance in computer vision. However, they are still susceptible to adversarial examples. In this paper, we propose a novel adversarial attack method tailored for ViTs, by leveraging the inherent permutation-invariant of ViTs to generate highly transferable adversarial examples. Specifically, we split the image into patches of different scales and permute the local patches to generate diverse inputs. By optimizing perturbations on the permuted image set, we can prevent the generated adversarial examples from overfitting to the surrogate model, thereby enhancing transferability. Extensive experiments conducted on imageNet demonstrate that the permutation-invariant (PI) attack significantly improves transferability between ViTs and from ViTs to CNNs. PI is applicable to diverse ViTs and can seamlessly integrate with existing attack methods further enhancing transferability. Our approach surpasses state-of-theart ensemble methods for input transformation and achieves a notable performance improvement of 11.9% on average.

关键词： Black-box attacks vision Transformer transferability diversity input

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Alzheimer's disease classification using hippocampus and improved DenseNet 2

Alzheimer's disease classification using hippocampus and imp...

引用

2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Liu, Bo Liaoning Normal University School of Computer Science and Artificial Intelligence Liaoning Dalian China

ISBN: (纸本)9798350331417

Alzheimer's disease (AD) is an irreversible neurological disease that affects a patient's memory and cognitive abilities. The emergence of deep learning technology accelerates the progress of related research. However, many studies only use whole-brain MRI images in the experiment, and the network structure used for feature extraction is also very simple. This paper proposes a new method for Alzheimer's disease classification: adding MRI images of the hippocampus to the input image, using the pre-trained DenseNet for feature extraction, and adding the attention mechanism (DenseNet-AM). Experiments show that the accuracy of this DenseNet-AM is up to 92.8% in cognitive normal and AD classification, providing more potential to assist doctors in disease diagnosis. © 2023 IEEE.

关键词： Alzheimer's disease Attention mechanism Brain structure MRI image DenseNet

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Robustness of ConvNet to High-Frequency image Corruptions 8th

Robustness of ConvNet to High-Frequency Image Corruptions

引用

8th international conference on computer vision and image processing (CVIP)

作者： Banerjee, Arnab Zoom Video Commun India Pvt Ltd Mumbai Maharashtra India

ISBN: (纸本)9783031581731;9783031581748

image processing techniques such as blurring, JPEG compression are applied to natural images to meet different objectives. Additionally, corruptions such as Gaussian and shot noise appear on images due to digital fluctuations. Unfortunately, standard vision models tend to perform quite poorly under such unavoidable corruptions, i.e., these models are not robust to the distribution shifts induced by these corruptions at test time. The standard approach for overcoming this issue for a known corruption is by augmenting the training data with images perturbed using the corruption of interest. Motivated by settings where the corruption might not be known during training, Gaussian noise is used as an augmentation strategy to gain robustness to high-frequency corruptions. In this paper, we try to understand its properties from a Fourier lens. However, we show that Gaussian augmentation fails to maintain robustness to few high-frequency corruptions at high severity levels. Analyzing the Fourier signature of those corruptions reveal a change in behavior - at high severity they corrupt low frequencies as well. A Gaussian-trained model loses its performance due to this change. Current augmentation strategies for low-frequency corruptions are discussed at the end.

关键词： Fourier transform synthetic corruptions deep learning data augmentation

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Research on Color Quality Classification Algorithms in computer Art Education 4

Research on Color Quality Classification Algorithms in Compu...

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4th international conference on computer vision and Data Mining, ICCVDM 2023

作者： Li, Xiaojing Shanghai Donghai Vocational and Technical College 6001 Hongmei South Road Minhang District Shanghai China

ISBN: (纸本)9781510674448

In the realm of art education, the assessment of color blending and color differences has traditionally been a manual process, often subject to human subjectivity and variations in visual acuity. This paper delves into the application of computer vision techniques, a significant branch of artificial intelligence, to address these challenges. By leveraging the high precision, objectivity, and fast processing speed of computer vision, this study aims to revolutionize color inspection in art education. The research encompasses various stages, including art image acquisition, preprocessing, color difference calculation, and feedback provision. A focal point of the study is the extraction of features from art images and the exploration of classification algorithms for online color difference assessment. Through the application of a data modeling method based on the Morlet wavelet kernel SVM, the research achieves an accuracy rate of 88.67% in the classification of fine art images. While promising, the study also identifies areas for further research, including the quantification of color discrepancies in intricate art images and the enhancement of the classification algorithm's operational speed. © 2024 SPIE.

关键词： computer vision

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