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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Comprehensive Survey On Remote Sensing image processing Techniques for image Classification 12th

Comprehensive Survey On Remote Sensing Image Processing Tech...

引用

12th EAI International conference on Context-Aware systems and Applications, ICCASA 2023

作者： Tran, Thuy Thi Huynh, Hiep Xuan Faculty of Information Technology – Communication University of CuuLong Vinh Long Viet Nam College of Information and Communication Technology Can Tho University Can Tho Viet Nam

ISBN: (纸本)9783031588778

Remote sensing technology is now being used with unprecedented high resolution, making important contributions in practical applications such as urban development, construction planning and disaster prediction. However, although many scholars have studied algorithms for remote sensing image processing, there have not been detailed summary articles to support new researchers in this field. In this paper, we present an overview of remote sensing images, types of remote sensing satellite images and related studies. Next, we briefly review the recent history of remote sensing image processing techniques. Then, list related studies on remote sensing image classification. Finally, based on the current status of research on remote sensing images, we propose some future research directions in order to provide survey references for new studies in the field of remote sensing. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Urban planning

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Exploring the Efficacy of Spatial Subsampling Methods for 3D image Projection for 2D Classification

Exploring the Efficacy of Spatial Subsampling Methods for 3D...

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2024 IEEE International conference on Imaging systems and Techniques, IST 2024

作者： Wicaksono, Alif Aditya Purnama, I Ketut Eddy Rachmadi, Reza Fuad Muqtadiroh, Feby Artwodini Priyadi, Ardyono Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept. of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept. of Information Systems Surabaya Indonesia

ISBN: (纸本)9798350378214

In this paper we purpose several methods for creating 2D projection image from a 3D scan such as from CT or MR scans, and then look at different types of 2.5D projections with a growing window sizes and see its effects on the efficacy of the machine learning model when combined with the selection algorithms. As such we are using Shi-Tomasi Detectors, Sobel Edge Detectors, Sum of Pixel Intensity, Center Select, and Random Selection as selection algorithms from which we select the most interesting index for a given scan, from which depending on the window size, we stack the neighboring slices in the axial plane to the channel dimension creating a 2.5D image from which we could pass into our ResNet-9 model configured for classification and train the model for a given number of epochs, under the same conditions for all of our tests, this ensures that for all of our experiments the main effector is window size and the selection algorithm. From our tests we found that the Center Select on average outperforming the other selection algorithm, this result indicate that medical information is mostly centered around the scan, and when combined with the saving of 60% less parameters, the purposed method shows a new way of processing 3D Scans for processing with a 2D CNN model. © 2024 IEEE.

关键词： Convolutional neural networks

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Convergent Bregman Plug-and-Play image Restoration for Poisson Inverse Problems 37

Convergent Bregman Plug-and-Play Image Restoration for Poiss...

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37th conference on Neural Information processing systems (NeurIPS)

作者： Hurault, Samuel Kamilov, Ulugbek Leclaire, Arthur Papadakis, Nicolas Univ Bordeaux CNRS INRIA Bordeaux INPIMBUMR 5251 Bordeaux France Washington Univ St Louis MO 63110 USA Univ Bordeaux CNRS INRIA Bordeaux INPIMBUMR 5251LTCITelecom ParisIP P Bordeaux France

ISBN: (纸本)9781713899921

Plug-and-Play (PnP) methods are efficient iterative algorithms for solving ill-posed image inverse problems. PnP methods are obtained by using deep Gaussian denoisers instead of the proximal operator or the gradient-descent step within proximal algorithms. Current PnP schemes rely on data-fidelity terms that have either Lipschitz gradients or closed-form proximal operators, which is not applicable to Poisson inverse problems. Based on the observation that the Gaussian noise is not the adequate noise model in this setting, we propose to generalize PnP using the Bregman Proximal Gradient (BPG) method. BPG replaces the Euclidean distance with a Bregman divergence that can better capture the smoothness properties of the problem. We introduce the Bregman Score Denoiser specifically parametrized and trained for the new Bregman geometry and prove that it corresponds to the proximal operator of a nonconvex potential. We propose two PnP algorithms based on the Bregman Score Denoiser for solving Poisson inverse problems. Extending the convergence results of BPG in the nonconvex settings, we show that the proposed methods converge, targeting stationary points of an explicit global functional. Experimental evaluations conducted on various Poisson inverse problems validate the convergence results and showcase effective restoration performance.

关键词： Inverse problems

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NIS3D: A Completely Annotated Benchmark for Dense 3D Nuclei image Segmentation 37

NIS3D: A Completely Annotated Benchmark for Dense 3D Nuclei ...

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37th conference on Neural Information processing systems (NeurIPS)

作者： Zheng, Wei Peng, James Cheng Hou, Zeyuan Lyu, Boyu Wang, Mengfan Mi, Xuelong Qiao, Shuoxuan Wan, Yinan Yu, Guoqiang Virginia Tech Dept Elect & Comp Engn Blacksburg VA 24061 USA Univ Basel Biozentrum Basel Switzerland

ISBN: (纸本)9781713899921

3D segmentation of nuclei images is a fundamental task for many biological studies. Despite the rapid advances of large-volume 3D imaging acquisition methods and the emergence of sophisticated algorithms to segment the nuclei in recent years, a benchmark with all cells completely annotated is still missing, making it hard to accurately assess and further improve the performance of the algorithms. The existing nuclei segmentation benchmarks either worked on 2D only or annotated a small number of 3D cells, perhaps due to the high cost of 3D annotation for large-scale data. To fulfill the critical need, we constructed NIS3D, a 3D, high cell density, large-volume, and completely annotated Nuclei image Segmentation benchmark, assisted by our newly designed semi-automatic annotation software. NIS3D provides more than 22,000 cells across multiple most-used species in this area. Each cell is labeled by three independent annotators, so we can measure the variability of each annotation. A confidence score is computed for each cell, allowing more nuanced testing and performance comparison. A comprehensive review on the methods of segmenting 3D dense nuclei was conducted. The benchmark was used to evaluate the performance of several selected state-of-the-art segmentation algorithms. The best of current methods is still far away from human-level accuracy, corroborating the necessity of generating such a benchmark. The testing results also demonstrated the strength and weakness of each method and pointed out the directions of further methodological development. The dataset can be downloaded here https://***/yu-lab-vt/NIS3D.

关键词： Cells

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BachLedger: Orchestrating Parallel Execution with Dynamic Dependency Detection and Seamless Scheduling 30

BachLedger: Orchestrating Parallel Execution with Dynamic De...

引用

30th IEEE International conference on Parallel and Distributed systems, ICPADS 2024

作者： Yang, Yi Shang, Guangyong Qi, Guangpeng Ma, Zhen Liu, Yaxiong Tian, Jiazhou Duan, Aocheng Zhang, Meng Li, Jingying Ding, Xuan Tsinghua University School of Software Beijing China Inspur Yunzhou Industrial Internet Co. Ltd Shandong Jinan China Xidian University School of Cyber Engineering Shaanxi Xi'an China

ISBN: (纸本)9798331515966

Blockchain technology inherently necessitates redundant computation to achieve consensus among untrusted parties because of its fundamental threat model. This requirement, however, compromises system performance and impedes the widespread adoption of blockchain. To leverage existing physical resources, current research on high-performance consortium blockchain algorithms and architectures frequently employs cluster-node architectures to expand the parallel processing capability of traditional single physical nodes. Our investigation reveals a significant trend as the parallel capability of individual nodes improves. The idle time caused by synchronization of all transactions within each block, previously considered negligible, has become increasingly significant. To address this, we present BachLedger, which implements Seamless Scheduling to fully utilize inter-block thread idle time, thereby augmenting system resource utilization and achieving overall performance improvements. Our experimental results demonstrate that our algorithm surpasses current state-of-the-art (SOTA) performance levels in high-performance consortium blockchains and effectively resolves the aforementioned synchronization issue. Furthermore, this scheduling algorithm offers enhanced scalability for BachLedger, positioning it as a promising solution for future blockchain implementations. © 2024 IEEE.

关键词： Scheduling algorithms

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Performance Study of SIFT and Swin Transformer algorithms in Fuzzy Duplicate image Detection Problem 4th

Performance Study of SIFT and Swin Transformer Algorithms ...

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4th International conference on Novel and Intelligent Digital systems, NiDS 2024

作者： Bystryakov, Daniil V. Levshin, Denis V. Zubkov, Alexander V. Donsckaia, Anastasia R. Software Engineering Department Volgograd State Technical University Volgograd Region Russia

ISBN: (纸本)9783031733437

The quantity of data in any field of activity is increasing at an exponential rate on an annual basis. images are no exception to this rule. In such circumstances, the importance of image analysis and processing is becoming increasingly evident. One such task is the detection of fuzzy duplicates of images, which is of significance for search engines and databases, as well as for fields such as medicine, security and law enforcement, advertising and marketing, and so on. Despite the existing variety of fuzzy duplicate search algorithms and models, there is no unambiguous answer as to which approach will be the most effective for a particular problem. This study examines two approaches to fuzzy duplicate image retrieval: one based on SIFT descriptors and the other on Swin Transformer. The current work presents the experimental results of the two models. The study compares the two models in terms of recall, specificity, PPV (positive predictive value), NPV (negative predictive value) and accuracy. The Swin Transformer model demonstrated superior performance across all evaluated metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Marketing

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FashionR2R: Texture-preserving Rendered-to-Real image Translation with Diffusion Models 38

FashionR2R: Texture-preserving Rendered-to-Real Image Transl...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Hu, Rui He, Qian He, Gaofeng Zhuang, Jiedong Chen, Huang Liu, Huafeng Wang, Huamin Zhejiang University China Style3D Research China

Modeling and producing lifelike clothed human images has attracted researchers' attention from different areas for decades, with the complexity from highly articulated and structured content. Rendering algorithms decompose and simulate the imaging process of a camera, while are limited by the accuracy of modeled variables and the efficiency of computation. Generative models can produce impressively vivid human images, however still lacking in controllability and editability. This paper studies photorealism enhancement of rendered images, leveraging generative power from diffusion models on the controlled basis of rendering. We introduce a novel framework to translate rendered images into their realistic counterparts, which consists of two stages: Domain Knowledge Injection (DKI) and Realistic image Generation (RIG). In DKI, we adopt positive (real) domain finetuning and negative (rendered) domain embedding to inject knowledge into a pretrained Text-to-image (T2I) diffusion model. In RIG, we generate the realistic image corresponding to the input rendered image, with a Texture-preserving Attention Control (TAC) to preserve fine-grained clothing textures, exploiting the decoupled features encoded in the UNet structure. Additionally, we introduce SynFashion dataset, featuring high-quality digital clothing images with diverse textures. Extensive experimental results demonstrate the superiority and effectiveness of our method in rendered-to-real image translation. © 2024 Neural information processing systems foundation. All rights reserved.

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image Search Engine with Recognition 4

Image Search Engine with Recognition

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kumar, Jmsv Ravi Dheeraj, Bellaganti Gowtham, Geddada Babu, Dokala Vivek Kiran, Bonthu Asha Srikanth, M. Srkr Engineering College Department of Information Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798331523923

The image Recognition Search Engine is one of the modern advances in artificial intelligence and image analysis and processing. This advanced system surpasses typical functionalities in image identification as it captures features to solve new problems faced in existing systems of digital media authentication, content designing, and improvement of images. The system core relies on advanced AI algorithms that can rasterically recognize and analyze various forms of image objects ranging from objects, faces, scenes, and brand logos converting what appears to be simple images into indexical, analyzable data. One feature is that the system can integrate deepfake detection technology, which is relevant in the context of increasing the amount of manipulated content in the media space. Using special algorithms to learn even the least detectable abnormality in images including the presence of unnatural distinctive facial characteristics and changes in lighting conditions, we obtain a highly effective tool for improving the reliability of image identification. Furthermore, the platform allows for new features to convert an image into a video by implementing frame interpolation and motion estimation through animating the images to allow users to create interesting clips for applications regarding marketing or any personal use. © 2025 IEEE.

关键词： Motion estimation

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A Novel Approach to image Restoration and image Enhancement 3rd

A Novel Approach to Image Restoration and Image Enhancement

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3rd International conference on Computing and Communication Networks, ICCCN 2023

作者： Singh, Divya Upadhayay, Bhawna Gupta, Pradeep Gupta, Sonam Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819708918

image restoration and image enhancement are fundamental tasks in computer vision and image processing. image restoration aims to recover the original information from a degraded or corrupted image, while image enhancement aims to improve the visual quality and interpretability of an image. This research paper presents a comprehensive theoretical framework that integrates both image restoration and image enhancement techniques. We explore various methods, algorithms, and mathematical models to address the challenges associated with these tasks. The proposed framework leverages both classical and deep learning-based approaches to achieve superior performance in restoring and enhancing images. The experimental results demonstrate the effectiveness and versatility of our proposed approach in a wide range of applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image reconstruction

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ICCAI 2024 - Proceedings of the 2024 10th International conference on Computing and Artificial Intelligence

ICCAI 2024 - Proceedings of the 2024 10th International Conf...

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10th International conference on Computing and Artificial Intelligence, ICCAI 2024

ISBN: (纸本)9798400717055

The proceedings contain 72 papers. The topics discussed include: classification of degradation level of pineapple leaf spot disease using MobileNetV3;multi-processing depth map estimation for single-view 3D image generation;artistic outpainting through adaptive image-to-text and text-to-image generation;visual presentation learning with contrastive learning for radiology report generation;data-driven methodology for bike route identification to enhance urban cycling infrastructure in metropolitan manila;person re-identification from video surveillance systems using artificial intelligence methods;assessing the security posture of mobile automated fingerprint identification systems (MABIS): a comprehensive analysis for enhanced resilience;and facemasks detection with different orientation using deep learning algorithms.

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