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

共 15616 条记录，以下是301-310 订阅

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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...

引用

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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Advancing Brain Tumor Detection: A Cutting-Edge Machine Learning Approach Leveraging CAD systems 8

Advancing Brain Tumor Detection: A Cutting-Edge Machine Lear...

引用

8th IEEE International conference on image and Signal processing and their Applications, ISPA 2024

作者： Guerroudji, Mohamed Amine Amara, Kahina Zenati, Nadia Ameur, Zohra Centre for Development of Advanced Technologies Algeria University of Mouloud Mammeri UMMTO Tizi-Ouzou Algeria

ISBN: (纸本)9798350309249

In recent decades, medical imaging has emerged as a vital field in medicine, playing a crucial role in diagnosis. Computer Assisted Diagnosis (CAD) systems have become instrumental in this arena, employing sophisticated algorithms to extract crucial information from medical images. This study presents an innovative brain cancer detection system utilizing statistical classification methods. The approach involves three key stages: firstly, the identification of regions of interest through Gradient Vector Flow (GVF) Snake models and mathematical morphology techniques;secondly, the characterization of these regions using morphological and textural parameters;and finally, employing this characterization as inputs for a Bayesian network to classify malignant and benign cancer cases. Experimental validation of the proposed approach yielded impressive results, including a 100% sensitivity rate and a classification accuracy exceeding 98% for tumor segmentation. These findings underscore the high efficacy of the proposed CAD system, showcasing its potential in enhancing cancer diagnosis and patient care. © 2024 IEEE.

关键词： Bayesian networks

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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 ...

引用

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

Performance Study of SIFT and Swin Transformer Algorithms ...

引用

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

引用

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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Review of Artificial Intelligence methods for detecting cancer in medical image processing 3

Review of Artificial Intelligence methods for detecting canc...

引用

3rd International conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sarkar, Priya Saha, Ashim National Institute of Technology Agaratala Computer Science and Engineering Agartala India

ISBN: (纸本)9781665462167

Cancer is the unchecked spread of aberrant cells throughout the body. Cancer is a general word for a set of diseases brought on by the growth of abnormal cells in various bodily parts. Lung cancer, breast cancer, skin cancer, oral cancer, colon cancer, and prostate cancer are just a few of the more than a hundred different forms of cancer. Delays in treatment might result in major health problems and even death. This research reviews strategies for detecting liver, brain, and lung cancer using image processing. Automated and computer-aided detection systems (CAD) with artificial intelligence are the approaches utilized for detection, and these systems are good at processing big datasets to produce accurate and effective results in the detection of cancer. To be compatible with AI, these processing systems must overcome numerous obstacles, including picture capture, pre-processing, data management, segmentation, and classification algorithms. The numerous picture acquisition and segmentation approaches are reviewed in this work. These methods are now essential to meet the needs of the expanding patient population and to improve the healthcare system. © 2023 IEEE.

关键词： Diseases

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Learning-based image Analytics in User-AI Agent Interactions for Cyber-enabled Manufacturing 4

Learning-based Image Analytics in User-AI Agent Interactions...

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4th IEEE International conference on Human-Machine systems, ICHMS 2024

作者： Alarcon, Mauro Lemus Roy, Upasana Kambhampati, Anirudh Nguyen, Nguyen Attari, Minasadat Surya, Ramakrishna Bunyak, Filiz Maschmann, Matthew Palaniappan, Kannappan Calyam, Prasad University Of Missouri Columbia United States

ISBN: (纸本)9798350315790

Manual trial-and-error methods are employed for image parameter selection decisions in the processes that control cyber-enabled scientific instruments. Particularly in materials manufacturing use cases, where image analytics can be iterative, time-consuming and prone to errors, there is a need to enhance existing processes by using agents featuring learning algorithms that recommend image analytics parameters for users. In this paper, we present an analysis focused on identifying the optimal learning algorithm for an AI agent guiding real-time image processing tasks in carbon nanotube (CNT) manufacturing, which involves enhancing instrument control of cyber-enabled scanning electron microscope (SEM) instrument image scanning setup parameters such as Zoom, Focus, and Contrast. Specifically, we demonstrate the use of Reinforcement Learning (RL) and Imitation Learning (IL) based agents within a Remote Instrumentation Science Environment (RISE), a modularized system capable of utilizing multiple learning algorithms for guiding image analytics tasks. Further, we conduct a comparative performance analysis of RL and IL using 236CNT images captured in SEM material synthesis experiments. The experiments include configuring scanning parameters for CNT images generated by SEM, implementing CNT image segmentation, and assessing the effectiveness of RL and IL agents in identifying scanning parameters to enhance image quality. The objective is to predict Zoom, Focus, and Contrast parameters using limited labeled data for offline training, guiding dynamic adjustments in SEM settings. Our findings reveal that the IL agent outperforms the RL agent under dynamic conditions in characterizing CNT image parameters - specifically, Zoom, Focus, and Contrast - by evaluating image segmentation metrics. © 2024 IEEE.

关键词： Iterative methods

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