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

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COVID-19 and Pneumonia Detection using X-ray image-Based Deep Learning Methods 2

COVID-19 and Pneumonia Detection using X-ray Image-Based Dee...

引用

2nd International conference on Advanced algorithms and Signal image processing, AASIP 2022

作者： Li, Yuxiao McGill University Montreal Canada

ISBN: (纸本)9781510660571

Globally, pneumonia is the leading cause of death for young people and children. An X-ray of the chest is usually used to diagnose pneumonia by a trained specialist. However, the process is tedious and can result in disagreements among radiologists. It is possible to improve diagnostic accuracy through the use of computer-aided diagnostic systems. In this work, the ResNet model was selected to work as the covid-19 and pneumonia detector based on X-ray image. Several experiments are conducted on to achieve an optimal results. © 2022 SPIE.

关键词： Deep learning

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A Study on Operational Techniques of Preliminary Refinement of images in Camouflaged Enemy Detection 8th

A Study on Operational Techniques of Preliminary Refinement ...

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8th International conference on Information and Communication Technology for Intelligent systems, ICTIS 2024

作者： Suneel, J.S. Dileep, M.R. Department of Master of Computer Applications Visvesvaraya Technological University—Research Centre Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of Master of Computer Applications Nitte Meenakshi Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9789819766833

This study investigates the crucial significance of image processing in the domain of image analysis for the identification and detection of potential threats in advanced defense systems. Real-time images, captured over a wide range of terrains, introduce specific challenges and possibilities for precise target differentiation. The success of subsequent analyses is heavily dependent on the meticulous preprocessing of obtained images, aiming to improve pertinent features and reduce noise interference. It involves a series of operations applied to an image to enhance its quality, correct imperfections, or prepare it for further analysis. Some of the common image preprocessing techniques are Resizing and Rescaling, Normalizing the pixel values, Gray-scale conversion, Cropping, Noise reduction, Contrast enhancement, Edge detection, Thresholding, etc. Also, a wide analysis and survey have been conducted by considering variant parameters that reflect the outcome. By conducting an examination of the literature and carrying out empirical assessments, this study showcases the effectiveness of the proposed preprocessing techniques in enhancing capabilities for detecting adversaries. The outcomes highlight significant enhancements in the precision, robustness, sensitivity, and speed of algorithms designed for enemy detection when employed in conjunction with the specified preprocessing methods. This paper discusses various techniques that influence image analysis and explores the utilization of these techniques across multiple computer vision domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image analysis

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A REVIEW OF PHOTOREALISTIC image STYLIZATION TECHNIQUES 5

A REVIEW OF PHOTOREALISTIC IMAGE STYLIZATION TECHNIQUES

引用

5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Qazi, Hassaan A. Univ Waterloo Dept Appl Math Waterloo ON Canada

ISBN: (纸本)9781665462198

Rendering photorealistic images from the image stylization technique is still considered as a challenging task. In this paper, we compare three recent state-of-the-art approaches. All three algorithms are mainly driven by Convolution Neural Network (CNN) technique. A brief discussion of the selected approaches is followed by some comparisons and results. Both Structural Similarity Index (SSIM) and Learned Perceptual image Patch Similarity (LPIPS) metrics are used to generate new findings of the methodologies. Finally, subjective analysis is also presented to gauge the efficacy of the algorithms in discussion.

关键词： image stylization Photorealistic Artistic framework Deep Neural Network Wavelet transform Perceptual similarity metrics

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Object Detection in Adverse Weather Conditions using Machine Learning

Object Detection in Adverse Weather Conditions using Machine...

引用

2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Kumar, Manish Yadav, Anup Lal Arora, Abhilaksh Deb, Arnab Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350315455

Object detection in challenging weather conditions is a formidable hurdle in the realm of computer vision. Unfavorable weather circumstances, encompassing rain, snow, fog, and low light conditions, substantially impede the effectiveness of conventional computer vision algorithms. This research paper introduces an innovative approach to image-to-image translation by harnessing the power of CycleGAN, a deep learning architecture renowned for its proficiency in transmuting images from one domain to another. This study is dedicated to tailoring the CycleGAN framework to the specific task of enhancing images captured in adverse weather conditions, such as rain, fog, and snow. The proposed methodology capitalizes on the deployment of two distinctive generators, trained cyclically, for the transformation of images between a source domain (representing adverse weather) and a target domain (signifying clear weather). These generators are bolstered by discriminator networks, enabling adversarial training to enhance the quality of image translation. © 2023 IEEE.

关键词： Adversarial Training Autonomous Driving bdd100k Computer Vision CycleGAN Data Augmentation Dataset Deep Learning Generative Adversarial Networks image Degradation image Enhancement image processing image Quality Improvement image-to-image Translation Neural Networks Synthetic Data Weather Conditions

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Video Analysis-based Intelligent Fire Warning System for Heat Exchange Stations 15

Video Analysis-based Intelligent Fire Warning System for Hea...

引用

15th International conference on Signal processing systems, ICSPS 2023

作者： Wang, Wencheng Zhang, Xueyuan Ji, Tao Li, Lun Yin, Baoxin Lan, xiaowei Zhang, Yingqi Li, Dongfeng College of Machinery and Automation Weifang University China College of Engineering Qufu Normal University China School of Astronautics Shandong Jiaotong University China Beijing Zhuoyi Intelligent Technology Co. Ltd China

ISBN: (纸本)9781510675056

In order to solve the problem of fire detection in the heat exchange station, an intelligent fire early warning system based on image analysis is proposed, which realizes the network, digital and intelligent design of video monitoring system. This paper introduces the framework of the platform and the functional structure of each subsystem, gives the overall structure diagram of the system, focuses on the basic algorithm of background modeling, flame segmentation and morphological filtering in the fire identification and analysis module, and puts forward the design scheme and workflow of the alarm and linkage module. Experiments show that the system has low complexity and good real-time performance, which can improve the efficiency of fire emergency command in heat exchange station and meet the actual requirements of heating security. © 2024 SPIE.

关键词： image analysis

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Research on the method of suppressing speckle noise in SAR image 2

Research on the method of suppressing speckle noise in SAR i...

引用

2022 2nd International conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2022

作者： He, Shun Liu, xiangxi Li, Danyang He, xiaoyan xie, Yongni Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9783800760268

Synthetic Aperture Radar (SAR) is a high resolution active imaging sensor. Because of its coherent superposition imaging mechanism, a large number of bright or dark speckle noise is scattered in the SAR image, which seriously affects the texture features and edge contour of the image and increases the difficulty of segmentation algorithm. To solve this problem, this paper proposes a filtering algorithm based on the combination of guided filtering and morphology. Firstly, the appropriate structural elements are selected for morphological processing of SAR ship image slices, and then the processed image is guided filtering to filter out the speckle noise and retain the target edge. The experimental results show that for complex multi-target SAR images, the equivalent number of views and edge preserving index of the image after filtering by the proposed algorithm can reach 59.824495 and 0.537019, respectively, and the speckle noise index is reduced to 0.129289, indicating that the proposed algorithm can more effectively suppress the speckle noise and retain the edge information of the target. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Synthetic aperture radar

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Robust and Faster Zeroth-Order Minimax Optimization: Complexity and Applications 38

Robust and Faster Zeroth-Order Minimax Optimization: Complex...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： An, Weixin Liu, Yuanyuan Shang, Fanhua Liu, Hongying Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China College of Intelligence and Computing Tianjin University China Medical School Tianjin University China Peng Cheng Lab Shenzhen China

Many zeroth-order (ZO) optimization algorithms have been developed to solve nonconvex minimax problems in machine learning and computer vision areas. However, existing ZO minimax algorithms have high complexity and rely on some strict restrictive conditions for ZO estimations. To address these issues, we design a new unified ZO gradient descent extragradient ascent (ZO-GDEGA) algorithm, which reduces the overall complexity to O(dϵ-6) to find an ϵ-stationary point of the function ψ for nonconvex-concave (NC-C) problems, where d is the variable dimension. To the best of our knowledge, ZO-GDEGA is the first ZO algorithm with complexity guarantees to solve stochastic NC-C problems. Moreover, ZO-GDEGA requires weaker conditions on the ZO estimations and achieves more robust theoretical results. As a by-product, ZO-GDEGA has advantages on the condition number for the NC-strongly concave case. Experimentally, ZO-GDEGA can generate more effective poisoning attack data with an average accuracy reduction of 5%. The improved AUC performance also verifies the robustness of gradient estimations. © 2024 Neural information processing systems foundation. All rights reserved.

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A Smart Agriculture Friendly Intelligent Flying Robot Design to Identify Plant Leaf Diseases by using Digital image processing with Learning Approach

A Smart Agriculture Friendly Intelligent Flying Robot Design...

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2023 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Kavitha, M.S. Harikrishnan, R. Shankar, S. Vijay Krishnakumari, S. Sherly, S. Irin R.M.K.Engineering College Department ECE Chennai India S.A. Engineering College Department of ECE Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Loyola ICAM College of Engineering and Technology Department of ECE Chennai India Panimalar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350319200

Smart agriculture practices have become crucial in meeting the growing global food demand while minimizing resource use. In this context, the development of intelligent systems and automation plays a pivotal role. This paper introduces a novel solution: a smart agriculture-friendly intelligent flying robot equipped with cutting-edge digital image processing techniques and a learning approach to identify plant leaf diseases. The suggested system merges state-of-the-art robotics and aerial technology with advanced machine learning techniques, specifically the VGG-SVM model. The flying robot is equipped with a high-resolution camera to capture images of plant leaves in real-time, and the VGG-SVM model processes these images for disease detection. VGG offers deep feature extraction capabilities, while SVM provides accurate classification, making the model adept at discerning healthy from diseased leaves. Through digital image processing, the robot captures high-resolution images of plant leaves, which are subsequently subjected to advanced machine learning algorithms. These algorithms analyze and classify the images, providing real-time insights into the health of crops. The combination of precision aerial data collection and machine learning ensures early disease detection and targeted intervention, optimizing crop yield, reducing resource consumption, and ultimately contributing to sustainable agriculture. Proposed model excels with the highest accuracy of 0.97, indicating an overall high rate of correct predictions. © 2023 IEEE.

关键词： Deep Learning Drones Plant Leaf Diseases SVM VGG

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Wavelet-based Compressive Total Variation for image processing 8

Wavelet-based Compressive Total Variation for Image Processi...

引用

8th Annual International conference on Network and Information systems for Computers, ICNISC 2022

作者： Wang, Di Li, Wanshe School of Mathematics and Statistics Shaanxi Normal University Xi'an China

ISBN: (纸本)9781665453516

It is one of the main tasks of image processing to recover the original image from the noise polluted image (observed image). In the wavelet transform domain the approximate coefficient matrix of the original image matrix is low-rank, and the corresponding gradient transform matrix is low-rank and sparse. Compressive total variation has excellent performance in image processing when the original image is sparse and low-rank. Based on this prior knowledge, the denoising of the observed image is realized by minimizing an energy functional containing the fitting term and regularization term, and the image is reconstructed by using the inverse wavelet transform. © 2022 IEEE.

关键词： Wavelet transforms

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ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models 38

ClavaDDPM: Multi-relational Data Synthesis with Cluster-guid...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Pang, Wei Shafieinejad, Masoumeh Liu, Lucy Hazlewood, Stephanie He, xi University of Waterloo Canada Vector Institute Canada Royal Bank of Canada Canada

Recent research in tabular data synthesis has focused on single tables, whereas real-world applications often involve complex data with tens or hundreds of interconnected tables. Previous approaches to synthesizing multi-relational (multi-table)2 data fall short in two key aspects: scalability for larger datasets and capturing long-range dependencies, such as correlations between attributes spread across different tables. Inspired by the success of diffusion models in tabular data modeling, we introduce Cluster Latent Variable guided Denoising Diffusion Probabilistic Models (ClavaDDPM). This novel approach leverages clustering labels as intermediaries to model relationships between tables, specifically focusing on foreign key constraints. ClavaDDPM leverages the robust generation capabilities of diffusion models while incorporating efficient algorithms to propagate the learned latent variables across tables. This enables ClavaDDPM to capture long-range dependencies effectively. Extensive evaluations on multi-table datasets of varying sizes show that ClavaDDPM significantly outperforms existing methods for these long-range dependencies while remaining competitive on utility metrics for single-table data. © 2024 Neural information processing systems foundation. All rights reserved.

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