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

共 16439 条记录，以下是1641-1650 订阅

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A Study on Comparative Analysis of Machine Learning and image processing Techniques for Identifying Plant Leaf Disease 7th

A Study on Comparative Analysis of Machine Learning and Imag...

引用

7th International conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Badami, Vittal Kanakaraddi, Suvarna Kalwad, Priyadarshini K L E Technological University Karnataka Hubballi India

ISBN: (纸本)9789819908370

This research investigates the approaches for identifying and classifying plant leaf diseases from digital images using deep neural networks. While diseases can affect any part of a plant and occasionally go undetected, there are some physical characteristics or symptoms that the plants exhibit that can be quickly detected using vision. These diseases can be identified and classified by machine learning algorithms using a wide range of techniques like deep neural networks, regression analysis, colour analysis and so on but not all techniques are appropriate for identifying all types of plant leaf diseases. It might be difficult to determine which approach is best for particular type of disease detection. This paper mainly focuses on image classification based on deep neural networks, object detection based on thresholding and image transformation and severity quantification with different methods like colour analysis which include Histogram of Oriented Gradients (HOG) and spot analysis and leaf area measurement using open source tool and segmentation-based calculation of affected area. This detailed analysis will be helpful to researchers working on algorithm optimization for image classification, object detection and severity quantification in identifying plant leaf diseases. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Vessel image Enhancement Method Using Nonlinear Transformation Based on Guided Filtering

Vessel Image Enhancement Method Using Nonlinear Transformati...

引用

2021 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2021

作者： Qi, Tang Wen-Tian, Wang Ke-xin, Zhou Yi-Xuan, Sun Xi'An University of Posts Telecommunication Xi'an China

ISBN: (纸本)9781665429184

Aiming at the problems of blurred contrast and difficult diagnosis of vascular tissue in medical images, A nonlinear transform blood vessel enhancement method based on guided filtering is proposed. In this paper, a hybrid filter is obtained by fusing guided filtering and Laplacian filtering to complete the preprocessing of image vessel detail enhancement, then the green component is stretched by nonlinear mapping function in RGB color space, the red component and blue component are adjusted according to the stretching degree. Finally, the saturation component is nonlinear contrast stretched in HSV color space to achieve vascular image enhancement. The paper uses objective evaluation methods such as image DV-BV, information entropy and average gradient to compare the performance of existing image enhancement algorithms. The results show that the proposed method has excellent performance. © 2021 IEEE.

关键词： image enhancement

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Multi-modality Medical image Fusion Employing Various Contrast Enhancement Techniques 2nd

Multi-modality Medical Image Fusion Employing Various Contra...

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Emergent Converging Technologies and Biomedical systems Select Proceedings of the 2nd International conference, ETBS 2022

作者： Jain, Shruti Pal, Bandana Sood, Rittwik Jaypee University of Information Technology Himachal Pradesh Solan Waknaghat India Indira Gandhi Delhi Technical University for Women Delhi India NIT Hamirpur Himachal Pradesh Hamirpur India

ISBN: (纸本)9789819922703

Clinical imaging has a major role in healthcare applications. The blur and the noise of the picture are eliminated, which improves the contrast and provides information about the image. But to increase the precision of the image, it is first improved. The tasks of improving medical pictures are to intensify an area of concern. Because of such issues, image processing techniques like enhancement and fusion algorithms aid human visuals in making unbiased judgments of brain images using CT and MRI. The main aim of this research paper is to exploit the benefits of spatial enhancement methods, namely binarization, median filter, contrast stretching, and contrast limited adaptive histogram equalization (CLAHE). This helps in attaining greater quality images. The enhanced images are followed by the fusion of images using principal component analysis, discrete wavelet transform, and Laplacian re-decomposition (LRU) fusion methodology. The comparative analysis is done based on qualitative and quantitative parameters and found that the results using CLAHE and LRU outperform other techniques. Later, the model is validated on the available datasets and the results are in consensus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Median filters

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Circular Synthetic Aperture Radar for Near-field Terahertz Imaging 5

Circular Synthetic Aperture Radar for Near-field Terahertz I...

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5th International conference on Multimedia, Signal processing and Communication Technologies (IMPACT)

作者： Gezimati, Mavis Singh, Ghanshyam Univ Johannesburg Ctr Smart Informat & Commun Syst Dept Elect & Elect Engn Sci Auckland Pk Kingsway CampusPOB 524 ZA-2006 Johannesburg South Africa

ISBN: (纸本)9781665476478

The potential of synthetic aperture radar (SAR) systems to yield high resolution images has led to its exploration in new applications. The conventional microwave radar imaging suffer limited range resolution in the sub-millimeter range and optical imaging methods are constrained by the diffraction limit. In the Terahertz (THz) spectrum, SAR systems are capable of operating in all weather, all time and penetrating through clouds, smoke, dust etc. to achieve better resolution images beyond the diffraction limit. In this paper, the different aspects of Multimedia computing are investigated in THz circular SAR as a new mode of radar imaging for near-field applications for example indoor environment. We have explored a new toolbox that enables the rapid development of near-field THz imaging SAR systems and generation of large near-field THz imaging scenarios that could be used for data driven applications. A circular SAR imaging system is presented for 2D (two-dimensional) imaging of targets in THz-SAR and the radar images are reconstructed using the Back Propagation and Polar Formatting algorithms. The SAR performance evaluation metrics are reported and scope for future work is discussed.

关键词： Synthetic aperture radar (SAR) THz imaging Circular SAR Near-field Radar image reconstruction High resolution imaging Reconstruction algorithms MIMO FMCW Multimedia

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Proximity-Based Reward systems for Multi-Agent Reinforcement Learning

Proximity-Based Reward Systems for Multi-Agent Reinforcement...

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image processing and Robotics (ICIP), International conference on

作者： Marc-André Blais Moulay A. Akhloufi Dept. of Computer Science Université de Moncton Perception Robotics and Intelligent Machines Lab (PRIME) Moncton NB Canada

ISBN: (数字)9798350374766

ISBN: (纸本)9798350374773

Unmanned vehicles, such as drones, have surged in popularity in recent years. Swarms of these vehicles offer new opportunities in applications such as agriculture, weather monitoring and natural events management. However, efficiently controlling a large swarm of unmanned vehicles poses a significant challenge. Intelligent solutions, particularly reinforcement learning, have been proposed to address this challenge. We introduce a proximity-based reward system for multi-agent reinforcement learning to handle the issue of reward sparsity. Our goal is to develop an approach for controlling a swarm towards a common objective while maintaining robust swarm cohesion. In this paper, we compare various distance-based functions to build a comprehensive reward system. Specifically, we explore the Euclidean, Manhattan, Chebyshev and Minkowski distances in our experiments. We evaluate the impact of these proximity-based reward systems on four reinforcement learning algorithms. We conduct a comparison of our reward systems using various metrics during validation and test episodes. Our goal is to highlight the importance of comparing different algorithms and distance functions in the development of multiagent reinforcement learning systems.

关键词： Measurement image processing Reinforcement learning Chebyshev approximation Control systems Agriculture Task analysis

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HU-Net: Hessian-incorporated U-Net for CBCT projection-domain metal segmentation with guidance from SAM

HU-Net: Hessian-incorporated U-Net for CBCT projection-domai...

引用

IEEE conference on Cybernetics and Intelligent systems

作者： Chen Jiang Tianling Lyu Gege Ma Yan xi Yang Chen Wentao Zhu Zhejiang Lab Hangzhou China Hangzhou Institute for Advanced Study of UCAS Hangzhou China Shanghai First-Imaging Tech. Shanghai China Laboratory of Image Science and Technology Southeast University Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Metal artifact is a prevailing factor reducing the image quality of CBCT in minimally invasive spine surgery. In CBCT, conventional metal artifact reduction algorithms pay more attention to the inpainting of the metal traces, but metal segmentation is also challenging. Despite the current success in image segmentation with deep learning, the substantial expense associated with annotating metal traces in the projection domain makes most of these approaches impractical for this task. To address this, we propose a Hessian-incorporated U-Net (HU-Net) for CBCT projection-domain metal segmentation with guidance from SAM. The proposed method has been tested on both digital phantom data and clinical CBCT data. According to the experimental results, our method has demonstrated notable improvement in the segmentation of metal traces across both datasets. This paper presents a feasible approach for metal trace segmentation in the projection domain with deep learning and has the potential to be applied in clinical applications.

关键词： Deep learning image quality image segmentation Minimally invasive surgery Mechatronics Metals Phantoms

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RAV4D: A Radar-Audia-Visual Dataset for Indoor Multi - Person Tracking

RAV4D: A Radar-Audia-Visual Dataset for Indoor Multi - Perso...

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2024 IEEE Radar conference, RadarConf 2024

作者： Zhou, Yi Song, Ningfei Ma, Jieming Man, Ka Lok López-Benítez, Miguel Yu, Limin Yue, Yutao School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China University of Liverpool Department of Electrical Engineering and Electronics Liverpool United Kingdom Institute of Deep Perception Technology Jitri Wuxi China

ISBN: (纸本)9798350329209

Indoor multi-person tracking is a widely explored area of research. However, publicly available datasets are either oversimplified or provide only visual data. To fill this gap, our paper presents the RAV4D dataset, a novel multimodal dataset that includes data from radar, microphone arrays, and stereo cameras. This dataset is characterised by the provision of 3D positions, Euler angles and Doppler velocities. By integrating these different data types, RAV 4D aims to exploit the synergistic and complementary capabilities of these modalities to improve tracking performance. The development of RAV4D addresses two main challenges: sensor calibration and 3D annotation. A novel calibration target is designed to effectively calibrate the radar, stereo camera and microphone array. In addition, a visually guided annotation framework is proposed to address the challenge of annotating radar data. This framework uses head positions, heading orientation and depth information from stereo cameras and radar to establish accurate ground truth for multimodal tracking trajectories. The dataset is publicly available at https://***/records/l0208199. © 2024 IEEE.

关键词： Stereo image processing

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A New hybrid deep learning coder for Alzheimer classification

A New hybrid deep learning coder for Alzheimer classificatio...

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International conference on image and Signal processing and their Applications (ISPA)

作者： Zoulikha Bebboukha Athmane Zitouni Department of Electrical Engineering Faculty of Science and Technology University of Mohammed Khider Biskra Algeria Vision and Communication Systems Laboratory (VCS) Biskra

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Deep learning algorithms offer distinct advantages over other machine learning methods, enabling the exploitation of advanced techniques to analyze brain MRI scans. In a recent research study, a combination of ResNet-50 and DenseNet-201 convolutional neural network models was utilized to extract crucial features from brain MRI images, which were then fed into a compact classification model. The proposed model demonstrated remarkable results in accurately classifying the stages of Alzheimer’s disease. It outperformed all other approaches using the same dataset and achieved an outstanding accuracy of 99.9 % in categorizing the four cases, affirming the effectiveness and promising potential of this proposed model in the precise diagnosis and classification of Alzheimer’s disease.

关键词： Deep learning Machine learning algorithms Databases Magnetic resonance imaging Signal processing algorithms Signal processing Feature extraction

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Design and Implementation of an AI-Driven Hybrid Framework for Risk Assessment

Design and Implementation of an AI-Driven Hybrid Framework f...

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Advanced algorithms and Control Engineering (ICAACE), International conference on

作者： Haibo Zhang xi Yang Dept. of Economics and Management School of Economics and Management Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

This paper discusses key challenges of data processing in the field of artificial intelligence (AI), specifically in dealing with unstructured data and adapting to market changes. We propose a novel AI risk assessment framework by developing a multi-model hybrid scoring system that integrates machine learning and deep learning, focusing on random forests and long Short-Term memory (LSTM) networks. Experimental validation shows that our framework performs more effectively in accurate risk classification compared with existing SOTA methods, significantly enhancing the capabilities of AI systems in complex data environments. Our results provide a new perspective and technical approach for AI risk assessment, which plays a crucial role in the optimization and application of AI systems.

关键词： Industries Supply chain management Machine learning algorithms Supply chains Natural language processing Risk management Sustainable development

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Improving Cross-Domain Few-Shot Classification with Multilayer Perceptron

Improving Cross-Domain Few-Shot Classification with Multilay...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shuanghao Bai Wanqi Zhou Zhirong Luan Donglin Wang Badong Chen Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China School of Electrical Engineering Xi’an University of Technology Xi’an China Westlake Institute for Advanced Study Westlake University Institute of Advanced Technology

Cross-domain few-shot classification (CDFSC) is a challenging and tough task due to the significant distribution discrepancies across different domains. To address this challenge, many approaches aim to learn transferable representations. Multilayer perceptron (MLP) has shown its capability to learn transferable representations in various downstream tasks, such as unsupervised image classification and supervised concept generalization. However, its potential in the few-shot settings has yet to be comprehensively explored. In this study, we investigate the potential of MLP to assist in addressing the challenges of CDFSC. Specifically, we introduce three distinct frameworks incorporating MLP in accordance with three types of few-shot classification methods to verify the effectiveness of MLP. We reveal that MLP can significantly enhance discriminative capabilities and alleviate distribution shifts, which can be supported by our expensive experiments involving 10 baseline models and 12 benchmark datasets. Furthermore, our method even compares favorably against other state-of-the-art CDFSC algorithms.

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