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

共 16408 条记录，以下是441-450 订阅

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Performance Evaluation of Underwater image Enhancement Techniques

Performance Evaluation of Underwater Image Enhancement Techn...

引用

2024 IEEE International conference on Information Technology, Electronics and Intelligent Communication systems, ICITEICS 2024

作者： Shubhangi, Adagale-Vairagar Gupta, Praveen CSMU Navi Mumbai Mumbai India

ISBN: (纸本)9798350382693

Absorption, scattering, and colour distortion make underwater photography difficult. Marine biology, underwater archaeology, and surveillance need better underwater photos. This research compares cutting-edge underwater image-enhancing methods to assess their efficacy and usefulness. This paper provides a comparative analysis of the various filtering approaches for underwater image enhancement, such as median filtering, average filtering, Gaussian filtering, Wiener filtering, and wavelet-based soft thresholding. The performance of these techniques is evaluated based on the mean square error, peak signal-to-noise ratio, average gradient, PCQI, and UICQI. The effectiveness of the suggested filtering scheme is validated on some sample images of the public UIEB dataset. It provides the analytical finding, advantages, disadvantages and performance of the different techniques for wide variety of underwater images enhancement. The paper also explores underwater image enhancement issues and future approaches. The promising research directions are integrating domain-specific information, real-time processing, and adaptive algorithms for dynamic underwater scenes. The discoveries help increase underwater photography visibility and interpretability, increasing underwater exploration and monitoring systems. © 2024 IEEE.

关键词： Underwater imaging

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Real-Time Multi-Camera Video Stitching Algorithm with Adaptive Sensing of Illumination and Dynamic Objects 4

Real-Time Multi-Camera Video Stitching Algorithm with Adapti...

引用

4th Asia-Pacific conference on Communications Technology and Computer Science, ACCTCS 2024

作者： Lei, Min He, Lijun Li, Fan Xi'an Jiaotong University Department of Information and Communications Engineering Xi'an China

ISBN: (纸本)9798350359985

With the development of digital image processing technology, the demand and application of wide-view video continue to grow. However, the existing stitching algorithms may result in unnatural appearance, misalignment, and ghosting issues when stitching scenes with inconsistent brightness or dynamic objects. To address the above problems, we propose a real-time multi-camera video stitching algorithm with adaptive sensing of illumination and dynamic objects. Within the proposed illumination compensation module, brightness equalization across varying illumination conditions is attained through the application of gain compensation to image blocks. We propose a histogram-based dynamic object detection approach to dynamically detect the presence of moving objects along the stitching seam. This method facilitates the adaptive detection of dynamic entities by analyzing the histogram disparities between the local vicinity of the stitching seam and the broader image context. The experimental results show that it can effectively solve the problem of unnatural stitching results arising from the uneven brightness of the image, and can effectively detect the presence of dynamic objects near the stitching seam so that the effect of the video stitching has been improved. © 2024 IEEE.

关键词： Object detection

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Detail enhancement for infrared image based on Iterative least squares and difference of Gaussian filter 3

Detail enhancement for infrared image based on Iterative lea...

引用

3rd International Computing Imaging conference, CITA 2023

作者： Chen, Zhiqiang Zhao, Zehao Guan, Lei Zhou, Feng Chen, Yaohong Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Shaanxi Xi’an710119 China University of Chinese Academy of Sciences Beijing100049 China Xi’an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology Shaanxi Xi’an710119 China

ISBN: (纸本)9781510671409

Effective visualization of infrared images with low contrast and low signal-to-noise ratio is one of the key technologies for high-performance infrared imaging systems. The conventional decomposition-based algorithms have advantages in image details enhancement, but still suffer from high computational cost, unbalanced noise suppression and detail information. In this paper, we decompose the base and detail components of the image by the iterative least squares and the Difference of Gaussian filter, and further enhance the base layer and the detail layer via plateau equalization and gradient mask, respectively. We then fusion the enhancement result and re-project to 8-bit dynamic range. Experimental results shown that the proposed method achieves a good balance between detail enhancement and computational cost, with a high-performance in different scenes. © 2023 SPIE.

关键词： image enhancement

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TMFER: Multimodal Fusion Emotion Recognition Algorithm Based on Transformer 4

TMFER: Multimodal Fusion Emotion Recognition Algorithm Based...

引用

4th International conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Qin, Zunying Chen, xingyuan Li, Guodong Cui, Jingru Xi'An Jiaotong University Network Information Center Xi'an China Xi'An Jiaotong University School of Software Xi'an China

ISBN: (纸本)9798350343113

According to the problems of the existing emotion recognition algorithms, which are not rich in emotion information, weak in feature representation and not high in recognition accuracy, this paper proposes a multimodal fusion emotion recognition algorithm based on Transformer (TMFER), which fuses three modalities of text, speech and image information for emotion recognition. For the different characteristics of each modal information, Bert model pre-training processing, MFCC feature extraction and CNN feature extractor extraction methods are used to extract features for each modality respectively, to explore deeper features. To address the problem of unreasonable combination of multi-modal features, the Transformer Encode multi-headed attention mechanism is used to build a feature fusion module to extract and combine potential feature information in different modalities in parallel. The fused data are fed into the algorithm classification module for sentiment recognition classification, and a joint supervised loss function based on large margin learning is customized to solve the problem of unbalanced classification and feature confounding in the baseline model. Finally, based on the IEMOCAP and MELD multimodal datasets, the TMFER algorithm is experimentally compared with current algorithms in the field that are more effective in emotion recognition classification. The experimental results show that the TMFER algorithm outperforms other algorithms in all evaluation metrics. © 2023 IEEE.

关键词： Emotion Recognition

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2024 18th International conference on Open Source systems and Technologies, ICOSST 2024 - Proceedings

2024 18th International Conference on Open Source Systems an...

引用

18th International conference on Open Source systems and Technologies, ICOSST 2024

ISBN: (纸本)9798331508692

The proceedings contain 23 papers. The topics discussed include: solar powered auto irrigation monitoring system with plant health indication using image processing;large language model and artificial intelligence based human conversation agent;to enhance graph-based retrieval-augmented generation (RAG) with robust retrieval techniques;automatic timetable generation using neural networks trained by genetic algorithms;assessment of efficient and cost-effective vehicle detection in foggy weather;enhanced detection and prevention of SQL injection and cross-site scripting attacks in web applications: analyzing algorithms and threat modeling approaches;efficient duplicate question detection;and a comprehensive survey on computing services to detect stress and anxiety using smart devices.

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Cloud Architectural Design for image-based Vehicle Positioning in Traffic Management 28

Cloud Architectural Design for Image-based Vehicle Positioni...

引用

28th International conference on System Theory Control and Computing

作者： Beti, Iosif-Alin Herghelegiu, Paul-Corneliu Amarandei, Cristian-Mihai Caruntu, Constantin-Florin Gheorghe Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Iasi Romania Gheorghe Asachi Tech Univ Iasi Dept Comp Sci & Engn Iasi Romania

ISBN: (纸本)9798350364309;9798350364293

Vehicle positioning algorithms are essential for improving traffic management and safety by accurately locating vehicles in real-time, and, thus, minimizing congestion and accidents. They also support the development of advanced driver assistance systems and autonomous vehicles, relying on precise positioning data for safe navigation. One of the solutions involves using image processing algorithms, which can have two approaches. One approach is decentralized, in which each vehicle performs its own computing steps and determines its position concerning the other nearby vehicles. The second approach, proposed in this paper, is centralized, where each vehicle sends data to a server that uses cloud computing to process all the data in real-time. As such, vehicles can create a more comprehensive view of the driving conditions in the area by using either of these two approaches, which can help them anticipate potential hazards and make more informed decisions.

关键词： computer vision traffic optimization vehicle platooning cloud computing 5G networks vehicle positioning V2V communication

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Optimized Machine Learning for Medical Data Analysis and Disease Prediction

Optimized Machine Learning for Medical Data Analysis and Dis...

引用

2024 International conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Dubey, Anil Kumar Kapoor, Raman Saraswat, Mala Abes Engineering College Ghaziabad India Bennett University Greater Noida India

ISBN: (纸本)9798350379716

In the context of healthcare and human-computer interaction., this research study provides a thorough analysis of sophisticated computational algorithms for data classification, picture processing, and disease prediction. For multi-disease prediction and intrusion detection, various works have been investigated to arrive at optimal hybrid learning methods and biological feature selection techniques. The role of revolutionary blockchain-based data storage systems and enhanced analysis techniques of EEG data for brain illness detection have been reviewed. Furthermore, an improved bidirectional long short-term memory (LSTM) for time-series data categorization is also discussed. Some noteworthy studies such as the analysis of medical picture defects, application of immersive technology to diagnose Alzheimer's illness and improvements in image resolution and segmentation are also reviewed in this work. Based on the reviewed literature, an improved method is also proposed which is expected to assist with early diagnosis and risk reduction. © 2024 IEEE.

关键词： Brain-Computer Interface Feature Selection Techniques Hybrid Learning algorithms Intrusion Detection systems and Medical image Analysis

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ADIMPR-Net: Automatic Denoising Intra-pulse Multi-parameter Recognition with a Deep Multi-task Network

ADIMPR-Net: Automatic Denoising Intra-pulse Multi-parameter ...

引用

International conference on Electronic Engineering and Information systems (EEISS)

作者： Liu, Yingming Chen, Tao Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9798350351040;9798350351033

To concurrently address the challenges of automatic modulation recognition (AMR) and modulation parameter estimation (MPE) in radar signals, we introduce a multi-task learning (MTL) network architecture designed for intra-pulse multi-parameter recognition through automatic noise reduction. The proposed approach initiates by generating time -frequency image (rn) directly from noisy time-domain signal using a neural network. To effectively capture relevant features from TFls, we employ a VGG-like network Subsequently, the multitask learning framework is utilized to achieve AMR and MPE simultaneously. Four typical intra-pulse modulated signals were simulated in the experiments, simulation results verify the effectiveness and reliability of the proposed algorithms.

关键词： component: Radar signal Signal processing modulation recognition parameter estimation deep learning

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An anti-jamming method based on joint spatial-polarimetric processing for FDA-MIMO Radar 4

An anti-jamming method based on joint spatial-polarimetric p...

引用

4th International conference on Signal image processing and Communication, ICSIPC 2024

作者： Zhang, Yunhui Pang, Bo Dai, Dahai Tan, Zhengkuan College of Electronic Science and Technology National University of Defense Technology Changsha410073 China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510682467

With the continuous development of radar jamming methods and styles, radar systems are facing more and more complex electromagnetic environment, resulting in limited suppression performance through spatial anti-jamming measures solely. In order to solve the problem, this paper proposes an anti-jamming method based on joint spatial-polarimetric processing for Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) system. Firstly, the signal model of Polarimetric FDA-MIMO (PFDA-MIMO) is established. Based on this model, a joint spatial-polarimetric adaptive filtering method is proposed and experiments are carried out to demonstrate the advantages of joint spatial-polarimetric processing in anti-jamming. Compared to the filtering method that only utilizing the spatial information, this method has a better performance dealing with multidimensional jamming and improves the ability of FDA-MIMO in resisting main lobe jamming. © 2024 SPIE.

关键词： Adaptive filtering

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Learning Trajectories are Generalization Indicators 37

Learning Trajectories are Generalization Indicators

引用

37th conference on Neural Information processing systems (NeurIPS)

作者： Fu, Jingwen Zhang, Zhizheng Yin, Dacheng Lu, Yan Zheng, Nanning Xi An Jiao Tong Univ Natl Key Lab Human Machine Hybrid Augmented Intel Natl Engn Res Ctr Visual Informat & Applicat Xian Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Microsoft Res Asia Beijing Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781713899921

This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient descent algorithms. Instead of concentrating solely on the generalization error of the DNN post-training, we present a novel perspective for analyzing generalization error by investigating the contribution of each update step to the change in generalization error. This perspective enable a more direct comprehension of how the learning trajectory influences generalization error. Building upon this analysis, we propose a new generalization bound that incorporates more extensive trajectory information. Our proposed generalization bound depends on the complexity of learning trajectory and the ratio between the bias and diversity of training set. Experimental observations reveal that our method effectively captures the generalization error throughout the training process. Furthermore, our approach can also track changes in generalization error when adjustments are made to learning rates and label noise levels. These results demonstrate that learning trajectory information is a valuable indicator of a model's generalization capabilities.

关键词： Deep neural networks

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