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

共 16347 条记录，以下是601-610 订阅

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Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR image Fusion 56

Convolutional Simultaneous Sparse Approximation with Applica...

引用

56th Asilomar conference on Signals, systems, and Computers

作者： Veshki, Farshad G. Vorobyov, Sergiy A. Aalto Univ Dept Signal Proc & Acoust Espoo Finland

ISBN: (纸本)9781665459068

Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports. The SSA model has been used in various signal and image processing applications involving multiple correlated input signals. In this paper, we propose algorithms for convolutional SSA (CSSA) based on the alternating direction method of multipliers. Specifically, we address the CSSA problem with different sparsity structures and the convolutional feature learning problem in multimodal data/signals based on the SSA model. We evaluate the proposed algorithms by applying them to multimodal and multifocus image fusion problems.

关键词： Simultaneous sparse approximation convolutional sparse coding dictionary learning image fusion

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Error Analysis of a Multi-Sensor Maritime Targeting System 20

Error Analysis of a Multi-Sensor Maritime Targeting System

引用

conference on Electro-Optical and Infrared systems - Technology and Applications XX

作者： Hickman, Duncan L. Niebla, Maria Sang, Daekyu Kim, Boomin Baek, Jeonghyun Tektonex Ltd Long View Argyll Rd Kilcreggan Argyll & Bute Scotland Agcy Def Dev 160 Bugyuseong Dero 488beon Gil Daejeon South Korea

ISBN: (数字)9781510667044

ISBN: (纸本)9781510667037

Targeting systems are subject to multiple sources of error when operating in complex environments. To reduce the effect of these errors, modern targeting systems generally include both imaging and RF sensors. Data processing then provides target detection and classification information, and the detection streams are combined using a data fusion scheme to produce an optimal target location estimate with an associated latency. In this paper, the performance of a multi- sensor system in a maritime application is investigated using a mathematical simulator that has been developed to provide the system performance error analysis for different engagement scenarios and test conditions. This simulator is described together with the sources of targeting error such as image motion blur and radar glint. Additionally, the impact of flare and chaff countermeasures on the targeting performance is reviewed in terms of different types of target recognition and tracking algorithms.

关键词： Multisensor systems Error Analysis image Blur Radar Glint Model Validation Countermeasure Model ATR processing Targeting systems

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image Preprocessing to Improve Object Recognition in Complex Weather Conditions

Image Preprocessing to Improve Object Recognition in Complex...

引用

2023 International Russian Smart Industry conference, SmartIndustryCon 2023

作者： Schegolikhin, Yaroslav Mitrokhin, Maxim Eremin, Alexandr Penza State University Penza Russia

ISBN: (纸本)9781665464291

Object recognition systems on images allow to automate of various routine processes. Vehicle detection systems are becoming widespread and are being incorporated into, "smart city"initiatives, and traffic control systems. Some applications of such systems are related to the impact of precipitation and other atmospheric phenomena that degrade image quality. The deterioration of image characteristics leads to a decrease in the efficiency of the entire automated system. The research issue is to develop a software solution to improve the efficiency of vehicle detection on images captured in difficult weather conditions. The neural network detector YOLOv5, trained on images collected by the authors from open sources, was used as an algorithm for the detection of cars on the images. Contrast correction and neural network methods were used as image enhancement algorithms. The performance of the YOLOv5 detector was compared on a test base of images preprocessed by image enhancement methods. It was found that the algorithms chosen for the study were indeed able to improve image quality by partially removing interference. Generative neural networks can introduce visual distortions and initially missing elements in images, so their application within the task's framework is very limited. image processing methods based on contrast correction allow for the improvement of vehicle detector performance on images with interference, but their application negatively affects performance on images without interference. On average, the performance of the detector decreases insignificantly when comparing images with and without interference. © 2023 IEEE.

关键词： Efficiency

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Deep Learning-based Serialized Model and Feature Extraction Algorithm for Solid Oxide Fuel Cell State Prediction 43

Deep Learning-based Serialized Model and Feature Extraction ...

引用

43rd Chinese Control conference, CCC 2024

作者： Wu, Jiajian Zhu, Kanglin Wu, Han xiang, Guohui Li, xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen China

ISBN: (纸本)9789887581581

Deep learning-based methods have shown their wide application prospects in the field of solid oxide fuel cell(SOFC) prediction. However, the irrationality of the prediction object and the lack of prediction accuracy have hindered the progress of the prediction methods for SOFC systems. Therefore, two algorithms are proposed in this study, a feature selection algorithm based on maximum information coefficient and Bayesian information criterion for identifying key variables that are sensitive to changes in the SOFC system, and a prediction model based on a genetic algorithm optimized for sequence to sequence architecture. The M/BIC algorithm reduces the number of features by 62.5% (from 40 to 15), which contributes to the acceleration of the algorithm training and the subsequent sensor layout optimization. The prediction model incorporates a two-dimensional convolutional neural network and a multi-head attention mechanism to capture more information in the input sequence, an encoder-decoder architecture and a Gated recurrent unit to better predict performance trends. Tests with real experimental data show significant changes in feature dimensionality reduction and prediction accuracy. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Dimensionality reduction

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A GNSS RTK/INS Tight Coupling Method Under Challenging Scenarios 14

A GNSS RTK/INS Tight Coupling Method Under Challenging Scena...

引用

14th IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2024

作者： Ji, Zikang Zhao, Yanyan Zhao, Hongwei Li, xiaoang School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350366556

The Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) positioning technology is a robust algorithm capable of providing centimeter-level accuracy in environments with favorable satellite visibility. However, in GNSS-Challenging environments such as urban canyons, where buildings and foliage may weaken GNSS signals, impede signal tracking by GNSS receivers, and exacerbate multi-path effects, RTK positioning can suffer performance degradation. The tight integration of Inertial Navigation systems (INS) with RTK overcomes the limitations inherent when each system operates independently, delivering enhanced performance. This study realizes a tightly coupled RTK/INS algorithm. Performance tests and analyses were conducted on public vehicular datasets and one empirically measured static data set, which presents challenges in RTK algorithms. The results indicate that the tight integration algorithm improved the positioning accuracy of the empirically measured static data and the open dynamic data set. Researchers may access our datasets via the provided link. © 2024 IEEE.

关键词： Inertial navigation systems

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Online Antenna Mode Selection for Reconfigurable MIMO via Adaptive Bandit 15

Online Antenna Mode Selection for Reconfigurable MIMO via Ad...

引用

15th IEEE International conference on Wireless Communications and Signal processing, WCSP 2023

作者： Zhang, Boxi Li, Ang Xi'an Jiaotong University School of Information and Communications Engineering Xi'an China

ISBN: (纸本)9798350324662

Reconfigurable antennas (RAs) can enhance link capacity by swiftly changing their radiation pattern to adapt to the wireless propagation environment actively. In order to exploit the property of RAs, selecting the time-varying optimal mode from candidate radiation patterns for MIMO communication is essential but however challenging in a mobile environment. In this paper, we focus on the efficient radiation mode selection for reconfigurable MIMO systems, where the transmit array consists of RAs instead of conventional antennas with fixed radiation mode. Channel prediction based on angle information is studied to obtain the channel state information (CSI) of unselected modes based on the estimated path gains. We further propose two bandit algorithms: upper confidence bound based on adaptive bandit with sliding window (UCB-AB) and Thompson sampling based on adaptive bandit with sliding window (TS-AB). UCB-AB and TS-AB exploit the useful parts of outdated CSI outside the sliding window and improve the convergence rate of bandit algorithms under mobile scenarios. Numerical results demonstrate that the introduction of adaptive bandit can enhance the convergence of bandit algorithms and improve the throughput of MIMO systems. © 2023 IEEE.

关键词： antenna mode selection multi-armed bandit (MAB) online learning Reconfigurable antennas (RAs) Reconfigurable MIMO

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Vision Semantics image Captioner

Vision Semantics Image Captioner

引用

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

作者： Pereira, Gavin Kundar, Risha Kushwaha, Devendra Shaikh, Phiroj Don Bosco Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350382693

This paper introduces the completed project development of a cutting-edge Vision Semantics image Captioner., a comprehensive platform aimed at generating contextually rich descriptions for images. Focused on leveraging advancements in vision semantics, our system utilises state-of-the-art techniques to provide detailed and meaningful captions for a wide array of images. The primary objective of this research is to enhance the understanding and interpretation of images through automated captioning, contributing to the fields of computer vision and natural language processing. The image captioner goes beyond basic descriptions by incorporating semantic nuances, ensuring that the generated captions capture the essence and context of the visual content. Our platform addresses the need for a reliable and efficient image captioning system by offering a user-friendly interface and incorporating robust algorithms. The implementation plan involved thorough literature surveys, enabling us to integrate the latest advancements in vision semantics into the image captioning process. In addition to the technical aspects, the paper discusses the practical applications of the Vision Semantics image Captioner, emphasizing its potential in various domains such as accessibility, content indexing, and assistive technologies. The research also includes guidelines for optimising and customizing the system for specific use cases. By presenting a comprehensive solution for image captioning, this research contributes to the advancement of automated visual understanding. The platform's capabilities are demonstrated through a series of evaluations, showcasing its effectiveness in generating accurate and meaningful image captions. We believe that the Vision Semantics image Captioner has the potential to revolutionize the way images are interpreted, providing valuable insights across diverse applications. © 2024 IEEE.

关键词： Intelligent systems

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A New Extrapolation Solution for MU-MISO Symbol-Level Precoding 16

A New Extrapolation Solution for MU-MISO Symbol-Level Precod...

引用

16th International conference on Wireless Communications and Signal processing, WCSP 2024

作者： Liang, Mu Li, Ang Hu, xiaoyan Masouros, Christos School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China University College London Department of Electronic and Electrical Engineering London United Kingdom

ISBN: (纸本)9798350390643

Constructive interference (CI) precoding, which converts the harmful multi-user interference into beneficial signals, is a promising and efficient interference management scheme in multi-antenna communication systems. However, CI-based symbol-level precoding (SLP) experiences high computational complexity as the number of symbol slots increases within a transmission block, rendering it unaffordable in practical communication systems. In this paper, we propose a symbol-level extrapolation (SLE) strategy to extrapolate the precoding matrix by leveraging the relationship between different symbol slots within in a transmission block, during which the channel state information (CSI) remains constant, where we design a closed-form iterative algorithm based on SLE both for PSK and QAM modulation. In order to further reduce the computational complexity, a sub-optimal closed-form solution based on SLE is further developed. Extensive simulation results illustrate that the proposed algorithms can dramatically reduce the computational complexity and time complexity with only marginal performance loss, compared with the conventional SLP design methods. © 2024 IEEE.

关键词： Extrapolation

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MedSeg: A Medical image Analyzer

MedSeg: A Medical Image Analyzer

引用

Control Instrumentation System conference (CISCON) - Guiding Tomorrow - Emerging Trends in Control, Instrumentation, and systems Engineering

作者： Mehta, Shlok Pancholi, Keyur Pandey, Sakshi Dhage, Dr Sudhir Sardar Patel Inst Technol Comp Engn Dept Mumbai Maharashtra India

ISBN: (纸本)9798350375480;9798350375497

This research paper aims to address the critical need for efficient and accurate identification of chest diseases using chest X-rays through a combination of advanced image processing techniques and machine learning algorithms. With the growing prevalence of respiratory and cardiovascular conditions worldwide, timely and precise diagnosis is paramount for effective patient care. The study begins with a comprehensive review of existing methodologies and technologies employed in the identification of chest diseases from X-ray images. It critically evaluates the strengths and limitations of current approaches, highlighting the challenges faced in achieving high accuracy, speed, and scalability. To address these issues, the project aims to develop an AI-powered system for medical image analysis. In response to these challenges, our research proposes a novel approach that integrates Inception V3 model and imagenet. We leverage a large dataset of annotated chest X-rays to train a deep neural network capable of recognizing subtle patterns indicative of various diseases, including pneumonia, pneumothorax, lung and cardiac abnormalities. The model is optimized to provide not only accurate diagnoses but also to minimize false positives and negatives. In conclusion, this research contributes to the ongoing efforts in utilizing chest X-ray images for disease identification, presenting a robust and efficient methodology that could revolutionize the current diagnostic landscape. The findings hold promise for the development of automated systems capable of assisting healthcare professionals in the accurate and timely detection of chest diseases, ultimately contributing to enhanced patient care and management.

关键词： Machine learning Medical image analysis Chest X-Rays prediction imageNET InceptionV3 Disease prediction

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Cancer Sight: Illuminating the Hidden-Advancing Breast Cancer Detection with Machine Learning-Based image processing Techniques

Cancer Sight: Illuminating the Hidden-Advancing Breast Cance...

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2023 International conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sirisati, Ranga Swamy Kumar, C. Srinivasa Venuthurumilli, Pradeep Ranjith, J. Rao, Kanusu Srinivasa Ghatkesar Hyderabad Telangana Kondapur India Vignan's Institute of Management and Technology for Women Department of Cse Ghatkesar Hyderabad Telangana Kondapur India Mallareddy Engineering College for Women Department of Computer Science & Engineering Maisammagida Hyderabad500090 India Vignan's Institute of Management and Technology for Women Department of Information Technology Ghatkesar Hyderabad Telangana Kondapur India Yogi Vemana University Department of Computer Science and Technology Kadapa India

ISBN: (纸本)9798350313987

Breast cancer is a significant global health concern, with early detection being critical for successful treatment and improved patient outcomes. In recent years, machine learning-based image processing techniques have emerged as powerful tools in the field of medical imaging, particularly in breast cancer detection and diagnosis. This research paper explores the application of machine learning algorithms to enhance the accuracy and efficiency of breast cancer detection using various medical imaging modalities, such as mammography, ultrasound, and magnetic resonance imaging (MRI). The study begins by reviewing the current state of breast cancer detection methodologies and highlighting their limitations. It then delves into the utilization of machine learning algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), and deep learning models, for the automated analysis and interpretation of breast cancer images. Various preprocessing steps, feature extraction techniques, and data augmentation methods are discussed to optimize the performance of these algorithms. Furthermore, the paper examines the integration of machine learning models with radiomics, genomics, and clinical data to create comprehensive breast cancer diagnostic systems. These integrated systems aim to provide more accurate risk assessment, personalized treatment recommendations, and improved patient management. The results of several case studies and clinical trials are presented to demonstrate the effectiveness of machine learning-based image processing techniques in breast cancer detection. These studies illustrate how these techniques can improve sensitivity, specificity, and overall diagnostic accuracy compared to traditional methods. This research underscores the promising role of machine learning-based image processing techniques in advancing breast cancer detection. It highlights the potential for early diagnosis and improved patient care, paving the way for mor

关键词： Breast cancer Convolutional neural networks (CNNs) image processing Machine learning Magnetic resonance imaging (MRI) Support vector machines (SVMs)

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