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检索条件"任意字段=Conference on Applications of Digital Image Processing XIX"

共 10246 条记录，以下是81-90 订阅

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Navigating Beyond GPS: Horizon Detection for Autonomous Navigation

Navigating Beyond GPS: Horizon Detection for Autonomous Navi...

引用

International image processing, applications and Systems conference (IPAS)

作者： Alastair Finlinson Sotiris Moschoyiannis Björn Holmberg Department of Computer Science University of Surrey Surrey UK Space and Defence SAAB Defence Sweden Upsala Sweden

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In a scenario where a GPS signal is unavailable, navigation becomes more challenging. To enable precise navigation, visual features of the surroundings can be interpreted to direct vessels. We propose to use the visible horizon line as a novel solution. The visible horizon line is constructed from features at the intersection between the sky, land masses, and the waterline. Accurate extraction relies on a robust model operating in adverse weather and equipment conditions. We propose to tackle this new problem with an approach based on semantic boundary prediction with a framework that primarily looks at deep supervision for feature conditioning and differential edge detection to incorporate strong priors early in the training process. We also use semantic segmentation as a support task to provide a strong supervision signal and features to the semantic boundaries prediction task. We conduct experiments on the dataset to evaluate the model in various scenarios. Our results show that our framework predicts high-quality boundaries and segmentation masks over various datasets and domains with the ability to perform in low-data scenarios. We demonstrate that the combination of the labelling and the edge priors improve over the baseline by 32-44% on the semantic boundary estimation task.

关键词： Training Visualization Navigation Semantic segmentation image edge detection Semantics Feature extraction Labeling Global Positioning System Autonomous robots

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A Review On Fusion Of Spiking Neural Networks And Transformersc

A Review On Fusion Of Spiking Neural Networks And Transforme...

引用

International image processing, applications and Systems conference (IPAS)

作者： Oumaima Marsi Sébastien Ambellouis José Mennesson Cyril Meurie Anthony Fleury Charles Tatkeu IMT Nord Europe Institut Mines-Télécom Univ. Gustave Eiffel COSYS LEOST Lille France Univ. Gustave Eiffel COSYS LEOST Lille France IMT Nord Europe Institut Mines-Télécom Univ. Lille Centre for Digital Systems Lille France

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This paper provides a comprehensive review of the integration of Spiking Neural Networks (SNNs) and Transformers, combining the energy efficiency of SNNs with the high performance of Transformer architectures. By leveraging the event-driven nature of SNNs and the powerful self-attention mechanism of Transformers, this fusion aims to address the challenges of high energy consumption in deep learning while improving task accuracy, especially for complex datasets. We introduce the core concepts of SNNs and Transformers, reviewing state-of-the-art methods for their combination, including hybrid architectures. The performance of each architecture is presented thanks to both static and neuromorphic datasets, highlighting their advantages and limitations. This review also discusses the challenges of integrating self-attention into spiking architectures and outlines future research directions to further enhance model performance and energy efficiency.

关键词： Deep learning Energy consumption Accuracy Reviews Neuromorphics Spiking neural networks Transformer cores Transformers Feature extraction Energy efficiency

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Adapting Without Seeing: Text-Aided Domain Adaptation for Adapting CLIP-like Models to Novel Domains

Adapting Without Seeing: Text-Aided Domain Adaptation for Ad...

引用

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

作者： Louis Hémadou Héléna Vorobieva Ewa Kijak Frédéric Jurie Digital Sciences & Technologies Department Safran Tech Université de Rennes IRISA INRIA CNRS Université de Caen Normandie ENSICAEN CNRS

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper addresses the challenge of adapting large vision models, such as CLIP, to domain shifts in image classification tasks. While these models, pre-trained on vast datasets like LAION 2B, offer powerful visual representations, they may struggle when applied to domains significantly different from their training data, such as industrial applications. We introduce TADA, a Text-Aided Domain Adaptation method that adapts the visual representations of these models to new domains without requiring target domain images. TADA leverages verbal descriptions of the domain shift to capture the differences between the pre-training and target domains. Our method integrates seamlessly with fine-tuning strategies, including prompt learning methods. We demonstrate TADA’s effectiveness in improving the performance of large vision models on domain-shifted data, achieving state-of-the-art results on benchmarks like DomainNet.

关键词： Learning systems Adaptation models Visualization Training data Signal processing Benchmark testing Data models Acoustics Speech processing image classification

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Optimization of Advanced Nanoscale Dynamic Voltage Comparators for the Development Future Analog to digital Converters

Optimization of Advanced Nanoscale Dynamic Voltage Comparato...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： J. Ruthwik N. Sai Mahanth B. Aravind Reddy J. Ajayan Electronics & Communication Engg. SR University Warangal India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Dynamic Voltage Comparators links the analog and digital worlds in emerging applications like IOT, Artificial Intelligence (AI), image processing. In this work the performance& reliability of nanoscale pre amplifier based two stage DVC had been studied using SPICE tools using 16nm CMOS and a V DD ranging from 0.7 V to 1.2 V. The results reveal that DVC-I&DVC-II with a V DD of 0.7/1.2 V exhibited a delay of 32.11/20.35 pS and 39.61/26.32 pS at the temperature of 27 0 C and a delay of 43.65/24.2 pS and 57.17/31.27 pS at the temperature of 107 0 C. The DVC-I & DVC-II with a V DD of 0.7/1.2 V consumed a power of 2.67/39.43 μW and 2.639/35.09 μW at temperature 107 0 C respectively. The DVC’s offer super performance at 27 0 C with a higher V DD . But at higher V DD , DVC’s Consumes higher power also elevates in operating temperature levels to the degradation in performance of DVC’s.

关键词： Temperature distribution Technological innovation image processing Voltage SPICE Nanoscale devices Delays Internet of Things Reliability Artificial intelligence

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Landscape more secure than portrait? zooming into the directionality of digital images with security implications 24

Landscape more secure than portrait? zooming into the direct...

引用

Proceedings of the 33rd USENIX conference on Security Symposium

作者： Benedikt Lorch Rainer Böhme University of Innsbruck

ISBN: (纸本)9781939133441

The orientation in which a source image is captured can affect the resulting security in downstream applications. One reason for this is that many state-of-the-art methods in media security assume that image statistics are similar in the horizontal and vertical directions, allowing them to reduce the number of features (or trainable weights) by merging coefficients. We show that this artificial symmetrization tends to suppress important properties of natural images and common processing operations, causing a loss of performance. We also observe the opposite problem, where unaddressed directionality causes learning-based methods to overfit to a single orientation. These are vulnerable to manipulation if an adversary chooses inputs with the less common orientation. This paper takes a comprehensive approach, identifies and systematizes causes of directionality at several stages of a typical acquisition pipeline, measures their effect, and demonstrates for three selected security applications (steganalysis, forensic source identification, and the detection of synthetic images) how the performance of state-of-the-art methods can be improved by properly accounting for directionality.

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Improving e-Health Security with Lightweight Cryptography for Enhanced Protection

Improving e-Health Security with Lightweight Cryptography fo...

引用

International conference on Wireless Communications, Signal processing and Networking (WiSPNET)

作者： Bhuvaneshwari A J Kaythry P Jegadish Kumar K. J Department of ECE Sri SivaSubramaniya Nadar College of Engineering Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

Securing medical data in e-health environments is imperative for maintaining trust and privacy. This abstract presents an innovative algorithm tailored for this purpose, employing advanced cryptographic techniques such as Cellular Automata and Circular Shift techniques. The methodology emphasizes robust protection against unauthorized access and differential attacks, which is crucial for safeguarding medical images from cyber threats. Rigorous testing, including the NIST test, validates the algorithm's reliability and suitability for e-Health applications. The comparative analysis highlights its superior security and processing efficiency over existing methods, positioning it as a leading solution for enhancing security, trust, and privacy in digital healthcare settings. The study's importance lies in addressing the critical need for secure handling of medical image data, ensuring both confidentiality and integrity. Its uniqueness lies in the innovative integration of cryptographic techniques tailored for e-Health, promising practical utility in fortifying security across healthcare sectors.

关键词： Wireless communication Privacy Automata Signal processing algorithms NIST Cryptography Reliability Protection Biomedical imaging Testing

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Solid waste classification based on AI: A review

Solid waste classification based on AI: A review

引用

International image processing, applications and Systems conference (IPAS)

作者： Mouna Zouari Mahdi Jihen Frikha Elleuch Dorra Sellami Control and Energy Management Laboratory(CEM Lab) Digital Research Center of SFAX Sfax Tunisie Control and Energy Management Laboratory (CEM Lab) National Engeneering School of Sfax (ENIS) Sfax Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Waste management is becoming an intriguing problem all over the world. waste’s volume is becoming more and more huge due to the urbanization and industrialization. This issue impacts all components of the environment, including air, water, and land. This alarming situation needs some solutions which should be rapid, urgent and innovative. For this reason, several solutions have been proposed to reduce the huge amount of waste globally. In fact, recycling may be an efficient tool, especially when the sorting process is done automatically. As this is one of the main goals of the circular economy, many researchers have focused on developing automatic solutions to address environmental problems. Recently, with rapid advancement of Artificial Intelligence (AI), different solutions were developed in order to improve accuracy and save time. Many investigations on AI techniques have been made in order to include machine learning, deep learning and ensemble approaches. This paper reviews AI based approaches for waste classification while emphasizing their real world scope. In addition, it highlights the most popular dataset used in order to validate the efficiency of the developed solutions. The main goal of this paper is to provide an overview of the existing techniques of AI helping researches to built a more accurate solution for a cleaner environment.

关键词： Waste management Deep learning Accuracy Reviews Databases Urban areas Glass Water conservation Recycling Sorting

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image Search Engine with Recognition

Image Search Engine with Recognition

引用

Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： JMSV Ravi Kumar Bellaganti Dheeraj Geddada Gowtham Dokala Vivek Babu Bonthu Asha Kiran M. Srikanth Department of Information Technology SRKR Engineering College Bhimavaram Andhra Pradesh India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

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.

关键词： Deepfakes Visualization Sentiment analysis image recognition Face recognition Search engines Feature extraction User experience Artificial intelligence Usability

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Intelligent Power Optimization and Allocation Algorithm for digital Media Content Generation Based on Deep Learning 24

Intelligent Power Optimization and Allocation Algorithm for ...

引用

Proceedings of the 7th International conference on Information Technologies and Electrical Engineering

作者： Yifan Pan Shanghai Publishing and Printing College Shanghai China

ISBN: (纸本)9798400707087

This paper proposes an intelligent power optimization and allocation algorithm based on the combination of deep learning and reinforcement learning, aiming to reduce power consumption and improve task execution efficiency in the process of digital media content generation. By introducing multi-task scenarios and rich data sets, the proposed model can dynamically predict the power demand of tasks and perform real-time power allocation with high accuracy. Experimental results show that the hybrid deep learning and reinforcement learning model significantly outperforms the traditional static power allocation method and single deep learning model in terms of power consumption and task execution efficiency, reducing the average power consumption by about 15%, shortening the task execution time by about 7%, and achieving a power allocation accuracy of 96.5%. The algorithm is applicable to various digital media content generation tasks, including video rendering, image processing, 3D modeling, and audio synthesis, providing an effective solution for intelligent power management and energy-saving applications, and has broad application prospects.

关键词： Deep Learning

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digital Twin-Driven Bearing-Fault Detection in Induction Motor and Drives using Graph Sampling and Aggregation Network

Digital Twin-Driven Bearing-Fault Detection in Induction Mot...

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

作者： Haraprasad Badajena Suryanarayan Majhi Bivash Chakraborty Mamata Jenamani Aurobinda Routray Ronit Dutta Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Industrial and Systems Engineering Indian Institute of Technology Kharagpur India Dept. of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Bearing fault diagnosis is crucial for ensuring the reliability and safety of industrial systems, particularly in preventing operational failures and maintaining product quality. Traditional signal processing methods and deep learning algorithms, while useful, often overlook the complex structural relationships within sensor data, limiting their diagnostic effectiveness. To address this, we present a novel digital Twin-Driven Fault Diagnosis Framework that integrates graph-based learning techniques with advanced signal analysis. Our approach employs XGBoost and GraphSAGE embeddings to capture both spatial and temporal correlations within the current signals. The raw sensor data is then processed using a sliding window technique and time-frequency domain features are extracted then transformed into a graph structure that represents the intricate relationship in the signal. GraphSAGE is then applied to these graph structures, generating embeddings that enhance fault detection accuracy. Additionally, XGBoost is utilized for classification, improving the overall robustness of the system. The proposed method, deployed on edge devices, delivers real-time diagnostics, providing a scalable and efficient solution for industrial applications. Experimental results using real-world datasets demonstrate that our method significantly outperforms state-of-the-art algorithms, improving detection accuracy and Area under the curve (AUC) scores up to 97% and 99%, respectively.

关键词： Fault diagnosis Time-frequency analysis Accuracy Correlation Fault detection image edge detection Signal processing algorithms Feature extraction Real-time systems Speech processing

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