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

共 16349 条记录，以下是41-50 订阅

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Marine Targets InISAR Imaging via Virtual Antenna Construction Method under Squint Scenarios 16

Marine Targets InISAR Imaging via Virtual Antenna Constructi...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Yan, Haodong Shao, Shuai Liu, Hongwei National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

ISBN: (纸本)9781510689251

The squint imaging scenarios introduce the additional phase, leading to distortion in interferometric inverse synthetic aperture radar (InISAR) imaging results and presenting a formidable challenge for existing algorithms. To address this issue, the virtual antenna construction method (VACM) is proposed in this paper for squint InISAR imaging. In this method, the squint imaging scenarios can be transformed into simpler emmetropia imaging scenarios by compensating for the wave path difference resulting from antenna construction. Combined with the particle swarm optimization (PSO) and the angular motion parameter estimation algorithm (AMPEA), the proposed method enables the squint InISAR imaging of marine targets. Extensive experiments have validated the effectiveness and robustness of this approach for squint InISAR imaging. © 2025 SPIE.

关键词： Inverse synthetic aperture radar

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Enhanced Multimodal Conversational AI Using Speech and image Integration 5th

Enhanced Multimodal Conversational AI Using Speech and Image...

引用

5th International conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Dixit, Deepali Tyagi, Mihul Kandoi, Raunak Singh, Raghuraj School of Engineering and Technology Sharda University Greater Noida201310 India

ISBN: (纸本)9789819773701

The development of conversational artificial intelligence (AI) is examined in this research paper, with a focus on how speech and image recognition technologies can be combined to transform and interact with systems. The emphasis is on cutting-edge developments in multimodal conversational AI, particularly the smooth fusion of sophisticated image and speech processing methods to improve user experiences. The study addresses the applications, difficulties and potential applications of multimodal conversational AI by conducting a thorough review of the existing literature and case studies. By emphasizing the significance of enhancing voice recognition algorithms and utilizing advanced image processing techniques, the study promotes contextual understanding and customization. We have developed an innovative multimodal conversational AI system that integrates speech, text and image processing capabilities for seamless human–computer interactions. This study presents an improved multimodal conversational AI system that integrates several techniques, such as Google Text-to-Speech (gtts), speech recognition, sound playback and Convolutional Neural Networks (CNN). Our model architecture combines RNNs for sequential data analysis, CNNs for image processing and attention mechanisms for context extraction. Trained on diverse datasets, our model is optimized for accuracy and robustness. Evaluation against existing models demonstrates superior performance in terms of accuracy, speed and resource efficiency. Our study highlights how improved multimodal conversational AI can advance the field of human–computer interaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Deepfake image and Video Detection Using Deep Learning algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

引用

2025 International conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Srinivasan, Srivanth Nischitha, P. Chavan, Akshita Mohana Rv College of Engineering® Bengaluru India Rv College of Engineering® Bengaluru India

ISBN: (纸本)9798331509828

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%. © 2025 IEEE.

关键词： Adversarial machine learning

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Data processing, analysis and Machine Learning of Electrical Atomic Force Microscopy (AFM) in Data Cube mode: conduction of a nanomaterial for Lithium-ion battery electrodes 6

Data processing, analysis and Machine Learning of Electrical...

引用

6th IEEE International conference on image processing, Applications and systems, IPAS 2025

作者： Germanicus, Rosine Coq De Wolf, Peter Klapetek, Petr Normandie Univ Unicaen Ensicaen CNRS CRISMAT UMR6508 Caen14000 France Bruker Nano Surfaces Santa BarbaraCA93117 United States Czech Metrology Institute Brno638 00 Czech Republic

ISBN: (纸本)9798331506520

With the increasing integration of functional systems, nanoscale characterization has become crucial not only for material investigation but also for advancing the understanding of local behavior and optimizing performances. This paper details data processing techniques and machine learning methodologies for the analysis and prediction of advanced electrical Atomic Force Microscopy datasets. The datasets are acquired using a hyperspectral Data Cube approach, which combines both mapping and spectroscopy acquisitions. In the context of decarbonization and electrification, current challenges are related to energy storage and transport. A cathode material used in Lithium-ion battery electrodes is investigated for its electrical transport properties. To comprehensively characterize the electrical properties, the conductive mode of Atomic Force Microscopy is employed using the Data Cube method, which enables current-voltage spectroscopy at each pixel of the acquisition. The analysis of experimental datasets reveals localized electrical behavior of the material's components as a function of applied voltage. Furthermore, machine learning algorithms are employed to predict current maps and cluster distinct regions based on their electrical characteristics. © 2025 IEEE.

关键词： Network security

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th International conference on Intelligent Control and Information processing, ICICIP 2025

作者： Huang, Zhixuan Luo, xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 IEEE.

关键词： Contrastive Learning

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A Cloud-Edge Integrated Water Extraction Using Superpixel Segmentation 18th

A Cloud-Edge Integrated Water Extraction Using Superpixel Se...

引用

18th International conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Zhang, Qingmin Chen, Chen Zhou, Yang Wang, Zhiyi Jin, Fan Lu, Haitao Wan, Shaohua School of Communication Engineering Xidian University Xi’an China Xidian Hangzhou Institute of Technology Hangzhou311231 China The State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun130000 China National Key Laboratory of Science and Technology on Space Microwave CAST Xi’an710100 China Ningbo Joynext Technology Corporation Ningbo361000 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

ISBN: (纸本)9783031714665

With the development of the Industrial Internet of Things (IIoT) and the proposal of smart water conservancy, the integration of the Internet of Things (IoT), edge computing, and computer vision for hydrological information monitoring has become a trend in water conservancy. Difficult to provide comprehensive hydrological data from hydrological observation stations, hydrological forecasting in uninformative basins has become one of the research hotspots. water extraction can provide support for many hydrological monitoring. However, The water is difficult to extract when the water in the near-water image has reflection and water surface ripples. We propose a multi-condition water extraction method based on superpixel segmentation under cloud-edge integration architecture. Firstly, based on the color characteristics of the water and the surrounding vegetation, we propose a reflection processing mechanism. Secondly, we propose a mechanism based on superpixel segmentation for the effect of reflection on water extraction. Then, we propose a contour correction mechanism. Finally, the flood-filling algorithm is used to extract the water. We compare the traditional image segmentation algorithm with our proposed segmentation method. The results show that the proposed method outperforms other algorithms in all evaluation metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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A Novel Track Initiation Method based on Rule Knowledge and Deep Detection Network 16

A Novel Track Initiation Method based on Rule Knowledge and ...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Wang, Zengke Zhang, Weijie Pan, Meiyan Liu, Dai Xi'an Electronic Engineering Research Institute China

ISBN: (纸本)9781510689251

Radar target tracking plays an important role in reconnaissance, monitoring and defense. Clutter caused by complex electronic and natural environments poses many challenges to radar target tracking. The current track initiation method, which heavily relies on empirical knowledge, struggles with the increasing problem of false tracks and missed detections. To address this, a track initiation method based on rule knowledge and a deep detection network is proposed. In this method, radar echoes are converted into images that comprehensively reflect the different frames and target shapes, facilitating processing. With the help of the excellent detection, positioning and recognition ability of the deep learning network Faster-RCNN, the candidate boxes where might exist target track in the echo image are extracted candidate boxes where targets might exist in the echo image are extracted. Additionally, the corresponding angle and wave gate are set as initiation rules. The combination of radar data learning and partial experiential rules reduces the need for accurate prior parameters in traditional methods. Furthermore, by considering the spatial and temporal information of the target comprehensively, it achieves a good balance between the correct initiation rate and false initiation rate. Simulation environments under different observation trajectories are constructed, and Monte Carlo simulation is used to compare the proposed method with the traditional track initiation method and other track initiation methods combined with machine learning and prior rules. Results show that our method can ensure the correct initiation rate of the target track and effectively suppress the false initiation rate. © 2025 SPIE.

关键词： Deep learning

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Danger Detection and Cloud-Based Vocal Assistance System for Visually Impaired Users Using Meta Quest 3 25

Danger Detection and Cloud-Based Vocal Assistance System for...

引用

16th ACM Multimedia systems conference, MMSys 2025

作者： De Fiore, Fabrizio Murray, Niall Barrett, John Keighrey, Conor Technological University of the Shannon: Midlands Midwest Athlone Ireland

ISBN: (纸本)9798400714672

Individuals who experience a significant loss of vision or blindness often face challenges navigating the world. In such scenarios, there is often a dependence on various types of support or aids to enable individuals to move through their surroundings. Examples of this include guide dogs and assistive white canes. Those who suffer from blindness also rely on a heightened sense of auditory processing. Despite these solutions, navigating the modern world is challenging from a safety *** recent years, computer vision algorithms have been developed to interpret and report the contents of image streams. Applications of these algorithms have often focused on automated labeling of data (image analysis and audio processing etc.) within vast media databases;however, they can be leveraged to operate with live camera streams. In this research, the authors present an overview of a proof-of-concept technology stack to implement a danger detection system for individuals with significant visual impairment. The system integrates an advanced cloud driven approach employing a fine-tuned YOLO model for hazard detection, performs obstacle avoidance, segmentation processes for scene understanding, text recognition through optical character recognition (OCR), and visual question answering (VQA) for user queries. These technologies collectively address critical issues such as dynamic hazard recognition, spatial awareness, and personalized assistance. © 2025 Copyright held by the owner/author(s).

关键词： image segmentation

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An Automatic Data Cleaning Method for Radar Emitter Signal Recognition 16

An Automatic Data Cleaning Method for Radar Emitter Signal R...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Jing, Zehuan Mu, Hao Li, Peng Yin, Xuefeng Zhang, Jiamiao Li, Jingyi Wang, Zhao School of Electronic Engineering Xidian University Xi’an710071 China National Key Laboratory of Electromagnetic Space Security Chengdu610036 China

ISBN: (纸本)9781510689251

With the rapid advancement of modern electronic warfare, the volume of signals intercepted by electronic reconnaissance equipment is increasing exponentially, yet the quality of these signals remains inconsistent. Low-quality signals hinder radar signal recognition tasks, particularly those employing deep learning algorithms. Despite this challenge, there has been limited research focused on cleaning radar emitter signal data to enhance the quality of radar signal datasets. To address this issue, we propose a data cleaning method that automatically detects and removes low-quality signal samples from radar emitter signal datasets. By analyzing the characteristics of the received radar signals, we extract resemblance coefficients from their envelopes, effectively distinguishing noise signals from pulse signals in the feature space. We then apply the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster different types of signals. This approach enables the automatic detection and removal of noise data from the dataset. Extensive experiments on both simulated and experimental data demonstrate that the proposed method significantly improves data quality and enhances the recognition rate of deep learning networks for emitter recognition. © 2025 SPIE.

关键词： Radar signal processing

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image Search Engine with Recognition 4

Image Search Engine with Recognition

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

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

ISBN: (纸本)9798331523923

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. © 2025 IEEE.

关键词： Motion estimation

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