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

共 15028 条记录，以下是241-250 订阅

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A Comparative Study of Generative Adversarial Networks for image Recognition algorithms Based on Deep Learning and Traditional Methods 6

A Comparative Study of Generative Adversarial Networks for I...

引用

6th IEEE International conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Zhong, Yihao Wei, Yijing Liang, Yingbin Liu, Xiqing Ji, Rongwei Cang, Yiru New York University New York United States Northwestern University Evanston United States Northeastern University Seattle United States Columbia University New York United States Southern Methodist University Dallas United States Northeastern University Boston United States

ISBN: (纸本)9798350374315

This paper delves into an innovative image recognition algorithm that merges deep learning techniques with Generative Adversarial Networks (GANs) and offers a comparative analysis against traditional image recognition methods. The primary objective of this study is to evaluate the benefits and future prospects of deep learning, with a particular focus on GANs, within the realm of image recognition. The paper begins by reviewing conventional image recognition approaches, highlighting classical algorithms such as SIFT and HOG, which are based on feature extraction, and their combination with classifiers like Support Vector Machines (SVM) and Random Forests. Following this, the paper outlines the fundamental principles, network architecture, and unique advantages of GANs in the context of image generation and recognition. To verify the effectiveness of GANs in image recognition tasks, a series of experiments were performed using several publicly available image datasets for both training and testing purposes. The findings reveal that GANs significantly outperform traditional methods, especially in the context of processing complex images, enhancing accuracy, and improving resistance to noise. GANs are particularly adept at capturing intricate, high-dimensional features and subtle details in images, leading to substantial improvements in recognition performance. Additionally, GANs demonstrate exceptional capabilities in addressing issues related to image noise, managing incomplete data, and generating high-quality visual content. © 2024 IEEE.

关键词： Generative adversarial networks

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Development Approaches of Cloud-Based System for Object Recognition on images 17

Development Approaches of Cloud-Based System for Object Reco...

引用

17th IEEE International conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024

作者： Bershchanskyi, Yevhen Klym, Halyna Lviv Polytechnic National University Department of Specialized Computer Systems Lviv Ukraine

ISBN: (纸本)9798331520564

Object recognition systems have become integral in various domains, enhancing automation and decision-making processes. This research focuses on developing a cloud-based system specifically designed for efficient object recognition in images. Leveraging the scalability and flexibility of cloud computing, the proposed system not only performs accurate object detection but also ensures secure and scalable image storage. Implemented on the Amazon Web Services (AWS) platform, the system integrates advanced AI algorithms with cloud-native technologies to optimize resource usage and automate workload management. Key contributions include the design of a robust architecture that balances computational loads, enhances recognition accuracy, and streamlines image processing. This study demonstrates the potential of cloud technologies in simplifying object recognition tasks while maintaining security and efficiency. © 2024 IEEE.

关键词： Cloud platforms

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A Comprehensive Survey of image Steganography 2

A Comprehensive Survey of Image Steganography

引用

2nd International conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Kalaiarasi, G. Sudharani, B. Jonnalagadda, Sharon Christiana Battula, Harsha Vardhan Sanagala, Bhavana Vignans Fdn Sci Technol & Res Dept ACSE Guntur Andhra Pradesh India

ISBN: (纸本)9798350391558;9798350379990

This research study analyzes the multidimensional landscape of steganography, examining its historical roots, theoretical background, contemporary approaches, and various applications. Beginning with a historical overview, this study investigates the evolution of steganography from its ancient roots to its present iterations in the digital world. Next, the study progresses towards analyzing the fundamental principles and theoretical frameworks that underpin steganographic systems, such as cryptography and digital signal processing. Finally, this study presents a thorough evaluation of contemporary steganographic technologies, which range from simple LSB (Least Significant Bit) substitution techniques to advanced adaptive algorithms and machine learning methods by including deep-learning based steganography and coverless steganography. Notably, this study identifies key challenges, including detection resistance, payload capacity, and robustness against attacks. Overall, this study presents a thorough understanding of steganography, emphasizing its significance as a versatile tool for communication in the digital era, while also highlighting the challenges that pave way for future innovations.

关键词： image steganography Information hiding Convolutional Neural Network Steganography (CNN) Generative Adversarial Network Steganography (GAN) Stochastic computing

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

引用

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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Security Risk Assessment of image Classification Model Based on ANP-TOPSIS

Security Risk Assessment of Image Classification Model Based...

引用

2023 International conference on Networking and Network Applications, NaNA 2023

作者： Zhang, Zhiheng Liu, Zeyu Yang, Li Zhe, Tingbo Wu, Jian School of Computer Science and Technology Xidian University Xi'an China Guiyang Aviation Motor Co. Ltd Guizhou China

ISBN: (纸本)9798350327380

Recently, machine learning algorithms have been widely used in the fields of image processing, network security and natural language processing, etc., profoundly affecting human life. However, machine learning algorithms have the characteristics of uncertain output, vulnerability to adversarial attacks, and unexplained decision-making processes, which seriously threaten the security of machine learning-based face recognition, Malware detection, and autonomous driving. Hence, it is imperative for the security practitioners to evaluate algorithm security to ensure that security needs are met. In this article, the authors propose a set of security assessment index systems and methods for machine learning algorithms for image classification scenarios: Refer to the security specification of machine learning algorithms and requirements to construct the security index system of image classification model. Furthermore, The Analytic Network Process(ANP) is applied to quantify the index weights and the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) is applied to screen the optimal model, and finally the sensitivity analysis is applied to prove the stability of the proposed method. Experimental results show that this method has certain value and effect in assessing the security and model screening of image classification models. © 2023 IEEE.

关键词： image classification

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Accelerated Event-Based Feature Detection and Compression for Surveillance Video systems 24

Accelerated Event-Based Feature Detection and Compression fo...

引用

15th ACM Multimedia systems conference (ACM MMSys)

作者： Freeman, Andrew C. Mayer-Patel, Ketan Singh, Montek Univ North Carolina Chapel Hill NC 27515 USA

ISBN: (纸本)9798400704123

The strong temporal consistency of surveillance video enables compelling compression performance with traditional methods, but downstream vision applications operate on decoded image frames with a high data rate. Since it is not straightforward for applications to extract information on temporal redundancy from the compressed video representations, we propose a novel system which conveys temporal redundancy within a sparse decompressed representation. We leverage a video representation framework called AD Delta ER to transcode framed videos to sparse, asynchronous intensity samples. We introduce mechanisms for content adaptation, lossy compression, and asynchronous forms of classical vision algorithms. We evaluate our system on the VIRAT surveillance video dataset, and we show a median 43.7% speed improvement in FAST feature detection compared to OpenCV. We run the same algorithm as OpenCV, but only process pixels that receive new asynchronous events, rather than process every pixel in an image frame. Our work paves the way for upcoming neuromorphic sensors and is amenable to future applications with spiking neural networks.

关键词： event representation event video video processing event vision

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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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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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Research and application of active monitoring technology for power transmission and transformation construction safety in complex environments 9

Research and application of active monitoring technology for...

引用

9th International conference on Energy System, Electricity, and Power, ESEP 2024

作者： Lin, Zhikan Li, Wenxin Jin, Di Liu, Yuzheng Zhou, Guosheng Chen, Wei Lin, Yujie Lin, Cheng Wu, Jinshan Lin, Junjie Qiu, Peng State Grid Fujian Electric Power Co. Ltd. Construction Branch Fujian Building A No.9 Qinyuan Branch Road Jin'an District Fujian Province Fuzhou City China

ISBN: (纸本)9781510691728

Monitoring systems at power transmission and transformation construction sites often face challenges from complex environments such as haze, low lighting, and strong winds, leading to degraded video quality and stability. To enhance the reliability of these monitoring systems, this study employs three algorithms: image dehazing, low-light enhancement, and wind-induced blur removal. These algorithms are integrated through adaptive weight adjustment and parallel processing. This integration strategy enables the monitoring system to select the optimal algorithm in real-time based on environmental conditions and maintain stable image quality under multiple adverse factors. Experimental results show that the image dehazing algorithm improves PSNR to 28.37 dB in hazy environments, the low-light enhancement algorithm achieves a PSNR of 25.34 dB at night, and the wind-induced blur removal algorithm maintains an SSIM of 0.903 at a wind speed of 4 m/s. Transmission tests in weak signal areas indicate that a compression ratio of 30: 1 to 40: 1 can effectively balance video quality and bandwidth requirements. The monitoring system that integrates multiple algorithms demonstrates good adaptability and robustness in complex environments, providing reliable technical support for safety monitoring at power transmission and transformation construction sites. © 2025 SPIE.

关键词： image compression

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Optimized Machine Learning for Medical Data Analysis and Disease Prediction

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

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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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