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

共 14919 条记录，以下是221-230 订阅

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Automatic Number Plate Recognition System Using Deep Learning algorithms and image processing for Surveillance 9

Automatic Number Plate Recognition System Using Deep Learnin...

引用

9th International conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Jeba Malar, A. Christy Deepika, S. Jayasree, S. Keerthana, G. Sri Krishna College of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350365092

The increase in amount of vehicles in the past few years have made traffic management a difficult job. Technologies play an important role in these systems to regulate the traffic. Number plates are distinguished by their shape, bulk, and color in different countries. Number plates must adhere to rules in India, where a light hue serves as the background and a black color serves as the foreground. This paper recommended a new technique to locate the number plate on a vehicle by using histogram equalization and then identifying the alphabets and digits on the plates using character segmentation using Sobel and Canny edge detection image processing technique. © 2024 IEEE.

关键词： Deep learning

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Vision Based Runway Identification with marked or unmarked terrain for Automatic Landing Applications of UAV 8

Vision Based Runway Identification with marked or unmarked t...

引用

8th conference on Advances in Control and Optimization of Dynamical systems (ACODS)

作者： Tripathi, Amit Kumar Patel, Vijay V. Padhi, Radhakant Aeronaut Dev Agcy Bangalore Karnataka India Indian Inst Sci Fac Aerosp Engn Dept Bangalore Karnataka India

Vision based runway identification using 'marked or unmarked terrain' image sequences captured from a fixed wing unmanned aerial vehicle through onboard stereovision sensor is presented in this paper. An innovative convolutional neural netwok (CNN) based YOLO-V8 object detection algorithm is used to detect the runway during approach segment of UAV. This deep learning algorithm detects the region of interest in real time and in a computationally efficient manner. The captured unknown road segment or runway image frames are processed and examined for width, length, level and smoothness aspects to qualify as a suitable runway for UAV landings. Also, it is ensured that there are no obstacles, patches or holes on the detected road or runway. Runway start and end threshold lines and regions, touchdown point and runway edge lines are considered as the region of interest. image processing algorithms are applied on the captured runway or road images to detect strong features in the region of interest. Feature detector based image processing algorithm with stereo vision constraint is used to establish the relation between unmanned aerial vehicle's center of gravity and detected runway feature points image processing algorithms like hough line detection, RANSAC, Oriented FAST and Rotated BRIEF (ORB), median filters, morphological methods are applied to extract terrain features. Based on the detected runway orientation and position with respect to UAV position. An automatic landing manoeuvre is performed by UAV autopilot to land the UAV on intended touchdown point on runway computed through detected feature points.

关键词： image processing Runway Identification Feature detection Region of Interest threshold lines touch down point YOLOv8 ORB CNN runway width slant distance

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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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Understanding and Tackling Scattering and Reflective Flare for Mobile Camera systems 24

Understanding and Tackling Scattering and Reflective Flare f...

引用

32nd ACM International conference on Multimedia, MM 2024

作者： Lan, Fengbo Chen, Chang Wen The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798400706868

The rise of mobile devices has spurred advancements in camera technology and image quality. However, mobile photography still faces issues like scattering and reflective flares. While previous research has acknowledged the negative impact of the mobile devices' internal image signal processing pipeline (ISP) on image quality, the specific ISP operations that hinder flare removal have not been fully identified. In addition, current solutions only partially address ISP-related deterioration due to a lack of comprehensive raw image datasets for flare study. To bridge these research gaps, we introduce a new raw image dataset tailored for mobile camera systems, focusing on eliminating flare. This dataset encompasses over 2,000 high-quality, full-resolution raw image pairs for scattering flare, and 1,200 for reflective flare, captured across various real-world scenarios, mobile devices, and camera settings. It is designed to enhance the generalizability of flare removal algorithms across a wide spectrum of conditions. Through detailed experiments, we have identified that ISP operations, such as denoising, compression, and sharpening, may either improve or obstruct flare removal, offering critical insights into optimizing ISP configurations for better flare mitigation. Our dataset is poised to advance the understanding of flare-related challenges, enabling more precise incorporation of flare removal steps into the ISP. Ultimately, this work paves the way for significant improvements in mobile image quality, benefiting both enthusiasts and professional mobile photographers alike. © 2024 ACM.

关键词： Pipeline processing systems

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Designing a User-Friendly and Responsive AI based image Generation Website and Performing Diversity Assessment of the Generated images 4

Designing a User-Friendly and Responsive AI based Image Gene...

引用

4th International conference on Electronics and Sustainable Communication systems, ICESC 2023

作者： Kanakia, Harshil T. Nair, Suraj Prakkash Sardar Patel Institute of Technology Master of Computer Application Department Mumbai India

ISBN: (纸本)9798350300093

This research paper presents a website that leverages the power of the image GPT engine for image generation. The website allows users to input a textual prompt and generate a corresponding image using image GPT's natural language processing and machine learning capabilities. The paper details the architecture of the website, the image GPT API integration, and the algorithms used for image generation. Additionally, we present the results and evaluate its quality and discuss potential applications of this technology in various industries such as advertising, art and design. We also discuss how the performance of image generation can be potentially improved. Overall, the website demonstrates the potential of combining natural language processing and machine learning for image generation and opens up new avenues for future research in this field. © 2023 IEEE.

关键词： Engines

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Predicting Liver Tumor: Leveraging image processing with DenseNet121 3

Predicting Liver Tumor: Leveraging Image Processing with Den...

引用

International conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Sandhiya, B. Selvan, Anbu Gowtham, R. N. Jain Deemed Be Univ Sch Comp Sci & Informat Technol Bengaluru India Easwari Engn Coll Dept Informat Technol Chennai Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Early detection and accurate prediction of liver disease play a crucial role in improving patient outcomes and reducing the burden on healthcare systems. Segmenting the liver and its tumors using computed tomography (CT) images is an essential undertaking for the diagnosis and treatment of liver illnesses. Because of the uneven distribution, hazy boundaries, varied densities, forms, and sizes of lesions, segmenting the liver and associated tumor is a challenging task. Up until this point, our primary focus has been on developing deep learning algorithms that can separate the liver and its tumor from CT scan pictures of the abdomen, saving time and effort when diagnosing liver illnesses. A deep learning-based automatic segmentation method is presented that uses the improved densenet121 model to segment the liver and its tumor. In this model image processing is used for the accurate automated segmentation of tumors, the proposed method demonstrates the ability to accurately segment the liver as well, as indicated by the confusion matrix obtained when comparing to the previous work on liver and tumor segmentation. The Densenet121 architecture serves as the foundation for the algorithm employed here, we introduced an autonomous technique to segment the liver from CT scans and lesions from the segmented liver region, based on semantic segmentation convolutional neural networks ( CNNs). For liver and tumor segmentations, respectively, the suggested system achieves an accuracy of 95.31% to 95.39%.

关键词： Convolutional neural network Deep learning image processing Densenet121 Confusion matrix

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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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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 Comprehensive Survey of image Steganography 2

A Comprehensive Survey of Image Steganography

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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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