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

共 15616 条记录，以下是351-360 订阅

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vision Semantics image Captioner

Vision Semantics Image Captioner

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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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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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Research on Real-time Positioning and Control of Automatic Navigation Car Based on OpenMV vision System 24

Research on Real-time Positioning and Control of Automatic N...

引用

2024 International conference on Machine Learning, Pattern Recognition and Automation Engineering, MLPRAE 2024

作者： Liang, Zhitao Li, Chenye Ge, Zijie Qiu, Jiawen Qiu, Guangping Zhujiang College of South China Agricultural University China

ISBN: (纸本)9798400709876

With the rapid development of machine vision and embedded technology, real-time positioning technology is increasingly important in the field of automatic navigation. This study developed a real-time positioning and control system for an automatic navigation car utilizing the OpenMVH7Plus camera and STM32 microcontroller. The system utilizes the high-performance image processing capability of OpenMVH7Plus, combined with color recognition and distance measurement technology, to achieve real-time perception of the environment and precise navigation of the car. Additionally, the system adopts incremental and positional PID control algorithms to ensure stable driving and precise parking in various environments. Experimental results demonstrate that with optimized image processing algorithms and precise control strategies, the car can efficiently locate and track specific targets in complex environments. This paper details the design scheme, key technical implementations, and experimental evaluations of the system, verifying its feasibility and effectiveness. © 2024 ACM.

关键词： Three term control systems

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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...

引用

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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Underwater 3D measurement using sheet of light systems for nuclear fuel assemblies 4

Underwater 3D measurement using sheet of light systems for n...

引用

4th International conference on Advanced algorithms and Signal image processing, AASIP 2024

作者： Feng, Chuncheng Wang, Congzheng Zhang, Lingyi Gong, Wanqi Liu, Lei Feng, Chang Institute of Optics and Electronics Chinese Academy of Sciences Chengdu China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

As the demand for high-precision underwater measurements of nuclear fuel assemblies (NFA) continues to rise, there is a concurrent improvement in underwater optical measurement technology. The sheet of light, an optical measurement technology based on a straightforward principle, has progressively emerged as a focal point of research. However, the process of conducting high-accuracy measurements underwater presents significant challenges due to the impact of scattering, refraction, and intense radiation. This paper presents the development of a reflective underwater sheet of light system. It proposes an enhanced refraction correction method, utilizing ray tracing. Additionally, it suggests an improved center extraction method for underwater laser lines, based on the grayscale center of gravity, to mitigate random speckle interference. The comprehensive experimental results demonstrate that the system achieves an underwater measurement accuracy of 0.2mm to 0.4mm at a distance of 850mm from the NFA. These measurements confirm the effectiveness of the underwater measurement system and provide a methodical verification for the safety assessment of NFA. © 2024 SPIE.

关键词： Ray tracing

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Quantifying Stellar Energy Exchange: image Analysis with OpenCV and Machine Learning 5

Quantifying Stellar Energy Exchange: Image Analysis with Ope...

引用

5th IEEE International conference for Emerging Technology, INCET 2024

作者： Venkteswarulu, B. Kanagala, Chaitanya Snehitha Kakumanu, Naga Venkata Sai Mohana Raghavarapu, Laxmi Narasimha Charan Sai Peravali, Himavath Lakshmi visveswar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350361155

Stellar energy exchange, fundamental to the dynamics of celestial systems, remains a captivating yet challenging area of study in astronomy. This paper introduces an innovative methodology for quantifying stellar energy exchange through sophisticated image analysis techniques, harnessing the power of OpenCV and machine learning. Our approach integrates OpenCV's robust image processing capabilities with state-of-the-art machine learning algorithms to automate the intricate task of analyzing astronomical images and predicting energy exchange rates within stellar constellations. The methodology begins with the preprocessing of astronomical images using OpenCV, where radiometric and atmospheric corrections are applied to ensure accurate representations of stellar luminance. Subsequently, features relevant to energy exchange dynamics are extracted from the processed images, employing advanced computer vision techniques. These features encapsulate intricate spatial, spectral, and radiometric characteristics of stars, providing rich representations for subsequent analysis. Machine learning algorithms are then employed to predict the energy exchange rates of stars based on the extracted features. Leveraging the power of convolutional neural networks and ensemble learning techniques, our model learns intricate patterns within the data and delivers accurate estimations of energy exchange dynamics. Through extensive experimentation on real astronomical datasets, our approach demonstrates exceptional performance, achieving precise predictions of energy exchange rates across diverse stellar systems. © 2024 IEEE.

关键词： Spectrum analysis

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Split Computing based Quantized PoseNet Model for Distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

引用

15th International conference on Communication systems and Networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, Internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep Neural Network (DNN) models on them. To address this, we introduce Split Computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called Distributed Artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. The QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. The result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling image edge detection Computer architecture Bandwidth Inference algorithms Partitioning algorithms

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

引用

5th IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-Time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 IEEE.

关键词： Temperature sensors

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Research on Traditional Art image Reconstruction Method Based on Computer vision Analysis 2

Research on Traditional Art Image Reconstruction Method Base...

引用

2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Supriya, M. Anvesh, Kuncham Muruganandham, B. Manasa Krishnan, K. Muthu, R. Murugan, K. Kpr Institute of Engineering and Technology Department of Ece India

ISBN: (纸本)9798350369960

In the framework of the digital era, the technology of image processing is one of the technologies that is being used increasingly often in all aspects of modern life. image correction may be handled using algorithms based on computer vision, which can increase the correction's influence on the image as a whole. The conventional method of identifying structural systems collects data via the use of wired or wireless measuring systems. The process of establishing sensors and the equipment used for data collection is laborious and takes a lot of time. The process has two stages: the first is the extraction of the line drawing, and the second is the rendering of the line drawing. An edge detection approach that integrates low-level and middle-level visual information is used to extract the line drawing in the process of on-line drawing extraction. This method is used to extract the drawing. The edges are then simplified and linked in accordance with the computer vision theory. Additionally, the image processing technology is used as the research object in order to develop a scientific model for the distorted image. These steps are based on the findings of traditional edge detection. image restoration can be helped along by filtering samples of the image and getting rid of any noise that is there. The fundamentals of computer vision and the technologies that are connected to it are broken down in detail, and the behavioral patterns of visual interaction are investigated. The image of Chinese traditional art serves both the role of art communication and as a medium and symbol for cultural communication. At the same time, it carries on the legacy of a fine traditional culture that has been passed down from generation to generation in China. The image processing technology that is based on computer vision algorithms provides a bigger number of benefits, in addition to a better degree of accuracy. © 2023 IEEE.

关键词： Computer vision feedback image reconstruction Traditional art visual analysis

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An In-Depth Study and Practical Deployment of Index Modulation Techniques in Orthogonal Frequency Division Multiplexing systems 2

An In-Depth Study and Practical Deployment of Index Modulati...

引用

2nd IEEE International conference on image processing and Computer Applications, ICIPCA 2024

作者： Wang, Zhengzhuo United Kingdom

ISBN: (纸本)9798350360240

This paper provides an insightful overview of Orthogonal Frequency Division Multiplexing (OFDM) technology, starting with a detailed explanation of its underlying principles. OFDM is a sophisticated technique that splits a single data stream across multiple separate subcarriers, thereby increasing spectral efficiency and combating multi-path distortion. It significantly improves the data transmission rate and quality, making it an essential element in various modern communication systems. A comparative analysis is then presented to distinguish OFDM from other multiplexing technologies. The unique advantages of OFDM, including its robustness to channel fading, high spectral efficiency, and the ability to handle high data rates, make it a compelling choice for digital data transmission. The paper further delves into the applications of OFDM in real-world scenarios, demonstrating its prevalence in diverse fields such as digital television and radio broadcasting, wireless networking, and 4G/5G mobile communications. These examples illustrate the integral role of OFDM in powering our connected world. However, despite its advantages, OFDM is not without limitations. The paper elucidates issues such as high Peak-to-Average Power Ratio (PAPR), sensitivity to frequency offset and phase noise, and the necessity for complex receiver algorithms. Overcoming these challenges is a key area of ongoing research. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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