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

共 12948 条记录，以下是301-310 订阅

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Role, Significance, and Impact of Method Noise in Multi-Modal Medical image Fusion 5

Role, Significance, and Impact of Method Noise in Multi-Moda...

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5th International conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Bhosekar, Shailesh Singh, Prabhishek Garg, Deepak Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798331522100

Multi modal medical image fusion in, for instance, combining information from computed tomography (CT) scans, positron emission tomography (PET) and magnetic resonance imaging (MRI) into one single dataset, improves the accuracy of diagnostics due to atomistic and functional details of the images. In this paper, the role, importance, and effect of method noise in multi-modal medical image fusion, is considered to assess and improve fusion algorithms. The effectiveness of an algorithm can be determined in terms of its efficacy at preserving salient image content and removing unwanted artifacts while maintaining the former, which is defined by method noise, the residual noise after image processing. In this paper, I review the theoretical basis of method noise, discuss its function in both fusion and denoising processes, and discuss its practical and conceptual implications, showing its importance for quality assessment and algorithm refinement. We apply method noise to the Non-Subsampled Shearlet Transform (NSST) to demonstrate application in multi-modal medical image fusion. Comparative experiments using NSST based fusion with /without method noise demonstrate significantly different visual clarity and quantitative metrics as measured with different metrics. Results show that method noise both maintains image integrity by preventing detail loss and aids in the integration of complementary detail from different modalities. The need for method noise in advancing image fusion techniques is emphasized in this paper, and a framework for future research and application of fusion in diagnostic imaging is presented. © 2025 IEEE.

关键词： Positron emission tomography

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The Major Challenge in Improving image Dark Channel Defogging Algorithm 23

The Major Challenge in Improving Image Dark Channel Defoggin...

引用

6th International conference on Information Science and systems, ICISS 2023

作者： Samonte, Mary Jane C. Wang, Rongfu Li, Shuhua School of IT Mapua University Manila Philippines

ISBN: (纸本)9798400708206

With the rapid development of computer vision and image processing technology, scene image processing under particular weather conditions has become an important research direction, especially in foggy conditions of the image, which is easily affected by haze, resulting in unclear picture quality and image information loss. The defogging algorithm came into being to solve this kind of image problem. This paper analyzes the advantages and disadvantages of dark channels and histogram algorithms. Given the time and space complexity and incomplete fog removal of the dark channel and histogram algorithm, a combined improved algorithm is proposed, a fast fog removal algorithm combining dark channel and histogram enhancement. The analysis of experimental results shows that the fast-defogging algorithm combined with dark channel and histogram enhancement proposed in this paper can restore better visual effects and preserve edge details. Especially in the distant image area, image details are easier to obtain, and the time and space complexity of the algorithm is reduced. © 2023 ACM.

关键词： image enhancement

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Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR image Fusion 56

Convolutional Simultaneous Sparse Approximation with Applica...

引用

56th Asilomar conference on Signals, systems, and Computers

作者： Veshki, Farshad G. Vorobyov, Sergiy A. Aalto Univ Dept Signal Proc & Acoust Espoo Finland

ISBN: (纸本)9781665459068

Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports. The SSA model has been used in various signal and image processing applications involving multiple correlated input signals. In this paper, we propose algorithms for convolutional SSA (CSSA) based on the alternating direction method of multipliers. Specifically, we address the CSSA problem with different sparsity structures and the convolutional feature learning problem in multimodal data/signals based on the SSA model. We evaluate the proposed algorithms by applying them to multimodal and multifocus image fusion problems.

关键词： Simultaneous sparse approximation convolutional sparse coding dictionary learning image fusion

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Error Analysis of a Multi-Sensor Maritime Targeting System 20

Error Analysis of a Multi-Sensor Maritime Targeting System

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conference on Electro-Optical and Infrared systems - Technology and Applications XX

作者： Hickman, Duncan L. Niebla, Maria Sang, Daekyu Kim, Boomin Baek, Jeonghyun Tektonex Ltd Long View Argyll Rd Kilcreggan Argyll & Bute Scotland Agcy Def Dev 160 Bugyuseong Dero 488beon Gil Daejeon South Korea

ISBN: (数字)9781510667044

ISBN: (纸本)9781510667037

Targeting systems are subject to multiple sources of error when operating in complex environments. To reduce the effect of these errors, modern targeting systems generally include both imaging and RF sensors. Data processing then provides target detection and classification information, and the detection streams are combined using a data fusion scheme to produce an optimal target location estimate with an associated latency. In this paper, the performance of a multi- sensor system in a maritime application is investigated using a mathematical simulator that has been developed to provide the system performance error analysis for different engagement scenarios and test conditions. This simulator is described together with the sources of targeting error such as image motion blur and radar glint. Additionally, the impact of flare and chaff countermeasures on the targeting performance is reviewed in terms of different types of target recognition and tracking algorithms.

关键词： Multisensor systems Error Analysis image Blur Radar Glint Model Validation Countermeasure Model ATR processing Targeting systems

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Traffic Sign Recognition by image Preprocessing and Deep Learning 15th

Traffic Sign Recognition by Image Preprocessing and Deep Lea...

引用

15th International conference on Intelligent Human-Computer Interaction (IHCI)

作者： Khamdamov, U. R. Umarov, M. A. Khalilov, S. P. Kayumov, A. A. Abidova, F. Sh. Univ Management & Future Technol Tashkent Uzbekistan Tashkent Univ Informat Technol Samarkand Branch Tashkent Uzbekistan Tashkent Univ Informat Technol Tashkent Uzbekistan

ISBN: (纸本)9783031538292;9783031538308

Due to the improvement in the car manifacture, the rate of road traffic accidents is increasing. To solve these problems, there is loads of attention in research on the development of driver assistance systems, where the main innovation is traffic sign recognition (TSR). In this article, a special convolutional neural network model with high accuracy compared to traditional models is used for TSR. The Uzbek Traffic Sign Dataset (UTSD) applied in the zone of Uzbekistan was created, consisting of 21.923 images belonging to 56 classes. We proposed a parallel computing method for real-time processing of video haze removal. Our utilization can process the 1920 x 1080 video series with 176 frames per second for the dark channel prior (DCP) algorithm. 8.94 times reduction of calculation time compared to the Central processing Unit (CPU) was achieved by performing the TSR process on the Graphics processing Unit (GPU). The algorithms used to detect traffic signs are improved YOLOv5. The results showed a 3.9% increase in accuracy.

关键词： Traffic sign recognition deep learning image dehazing UTSD dataset parallel processing data augmentation

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image Preprocessing to Improve Object Recognition in Complex Weather Conditions

Image Preprocessing to Improve Object Recognition in Complex...

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2023 International Russian Smart Industry conference, SmartIndustryCon 2023

作者： Schegolikhin, Yaroslav Mitrokhin, Maxim Eremin, Alexandr Penza State University Penza Russia

ISBN: (纸本)9781665464291

Object recognition systems on images allow to automate of various routine processes. Vehicle detection systems are becoming widespread and are being incorporated into, "smart city"initiatives, and traffic control systems. Some applications of such systems are related to the impact of precipitation and other atmospheric phenomena that degrade image quality. The deterioration of image characteristics leads to a decrease in the efficiency of the entire automated system. The research issue is to develop a software solution to improve the efficiency of vehicle detection on images captured in difficult weather conditions. The neural network detector YOLOv5, trained on images collected by the authors from open sources, was used as an algorithm for the detection of cars on the images. Contrast correction and neural network methods were used as image enhancement algorithms. The performance of the YOLOv5 detector was compared on a test base of images preprocessed by image enhancement methods. It was found that the algorithms chosen for the study were indeed able to improve image quality by partially removing interference. Generative neural networks can introduce visual distortions and initially missing elements in images, so their application within the task's framework is very limited. image processing methods based on contrast correction allow for the improvement of vehicle detector performance on images with interference, but their application negatively affects performance on images without interference. On average, the performance of the detector decreases insignificantly when comparing images with and without interference. © 2023 IEEE.

关键词： Efficiency

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Vision Semantics image Captioner

Vision Semantics Image Captioner

引用

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

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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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Cancer Sight: Illuminating the Hidden-Advancing Breast Cancer Detection with Machine Learning-Based image processing Techniques

Cancer Sight: Illuminating the Hidden-Advancing Breast Cance...

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

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

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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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