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

共 12871 条记录，以下是101-110 订阅

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High-Dimensional Data processing Using Quantum-Inspired Evolutionary algorithms for Homeland Security Imaging systems

High-Dimensional Data Processing Using Quantum-Inspired Evol...

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2024 International conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Alekhya, V. Reddy, N. V. Uma Singh, Jay Boddu, Bhasker Sobti, Rajeev Hameed, Ali Abduhussien Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Artificial Intelligence And Machine Learning Bangalore India Gl Bajaj Institute Of Technology & Management U.P. Greater Noida India Mlr Institute Of Technology Department Of Information Technology Telangana Hyderabad India Lovely Professional University Phagwara India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

This research study explores the emerging area of quantum-inspired evolutionary algorithms (QIEAs) applied to high-dimensional data processing, with a focus on homeland security imaging systems. This work attempts to close the paradigm gap in image processing methodologies caused by the growing complexity of security threats. By combining the adaptive processes of evolutionary algorithms with the probabilistic reasoning inherent in quantum computing, the proposed technique creates a potent tool for managing the inherent difficulties of high-dimensional data. The main contribution of this study is the creation of a brand-new QIEA framework that is specifically designed for image analysis in security contexts and exhibits higher accuracy and efficiency than traditional techniques. The quantum bit representation and quantum gate operations, which have been cleverly tailored to the evolutionary algorithm structure, provide the methodological basis, and improve the search capacity in multidimensional data fields. An unparalleled degree of clarity and detail in security photography is made possible by this fusion, which is essential for threat identification and prevention. Experiments conducted on a variety of difficult datasets show that the suggested method is reliable for identifying important characteristics in complicated photos, which is a crucial component of homeland security applications. Beyond only addressing short-term security issues, this study establishes a standard for next investigations into quantum-inspired computing for image processing. © 2024 IEEE.

关键词： Imaging systems

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Segmentation-based low-resolution spatial tracker for video 34

Segmentation-based low-resolution spatial tracker for video

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conference on Automatic Target Recognition XXXIV

作者： Benyamin, Minas Goldman, Geoffrey H. DEVCOM Army Res Lab 2800 Powder Mill Rd Adelphi MD 20783 USA

ISBN: (纸本)9781510673977;9781510673960

Low-resolution image object recognition and tracking is often required for battlefield reconnaissance. For high-cost military systems, standard signal processing techniques can be used by tracking systems, however, low-cost systems require simpler approaches. We developed a fast detector-agnostic tracker for improving situational awareness using electro-optical video data. Our approach uses low computational techniques such as YOLO, match filters, and shape transforms to segment objects of interest in an image. From two or more successive detections, we initialize an alphabeta filter that predicts the location of the target of interest in the image. Next, we segment subsequent frames to a search area around the predicted region. This increases the sensitivity of the detector by improving the average signal-to-noise ratio and it also decreases the false alarm rate. The reduction in the size of the processing area can improve the detection speed per frame by an order of magnitude relative to a full-sized frame. By using algorithms with input from variablesize object a such as YOLO, this algorithm can be adapted to track virtually any object captured in a video.

关键词： Tracking Detection YOLO Hough Transform image processing Electro-optical Alpha-Beta Filter Low Resolution Classification

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Exploring the Potential of Deep Learning algorithms in Medical image processing: A Comprehensive Analysis 5th

Exploring the Potential of Deep Learning Algorithms in Medic...

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5th International conference on Communication and Intelligent systems, ICCIS 2023

作者： Pal, Ganesh Prasad Pal, Raju Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering School of Information and Communication Technology Gautam Buddha University Greater Noida India

ISBN: (纸本)9789819720781

In this study, we comprehensively examine the potential of deep learning algorithms in the domain of medical image processing. Through a systematic analysis of existing literature, we explore the applications, methodologies, and outcomes of these algorithms across diverse medical specialties. Our analysis underscores the transformative impact of convolutional neural networks and transfer learning techniques in enhancing diagnostic accuracy, image segmentation, and disease detection. We discuss challenges such as data variability, ethics, and interpretability while emphasizing the importance of interdisciplinary collaboration. By synthesizing findings and identifying future directions, we highlight the promising role of deep learning in revolutionizing healthcare diagnostics and treatment planning. After conducting a thorough analysis of various literature and empirical studies, we have evaluated the capabilities and drawbacks of deep learning models in managing different medical imaging modalities such as X-rays, MRIs, and CT scans. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Estimating the Feasibility of images Simulation in Unity 3D for Sub-Pixel processing algorithms

Estimating the Feasibility of Images Simulation in Unity 3D ...

引用

2023 Wave Electronics and its Application in Information and Telecommunication systems, WECONF 2023

作者： Sivov, N. Yu. Shmatko, E.V. Poroykov, A. Yu. National Research University MPEI Department of Physics Named after V.A. Fabrikant Moscow Russia

ISBN: (纸本)9798350348293

Computer image simulation is used in the development of optical measurement systems and processing algorithms. It allows the performance of a system or algorithm to be verified and its measurement uncertainty to be assessed. Modern algorithms make it possible to achieve sub-pixel accuracy in digital image processing. An example of such algorithms is the cross-correlation processing in Particle image Velocimetry. Often, standard graphics engines such as Unity 3D and Unreal Engine are used to simulate the images. The purpose of this work is to evaluate the possibility of using these tools to generate images for further processing by subpixel resolution algorithms. This paper presents the results of a computational experiment to estimate the maximum spatial resolution for simulations in Unity 3D. A qualitative and quantitative analysis of the results confirms the possibility of using the tested graphics engine for the analysis of subpixel resolution algorithms for digital image processing. © 2023 IEEE.

关键词： Engines

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Hardware Implementation of image processing Morphological and Convolution Operations as SoC on FPGAs 24

Hardware Implementation of Image Processing Morphological an...

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7th International conference on Digital Medicine and image processing, DMIP 2024

作者： Moussa, Alfred M. Groves, Richard Rafla, Nader Electrical and Computer Engineering Department Boise State University BoiseID United States

ISBN: (纸本)9798400709586

Efficient image processing architectures are consistently in demand across a multitude of applications, particularly those customized for resource-constrained systems-on-chip (SoC). The increasing need for high-performance image processing in various sectors has driven the development of specialized architectures. However, deploying such architectures on platforms with limited resources, such as SoCs, poses significant challenges. Furthermore, the implementation of complex algorithms to handle large datasets using software solutions often leads to slower response times, prompting exploration into hardware implementations. Field-Programmable Gate Arrays (FPGAs) are becoming popular for hardware implementations because of their attributes: low latency, connectivity, parallel computing capabilities, and flexibility. Consequently, the utilization of FPGA-based implementations has resulted in faster and more efficient performance of unique architectures tailored to specific requirements. This paper presents a novel hardware/software co-design approach to implement erosion, dilation, and neighborhood image processing operations on the FPGA development board, "Zedboard". In this approach, the FPGA is programmed by connecting it to a PC via USB, facilitating the transfer of an image pixel by pixel. The pixels are temporarily stored in on-chip DDR and accessed through DMA (Direct Memory Access) until they are requested by an interrupt signal from the image processing IP, at which point they are moved to line buffers for faster processing. Once processed, the image is transmitted back to the PC via UART, facilitating pixel-by-pixel transfer for verification, where it is compared with a reference image generated using Python. This comparison confirms a 99.22% match between the processed image and the reference image, with the discrepancy occurring at the image's edges due to initial padding. Additionally, the time required to process the entire image was measured and displayed

关键词： System-on-chip

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Maritime image Stabilization: A Comprehensive Review of Techniques and Challenges 9

Maritime Image Stabilization: A Comprehensive Review of Tech...

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9th International conference on Electronic Technology and Information Science (ICETIS)

作者： Wei, Enping Tan, Yong Chai Tai, Vin Cent Hao, Yanan Zhang, Xiaodong Zhang, Tian SEGI Univ Fac Engn Built Environm & Informat Technol Ctr Modelling & Simulat Kuala Lumpur Malaysia

ISBN: (纸本)9798350388350;9798350388343

image stabilization plays a crucial role in providing accurate and reliable visual information for machine vision applications. In maritime applications, such as unmanned ship navigation, where six degrees of freedom (DOF) motion and harsh maritime conditions prevail, the efficacy of image stabilization technology is vital for robust image processing algorithms. This paper offers a comprehensive review of image stabilization techniques tailored for maritime environments, developed over the past two decades. We analyzed a total of 39 research articles on the subject, sourced from Web-of-Science, SCOPUS, and the Engineering Index databases, discussing potential research directions to address the limitations of current image stabilization methods, with special consideration for the unique requirements of ship-borne cameras. It provides an up-to-date overview of the techniques, limitations, and algorithms of ship-borne cameras for maritime applications, identifying current knowledge gaps and areas requiring further research. This review aims to guide the development of new technologies and methods to improve the performance of image stabilization systems in maritime contexts.

关键词： image Stabilization Assessment Application Maritime Environment Ship-borne Camera

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Research on distribution inspection planning strategy based on artificial intelligence image recognition 24

Research on distribution inspection planning strategy based ...

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1st International conference on image processing Machine Learning and Pattern Recognition

作者： Gao, Fengxi Wu, Zhuohang Han, Zhentao Wang, Qixiang He, Xin Sun, Yan Zhao, Dewei Zhao, Wengang Sun, Mingze State Grid Liaoning Elect Power Compant Bcon Res Inst Shenyang Liaoning Peoples R China

ISBN: (纸本)9798400707032

In order to actively promote the construction of new power systems and the digital transformation and upgrading of power grid companies, and explore the application of artificial intelligence technology, the power grid company has built artificial intelligence "two libraries and one platform", that is, artificial intelligence platform, sample library, model library, and has carried out a lot of work in the application of artificial intelligence technology in equipment management, safe operation, marketing customer service and other business fields, and has precipitated rich samples, algorithms, and model achievements. With the increasing demand for intelligent application services and the increasing demand for samples and models in various business departments, it is urgent to introduce capabilities such as intelligent identification of electrical components and defect detection in scarce scenes on top of the existing artificial intelligence basic support capabilities to realize model training, iteration, optimization and improvement of accuracy, and deeply integrate the basic capabilities of artificial intelligence with the core business applications of the power grid. Further improve the ability to empower the profession and serve the grassroots.

关键词： New power systems Drone inspection Artificial intelligence Defect detection

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Classification of Philippine Soybean Variety Using image processing Technique and Machine Learning Method

Classification of Philippine Soybean Variety Using Image Pro...

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IEEE International conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Diamante, John Christian Dioses, Isaac Angelo M. Dioses, Jesusimo L., Jr. Tomas, John Paul Q. Mapua Univ Sch Informat Technol Makati Metro Manila Philippines Isabela State Univ Coll Comp Studies Informat & Commun Technol San Fabian Echague Isabela Philippines

ISBN: (纸本)9798350372113;9798350372106

Together with dairy and wheat, soybeans are a major agricultural import into the Philippines. Historically, imports accounted for 99% of the country's supply of soybeans from 1995 to 2014, with local manufacturers making up the remaining 1%. The emergence of novel technologies has enabled the classification of diverse agricultural commodities, such as soybean cultivars, by merging computer vision and machine learning methodologies that utilize edge detection algorithms. Precise categorization of seed variants is essential for farmers and seed manufacturers to maintain variety purity, which in turn affects crop productivity and the quality of soybeans provided to nearby retailers. Morphological features were retrieved from pre-processed soybean pictures using the regionprops function, which made use of edge detection methods. After the extraction of features, the data was subjected to pre-processing and machine learning analysis. The KNearest Neighbors (KNN) model classified the data using Euclidean distance. A 75:25 split of the dataset was made into training and testing subsets, with five neighbors being used for categorization. The CL1 and PSB SY2 soybean varieties were classified by the KNN model with an accuracy rate of 85%, indicating a first step in variety classification research.

关键词： machine learning KNN MATLAB image processing edge detection canny

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Robotic Endoscopic for Smart Diagnosis 3

Robotic Endoscopic for Smart Diagnosis

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3rd IEEE International conference on Intelligent Techniques in Control, Optimization and Signal processing, INCOS 2024

作者： Radha, P. Bavithraa, K.P. Mepco Schlenk Engineering College Mca Department Sivakasi India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361186

Developing healthcare systems for disease diagnosis and treatment require medical image segmentation as a vital prerequisite. U-Net, which is a U-shaped architecture, has become the standard in the segmentation of intestine images and has achieved remarkable success. The proposed approach is a novel medical image segmentation method that uses the TransUNet architecture, which combines both Transformers and U-Net, as an effective alternative. The system presents a review of the latest developments in robotic endoscopic systems, including their design, control algorithms, and clinical applications. The Transformer encodes tokenized image patches from a Convolutional Neural Network (CNN) feature map to extract global contexts, while the decoder up samples the encoded features and combines them with high-resolution CNN feature maps to allow precise localization. The Transformers serve as robust encoders for medical image segmentation tasks, while the combination with U-Net improves the recovery of localized spatial information and enhances finer details © 2024 IEEE.

关键词： image segmentation

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From linear to nonlinear unfolded Condat-Vũ algorithm for spectro-polarimetric hight-constrast image recovery 32

From linear to nonlinear unfolded Condat-Vũ algorithm for s...

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32nd European Signal processing conference, EUSIPCO 2024

作者： Chappon, E. Pustelnik, N. Tachella, J. Denneulin, L. Ferrari, A. Langlois, M. Laboratoire de Physique ENSL CNRS UMR 5672 LyonF-69342 France Laboratoire de Recherche de l’EPITA EPITA Le Kremlin-Bicêtre94270 France Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Lab. J.-L. Lagrange France Univ Lyon Univ. Lyon 1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-Laval69230 France

ISBN: (纸本)9789464593617

Studying circumstellar environments is crucial for understanding exoplanets and stellar systems. Instruments like SPHERE can extract information about these environments by leveraging advanced image reconstruction methods, possibly based on deep learning. This work focuses on unfolded proximal neural networks based on Condat-Vũ iterations and proposes a new nonlinear formulation. To evaluate and compare the performance of the proposed reconstruction strategies, two datasets dedicated to circumstellar environments analysis in the context of high-contrast imagery have been created offering different level of complexity in the evaluation of the performance. © 2024 European Signal processing conference, EUSIPCO. All rights reserved.

关键词： deep learning High constrast image recovery nonlinear algorithms proximal algorithms unfolded algorithms

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