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

共 14697 条记录，以下是1221-1230 订阅

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Research on Parallel Computation and Memory Optimization of Multi-Core Embedded DSP Technology in High Precision Missile Terminal Guidance image Tracking Algorithm

Research on Parallel Computation and Memory Optimization of ...

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IEEE International conference on Power, Intelligent Computing and systems (ICPICS)

作者： Fuming Xia Bo Jing Mingge Huo Xiaoxuan Jiao Shenglong Wang Jinxin Pan School of Aeronautical Engineering Air Force Engineering University Xi'an China Beijing Electro-Mechanical Engineering Institute Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

This paper mainly discusses how to effectively use multi-core embedded digital signal processor (DSP) technology to realize parallel computation of image tracking algorithm and memory optimization in high precision missile terminal guidance system. With the increasing requirement of missile strike accuracy in modern war, the vision-based terminal guidance technology has been widely concerned because of its strong autonomy and anti-jamming ability. However, such image processing algorithms are usually highly computation-intensive and data dependent, which requires high real-time and hardware resources. In this paper, the key steps of image tracking algorithm are analyzed and reconstructed deeply, and the architecture suitable for parallel processing of multi-core DSP is designed by modularization. The parallel computing advantages of multi-core DSP are given full play through task division and scheduling strategies to improve the execution efficiency and real-time performance of the algorithm. Secondly, the research also focuses on memory optimization, including the design of data cache strategy, reducing data redundancy, improving memory access locality, etc., and strives to maximize the data reading speed and reduce the memory bandwidth pressure under the condition of limited embedded system resources. To sum up, this research is committed to building an efficient and stable multi-core embedded DSP environment, parallel computing model and memory optimization scheme for high-precision missile terminal guidance image tracking algorithm, which has important theoretical value and practical significance for improving the intelligence level and combat efficiency of missile terminal guidance system.

关键词： Missiles Processor scheduling Digital signal processors Redundancy Signal processing algorithms Parallel processing Real-time systems Scheduling Optimization image reconstruction

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Design and Optimization of a Student Management System Based on Blockchain Technology in District Computers

Design and Optimization of a Student Management System Based...

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image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Ying Jin Fushun Vocational Technical Institute (Fushun Teachers College) Fushun Liaoning China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

Computer blockchain technology provides a possible means to enhance data security, transparency, and overall efficiency. This study focuses on the design and optimization ideas of using blockchain technology in student management systems. The architecture plan for this system was developed during the design phase, emphasizing the use of smart contracts to automate registration, classification, and authentication processes, as well as decentralized data storage and encrypted hashing of irreversible records. License blockchain can be used to provide privacy protection and access restrictions, ensuring that student data is secure and accessible to authorized parties. The goal of optimizing the solution is to improve scalability and transaction speed, while reducing the amount of energy used in traditional blockchain systems. The improvement of consensus algorithms and second layer extension technologies such as sidechain or off chain protocols can achieve faster data processing without compromising security. In addition, metadata management and advanced encryption technologies have improved system performance and minimized data to the greatest extent possible.

关键词： System performance Scalability image processing Smart contracts Memory Metadata Licenses Blockchains Protection Optimization

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Imaging infrared seeker design and tests for FOK guided missile 34

Imaging infrared seeker design and tests for FOK guided miss...

引用

conference on Infrared Imaging systems - Design, Analysis, Modeling, and Testing XXXIV

作者： Leshchenko, Yelyzaveta Kaczmarek, Kacper Labaj, Filip Malecki, Szymon Miedzinski, Dariusz Sochacki, Mateusz Warsaw Univ Technol Inst Micromech & Photon Fac Mechatron Student Club Photon Engn SPIE & Opt Chapter Andrzeja Boboli 8 PL-02525 Warsaw Poland Warsaw Univ Technol Fac Power & Aeronaut Engn Students Space Assoc Nowowiejska 21-25 PL-00665 Warsaw Poland Warsaw Univ Technol Inst Microelect & Optoelect Doctoral Sch Koszykowa 75 PL-00662 Warsaw Poland Polish Armaments Grp SA Nowy Swiat 4 PL-00497 Warsaw Poland Warsaw Univ Technol Fac Power & Aeronaut Engn Inst Aeronaut & Appl Mech Div Mech Nowowiejska 24 PL-00665 Warsaw Poland Warsaw Univ Technol Fac Power & Aeronaut Engn Inst Aeronaut & Appl Mech Div Automat & Aeronaut Syst Nowowiejska 24 PL-00665 Warsaw Poland

ISBN: (纸本)9781510661806;9781510661813

The design, development and performance evaluation of an imaging infrared seeker system of the FOK missile will be presented in this paper. FOK is a guided missile iteratively developed since 2017 by the Students' Space Association at Warsaw University of Technology, designed as a testbed for the development of control algorithms. Throughout the years of development the design evolved, and currently assumes the incorporation of an electro-optical seeker, enabling the missile to actively search and track ground-based targets. An infrared detection module was chosen as the core sensing component of the system. The module consists of an uncooled 640x512 microbolometer detector array integrated with dedicated optics, providing a high-resolution image in the 8-12 mu m spectral range. Since the seeker will be mounted at the front of the missile, an IR-transparent nose cone dome was required as part of the optical system. Two dome variants were designed, made out of silicon and germanium, respectively. Optical parameters of the imaging system, composed of the dome - objective - microbolometer array were calculated and confirmed using ray-tracing software. The designed domes were optimized in order to minimize image quality degradation. Key parameters considered during the calculations included: optical aberrations, modulation transfer function - MTF and field of view - FOV. Software-in-the-loop tests with the use of in-house developed simulation software will be performed together with hardware-in-the-loop tests based on capturing real images obtained by the tested system installed under a UAV. The tests' goal was to check the performance of the developed guidance algorithms in an operational-like environment.

关键词： electro-optical seeker missile guidance guidance algorithm thermal imaging long-wave infrared infrared optics microbolometers focal plane array image processing

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Mixed-Block Neural Architecture Search for Medical image Segmentation

Mixed-Block Neural Architecture Search for Medical Image Seg...

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conference on Medical Imaging - image processing

作者： Bosma, Martijn M. A. Dushatskiy, Arkadiy Grewal, Monika Alderliesten, Tanja Bosman, Peter A. N. Ctr Wiskunde & Informat Sci Pk 123 NL-1098 XG Amsterdam Netherlands Delft Univ Technol Mekelweg 5 NL-2628 CD Delft Netherlands Leiden Univ Med Ctr Albinusdreef 2 NL-2333 ZA Leiden Netherlands

ISBN: (数字)9781510649408

ISBN: (纸本)9781510649408;9781510649392

Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cases human-level, performance, they have become the standard approach in this field. The design of the best possible medical image segmentation DNNs, however, is task-specific. Neural Architecture Search (NAS), i.e., the automation of neural network design, has been shown to have the capability to outperform manually designed networks for various tasks. However, the existing NAS methods for medical image segmentation have explored a quite limited range of types of DNN architectures that can be discovered. In this work, we propose a novel NAS search space for medical image segmentation networks. This search space combines the strength of a generalised encoder-decoder structure, well known from U-Net, with network blocks that have proven to have a strong performance in image classification tasks. The search is performed by looking for the best topology of multiple cells simultaneously with the configuration of each cell within, allowing for interactions between topology and cell-level attributes. From experiments on two publicly available datasets, we find that the networks discovered by our proposed NAS method have better performance than well-known handcrafted segmentation networks, and outperform networks found with other NAS approaches that perform only topology search, and topology-level search followed by cell-level search.

关键词： image segmentation Medical imaging Network architectures Prostate Spleen Convolution Neural networks image classification image processing algorithms and systems Medical research

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Fast Sparse Learning from Streaming Data with LASSO

Fast Sparse Learning from Streaming Data with LASSO

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Marija Iloska Petar M. Djurić Mónica F. Bugallo Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we propose Online LASSO - a version of LASSO that is configured for streaming data. In standard LASSO, the penalty parameter is typically chosen by cross-validation, a procedure which requires the entire dataset upfront and repeated fitting. The main contribution of this work is in finding an easy and principled choice for the penalty parameter for every incoming data point, in cases where the input features are uncorrelated. The proposed Online LASSO has several benefits: i) it is memory and time efficient ii) it is easy to implement, iii) it does not require an initial batch of data to start, iv) it does not require any tuning (e.g., step size or tolerance), and finally v) it converges to the performance of the optimal predictor and correct selection of features. We demonstrate these capabilities and compare Online LASSO with standard LASSO as well as other adaptive LASSO variations and provide a discussion on their performances.

关键词： Simulation Noise Memory management Signal processing algorithms Prediction algorithms Iterative methods Speech processing Tuning Standards Time-varying systems

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A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans 5

A Novel Automated Classification and Segmentation for COVID-...

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5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Wang, Shiyi Yang, Guang Imperial Coll London Natl Heart & Lung Inst London England Royal Brompton Hosp Cardiovasc Res Ctr London England

ISBN: (纸本)9781665462198

Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation. In COVID-19 computed tomography (CT) images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis. Based on this situation, some researchers propose the relevant DL models which can replace professional diagnostic specialists in clinics when lacking expertise. However, although DL methods have a stunning performance in medical image processing, the limited datasets can be a challenge in developing the accuracy of diagnosis at the human level. In addition, deep learning algorithms face the challenge of classifying and segmenting medical images in three or even multiple dimensions and maintaining high accuracy rates. Consequently, with a guaranteed high level of accuracy, our model can classify the patients' CT images into three types: Normal, Pneumonia and COVID. Subsequently, two datasets are used for segmentation, one of the datasets even has only a limited amount of data (20 cases). Our system combined the classification model and the segmentation model together, a fully integrated diagnostic model was built on the basis of ResNet50 and 3D U-Net algorithm. By feeding with different datasets, the COVID image segmentation of the infected area will be carried out according to classification results. Our model achieves 94.52% accuracy in the classification of lung lesions by 3 types: COVID, Pneumonia and Normal. For 2 labels (ground truth, lung lesions) segmentation, the model gets 99.57% of accuracy, 0.2191 of train loss and 0.78 +/- 0.03 of MeanDice +/- Std, while the 4 labels (ground truth, left lung, right lung, lung lesions) segmentation achieves 98.89% of accuracy, 0.1132 of train loss and 0.83 +/- 0.13 of MeanDice +/- Std. For future medical use, embedding the model into the medical facilities might be an efficient way of assisting or substituting do

关键词： Deep Learning Medical image Segmentation COVID-19 3D CT Scans

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Comparison of Learned image Compression Methods and JPEG

Comparison of Learned Image Compression Methods and JPEG

引用

Innovations in Intelligent systems and Applications conference

作者： Emir Ozturk Altan Mesut Computer Engineering Department Faculty of Engineering Trakya University Edirne Turkey

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

Media such as audio, images, and videos, which occupy significant storage space in digital environments, are often compressed to save space, particularly outside professional settings. The compression process aims to achieve space savings while maintaining an acceptable level of quality. Many standard image compression algorithms are based on the principle of eliminating data that the human eye cannot perceive and selectively removing certain data after performing spatial transformation on the obtained image. In recent years, compression algorithms utilizing autoencoders or generative adversarial network (GAN) architectures have emerged. These algorithms fundamentally aim to reduce the dimensionality of data and create a representation of it. The dimension reduction stage used is considered equivalent to the compression process. In this study, the performance measurement of three different models using autoencoders and GANs is conducted, and the results are compared with the performance of the JPEG algorithm in terms of speeds and ratio. Upon examining the results, it is observed that learned image compression methods are catching up with JPEG in terms of quality. The methods achieve better results in terms of compression ratio compared to JPEG, but they operate much slower than JPEG in terms of processing time and have some artificial artifacts.

关键词： Measurement image quality Deep learning image coding Computational modeling Transform coding Generative adversarial networks Data models Time factors Visual perception

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Classification of Red Watermelon Varieties Using Canny Edge Detection and CNN

Classification of Red Watermelon Varieties Using Canny Edge ...

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

作者： Milca Rejaiah B. Martinez Kian Matthew D. Dayrit Analyn N. Yumang School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (数字)9798350372106

ISBN: (纸本)9798350372113

Watermelon is a commonly cultivated fruit worldwide, especially in Southeast Asia. As one of the top exports in Asia, the commercialization of watermelon has its market. Its consumer appeal makes it one of the most sought-after fruits globally. With the watermelon fruit consisting of different varieties, consumers usually have difficulty in classifying watermelons due to their similar external appearances, especially when labeled under the same name. This study is conducted to implement a system that detects and classifies three red watermelon varieties, Red Export, Orchid Sweet, and Dixie Queen, with image processing algorithms. The researchers utilized Canny Edge Detection for the dataset’s preprocessing phase and Convolutional Neural Network (CNN) for its classification. The Raspberry Pi is also applied to this study. Moreover, the researchers created and collected their datasets for testing and validation data. The model used in this study has acquired 84.71% overall accuracy.

关键词： Accuracy image edge detection image processing Asia Process control Classification algorithms Convolutional neural networks

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Research on Unmarked Motion Action Recognition Technology Based on Computer Vision

Research on Unmarked Motion Action Recognition Technology Ba...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Lun Zhao Shandong Vocational College of Science and Technology Weifang China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

The main purpose of this study is to explore the issues of real-time, accurate, and unmarked recognition of sports movements in recent years. By reviewing the relevant research on machine learning or deep learning for specific sports or target actions based on computer vision image data input, the aim is to provide references for the application of unmarked motion capture technology in the field of sports motion recognition. The research employed a literature review methodology, conducting searches in six databases, namely Web of Science, PubMed, Scopus, Google Scholar, IEEE Xplore, and China National Knowledge Infrastructure (CNKI), covering publications from January 2000 to June 2020. Through boolean logic operations on the retrieved literature, key information such as first author/publication year, types/targets of motion, participant information, camera parameters, image feature extraction techniques, action recognition algorithms, evaluation methods for action recognition quality, training and validation methods for image data, and performance metrics for action recognition were extracted. After screening, a total of 23 articles were included in the study. The findings revealed that $39 \%$ of the studies utilized machine learning algorithms based on support vector machines, while $35 \%$ employed deep learning algorithms based on convolutional neural networks. Commonly used evaluation metrics for action recognition quality included classification accuracy, confusion matrix, and displacement error. The development of computer vision motion capture, models, and algorithms demonstrated promising applications in areas such as action technique recognition and sports performance analysis. Traditional machine learning algorithms like support vector machines and principal component analysis remain dominant in action recognition technology; however, in certain scenarios, the performance of deep learning algorithms surpassed that of traditional machine learning methods.

关键词： Deep learning Support vector machines Computer vision Machine learning algorithms image recognition Cameras Feature extraction Motion capture Real-time systems Sports

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Real-Time Obstacle Detection and Path Adjustment for Autonomous Robots Using OpenCV

Real-Time Obstacle Detection and Path Adjustment for Autonom...

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Telecommunications and Intelligent systems (ICTIS), International conference on

作者： Abrar Elaoua Mohamed Nadour Lakhmissi Cherroun Mohammed Taqiyeddine Mahdi Ahmed Hafaifa Laboratory of Applied Automatic and Industrial Diagnostics (LAADI) University of Djelfa Djelfa Algeria

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

The real-time obstacle detection and path adjustment system for autonomous robots presented in this paper was created using OpenCV. The combination of image processing techniques enables the robot to identify and navigate around any obstacle efficiently. The main functions include real-time image acquisition; where the robot constantly takes pictures of its surroundings, and obstacle detection; which uses edge detection and contour analysis for proper identification of hazards in the environment. The method entails that once an object is detected, the robot's route is reshaped with sophisticated algorithms that are aimed at optimizing navigation as well as minimizing collision probabilities. A wide range of challenging settings such as static or dynamic environments were subjected to rigorous testing on the proposed system. The results indicate that it is robust and effective in detecting obstacles reliably to enhance navigation based on its capacity to make adjustments during operation time (discovered employing experimentation that took place in these settings).

关键词： Visualization Navigation Service robots image processing image edge detection Real-time systems Telecommunications Reliability Autonomous robots Testing

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