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

共 16440 条记录，以下是1991-2000 订阅

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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...

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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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Real-Time Malicious Traffic Detection Based on Frequency Domain Features

Real-Time Malicious Traffic Detection Based on Frequency Dom...

引用

International conference on Wireless Communications and Signal processing (WCSP)

作者： Jiachen Wu Yang Liu School of Internet of Things Engineering Jiangnan University Wuxi China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an Shaanxi China National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

As an emerging security paradigm, machine learning (ML) based malicious traffic detection has the potential to detect zero-day attacks. However, current ML based detection methods suffer from low throughput and accuracy due to inefficient traffic feature extraction. Consequently, they are unable to detect attacks in real-time and can be circumvented by sophisticated attacks. To address these limitations, this paper proposes a real-time malicious traffic detection algorithm by combining the extraction of traffic frequency domain features with the Isolation Forest algorithm. By utilizing frequency domain features with low redundancy and information loss, the proposed method achieves high accuracy and real-time performance. Attackers can hardly manipulate frequency domain features, enabling effective detection of various circumvention techniques. Experimental results demonstrate the high accuracy of the proposed method in the real-time detection of malicious traffic.

关键词： Wireless communication Accuracy Frequency-domain analysis Signal processing algorithms Clustering algorithms Forestry Feature extraction Throughput Real-time systems Time complexity

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Sparsity-aware Subband Adaptive Filtering in Impulsive Interferences

Sparsity-aware Subband Adaptive Filtering in Impulsive Inter...

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IEEE International conference on Electronic Information and Communication Technology (ICEICT)

作者： Zhikai Gong Yingsong Li Baohe Pang Xueyan Li Wudang xiao Liping Li Nikola Zlatanov College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In this paper, we propose two new subband adaptive filtering (SAF) algorithms based on the cost function of the logistic distance metric cost function and use the proportionate proximal gradient algorithm to exploit the sparsity of the system fully. Compared with prior works, the proposed algorithms can handle many different situations better. Through computer simulations, we prove that the algorithm performs excellently in sparse systems disturbed by impulse noises. The algorithm can be used in areas like system identifications.

关键词： Measurement Adaptive systems Computer simulation Noise Filtering algorithms Cost function Information filters

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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-...

引用

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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MSIC-YOLO: Identifying Coal and Gangue Based on MobileNetV3-SE- Inner_ciou

MSIC-YOLO: Identifying Coal and Gangue Based on MobileNetV3-...

引用

6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Yixin Li Yuanjiao Hu Liumei Zhang School of Computer Science Xi'an Shiyou University Xi'an China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

MSIC-YOLO Method for Identifying Coal and Gangue based on MobileNetV3-SE- Inner_ciou is proposed to address the issues of poor real-time performance, high consumption of computing resources and energy. Firstly, this method introduces the lightweight network MobileNetV3 to reduce the number of model parameters and computational complexity. Secondly, the SE attention mechanism effectively extracting global features. Finally, replace the regression loss function with inner_ ciou to improve the convergence speed and regression accuracy of the model. The experimental results show that compared with the original YOLOv8s algorithm, the MSIC-YOLO model has improved mAP50-95 by 0.4%, precision by 1.86% and reduced parameter count by 38.87%, computational complexity by 41.9%, and model size by 38.2%.

关键词： Attention mechanisms Accuracy Computational modeling Signal processing algorithms Coal Signal processing Feature extraction Loss measurement Real-time systems Computational complexity

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Classification of Red Watermelon Varieties Using Canny Edge Detection and CNN

Classification of Red Watermelon Varieties Using Canny Edge ...

引用

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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An Efficient Randomized Low-Rank Matrix Factorization with Application to Robust PCA

An Efficient Randomized Low-Rank Matrix Factorization with A...

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2021 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2021

作者： Kaloorazi, Maboud F. Chen, Jie Li, Fei Wu, Dan Xi'An Shiyou University School of Electronic Engineering Xi'an China Northwestern Polytechnical University School of Marine Science and Technology Xi'an China

ISBN: (纸本)9781665429184

Low-rank matrix factorization algorithms using the randomized sampling paradigm have recently gained momentum, owing to their computational efficiency, high accuracy, robustness, and efficient parallelization. This paper presents a randomized factorization algorithm tailored for low-rank matrices, called Randomized Partial UTV (RaP-UTV) factorization. RaP-Utvis efficient in arithmetic operations, and can harness the parallel structure of advanced computational platforms. The effectiveness of RaP-Utvis demonstrated through synthetic and real-world data. Applications treated in this work include image reconstruction and robust principal component analysis. The results of RaP-UTV are compared with those of multiple algorithms from the literature. © 2021 IEEE.

关键词： Computational efficiency

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Research on image Defogging Enhancement Technology Based on Retinex Algorithm

Research on Image Defogging Enhancement Technology Based on ...

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3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME), International conference on

作者： Lei Lei Liang Wang Jian Wu xiaochun Bai Pinghai Lv Miao Wei Electric Power Research Institute of State Grid Shaanxi Electric Power Co. Ltd Xi’an Shaanxi China State Grid (Xi’an) Environmental Protection Technology Center Co. Ltd Xi’an Shaanxi China State Grid Shaanxi Electric Power Co. Ltd Xi’an Shaanxi China

Under bad weather conditions, the quality of the image we get will be seriously affected. Therefore, we must restore and improve the image quality and enhance the image to meet the needs of human observation or subsequent processing. In recent years, an image enhancement technology based on Retinex algorithm has attracted wide attention because it is not affected by illumination and can better restore the original appearance of objects. In order to solve the problem that the details of the existing image enhancement algorithms are blurred when they are enhanced in foggy days, this paper will study the image defogging enhancement technology based on Retinex algorithm. The experimental results show that the clear processing method of foggy images in this paper has a good effect, but it needs a certain sample training time, and the higher the accuracy, the longer the time it takes, so it has certain algorithm complexity. From the subjective observation, this algorithm has better detail expression ability, eliminates the “halo artifact and at the same time, inhibits the over-sharpening phenomenon on the premise of ensuring hierarchy. Therefore, Retinex algorithm makes the defogging effect better under the condition of low complexity.

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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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