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

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Automated System for the Personalization of Retinal Laser Treatment in Diabetic Retinopathy Based on the Intelligent Analysis of OCT Data and Fundus images 14th

Automated System for the Personalization of Retinal Laser Tr...

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14th International KES conference on Intelligent Decision Technologies, KES-IDT 2022

作者： Ilyasova, Nataly Demin, Nikita Shirokanev, Aleksandr Andriyanov, Nikita Image Processing Systems Institute of the RAS—Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardeyskay 151 Samara443001 Russia Financial University Under the Government of the Russian Federation Leningradsky pr-t 49 Moscow Russia

ISBN: (纸本)9789811934438

In this work, we propose an automated system for the personalization of retina laser treatment in diabetic retinopathy. The system comprises fundus images processing methods, algorithms for photocoagulation pattern mapping, and intelligent analysis methods of OCT data and fundus images. The feasibility of introducing corrections at any interim stage of data processing makes for a safe treatment. A key module of the proposed software architecture is the system for the intelligent analysis of the photocoagulation pattern, allowing the proposed plan to be analyzed and the treatment outcome to be prognosticated. Working with the proposed system, the surgeon is able to map an effective photocoagulation pattern, which is aimed at providing a higher-quality diabetic retinopathy laser treatment when compared with the current approaches. The software developed is intended for the use of high-performance algorithms that can be parallelized using either a processor or a graphics processing unit. This allows achieving high data processing speed, which is so important for medical systems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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image Segmentation based Approach for Skin Disease Detection and Classification using Machine Learning algorithms

Image Segmentation based Approach for Skin Disease Detection...

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Integrated Circuits and Communication systems (ICICACS), IEEE International conference on

作者： Nazmul Hasan Sany Pintu Chandra Shill Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh

Skin disorders are ubiquitous, stemming from causes including fungus, germs, allergies, and viruses. Although laser and photonics medical technologies have revolutionised rapid and precise diagnosis, their exorbitant prices restrict accessibility. To address this, this paper describes an image processing strategy for dermatological screening and procedure that comprises obtaining digital photographs of damaged skin patches and applying image processing to detect the disease type. Moreover, when conducted manually by medical specialists, identifying skin illnesses is time-intensive and subjective. As a consequence, both patients and physicians want automated skin disease prediction, which makes the treatment plan faster. This cost-effective approach depends on a basic setup: a camera and a computer. Operating on colour photos, a solution combines resizing for feature extraction using a pertained convolutional neural network, followed by classification using a multiclass SVM. The technology efficiently recognises four separate skin illnesses with an astonishing 90.8% accuracy, giving users complete findings spanning disease kind, extent, and severity.

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Unsupervised domain adaptation in human detection for various human pose

Unsupervised domain adaptation in human detection for variou...

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International image processing, Applications and systems conference (IPAS)

作者： Benoit Lagadec Ekaterina Kostrykina Esifrance Le Cannet France Universite Cote d’Azur Nice Cedex 2 France

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In human detection algorithms, some recurrent issues are still challenging. Indeed, in a real application we have a lack of various human poses in our data. For example, in video surveillance it could be a significant issue to forget detecting a human in uncommon pose. We propose a compensation to these less frequent cases. We introduce a detection in rotated view to compensate limited various poses in our dataset. Instead of introducing a generative enhancement for every human body pose, which could be exhausting. Indeed, data annotation has become a difficulty for many applications. Moreover, a simple data augmentation is not efficient to generalize our training. We need a strategy to learn this new poses. So, we will introduce a process to compute pseudo labels in a sequence of rotated views. It avoids to use a clustering algorithm like dbscan [Chen et al.(2021)Chen, Lagadec, and Bremond.] or K-means to compute a pseudo label. A summary of theoretical results is described in a first section to illustrate our baseline. First of all, we will illustrate how we process pseudo label on various views by two mechanisms which have been called relaxation and scanning. We introduce a sequence of rotations of original view, to process a detection in a compensated view. In second step, we will learn this distortion created by the various views and construct an approach to distill original knowledge to a more generalized *** main improvement of this approach is to propose an alternative solution at dbscan [Chen et al.(2021)Chen, Lagadec, and Bremond.] approach which is popular, but which is difficult to use in real applications. We obtain a significant result by improving accuracy in especially various pose datasets.

关键词： Training Accuracy Annotations image processing Clustering algorithms Data collection Video surveillance Distortion Data augmentation Detection algorithms

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Implementation of Automated Forest Fire Detection System using IoT

Implementation of Automated Forest Fire Detection System usi...

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Applied Artificial Intelligence and Computing (ICAAIC), International conference on

作者： S Sujitha Harshika M Disha Aamna Nafiza V Hemavathi Department of EEE New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Forest fires pose a significant threat to ecosystems and wildlife, exacerbating global warming and environmental degradation. The contemporary challenge lies in the difficulty of timely detection by modern organizations. In response, the development of an automated forest fire detection system becomes imperative. The system involves the integration of various technologies, including Arduino microcontrollers, cameras, smoke detectors, flame detectors, and image processing algorithms. When a fire is detected, the system promptly sends alert messages to nearby forest departments, facilitating rapid response and mitigation efforts. This research work explores the design, implementation, and functionality of such an automated forest fire detection system, emphasizing the role of each component in enhancing the overall efficacy of early fire detection. The integration of Arduino technology, coupled with advanced sensors and image processing, exemplifies a multidimensional approach to addressing the pressing issue of forest fires.

关键词： Wildfires image processing Wildlife Prototypes Forestry Cameras Climate change Smoke detectors Global warming Environmental monitoring Autonomous systems Microcontrollers image sensors Alarm systems Internet of Things

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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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A Fast Saturation Based Dehazing Framework with Accelerated Convolution and Attention Block

A Fast Saturation Based Dehazing Framework with Accelerated ...

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

作者： Shuocheng Wang Jiaming Liu Yilian Zhong Ruoxi Zhu Jiazheng Lian Hao Zhang Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time image dehazmg is crucial for applications such as autonomous driving, surveillance, and remote sensing, where haze can significantly reduce visibility. However, many deep learning algorithms are hindered by large model sizes, making real-time processing difficult to achieve. Several fast and lightweight dehazing networks rely on estimating K(x), but they often fail to deliver satisfying performance. In this paper, we present a novel fast dehazing framework built upon the saturation-based algorithm. We design a new convolution module called Feature Extraction Partial Convolution (FEPC), which is faster and achieves better performance than the vanilla 3×3 convolution. Additionally, we fully leverage the information redundancy between feature map channels by dividing it into two parts along the channel dimension and designing a Self-Cross Attention Block (SCAB). The reduction in channel count significantly reduces computational load and improves the framework’s speed. Through extensive experiments, our method demonstrates not only a fast inference speed but also superior dehazing performance, providing a promising solution for real-time practical deployment. Our code will be available at https://***/superwscZFSB-Dehazing-Framework.

关键词： Deep learning image quality Convolution Surveillance Redundancy Signal processing algorithms Feature extraction Real-time systems Speech processing Remote sensing

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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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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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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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