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

共 15616 条记录，以下是361-370 订阅

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The Use of Multi-Spectral images Integration in On-Board Helicopter Information Support systems During Search and Rescue Operations

The Use of Multi-Spectral Images Integration in On-Board Hel...

引用

2024 systems of Signals Generating and processing in the Field of on Board Communications, SOSG 2024

作者： Leon, German Koptev, D.S. Department of Space Instrumentation and Communication Systems Southwest State University Kursk Russia

ISBN: (纸本)9798350383614

In order to ensure flight safety during search and rescue operations in difficult meteorological conditions, for example, in the Arctic zone, to generate messages and signals about an approaching obstacle, for the purpose of information support for the crew, it is necessary to provide visualization of the behind-the-cockpit environment by integration of the environment images obtained by cameras in different spectral ranges. The integral indicator of image quality is applicable to assess the information content of both the entire image and its individual parts of interest, obtained automatically or selected by the operator. It is advisable to use this indicator to assess the information content of the channels of technical vision systems for the purpose of adaptive adjustment of video image integration algorithms, in particular, as part of the hardware and software complex for search and rescue operations in difficult meteorological conditions. The implementation of the image integration procedure with subsequent visualization requires the presence of hardware, video cameras, television, long-wave and short-wave infrared ranges. It should be noted that each range requires certain shooting conditions, and, accordingly, is characterized by a given quality of the resulting images. The article shows the advantages and disadvantages of using cameras in the indicated spectral ranges, presents the results of their practical use, describes the procedure for image integration, and provides parameters for assessing their quality. It should be said that today, a very relevant area of research is the synthesis of complexes for supporting air search and res-cue operations with modules and hardware and software that implement procedures for ensuring the integration of initial data, which are often formed by a laser television module (LTM), including television video cameras (TV), long-wave infrared cameras (LWIR), shortwave infrared cameras (SWIR), laser-ranging module and forwar

关键词： Integration

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Interpreting and understanding the image using deep learning and imprecise information from the monitoring system

Interpreting and understanding the image using deep learning...

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2023 IEEE International conference on Big Data, BigData 2023

作者： Kubanek, Mariusz Bobulski, Janusz Karbowiak, Lukasz Czestochowa University of Technology Department of Computer Science Czestochowa Poland

ISBN: (纸本)9798350324457

The interpretation and understanding of images using deep learning algorithms in monitoring systems is an important area of research in computer vision. In this paper, we propose a novel approach for interpreting and understanding images of human behaviour captured from cameras and drones in monitoring systems. In the work, we presented a pedestrian detection method. This is the first element of a larger system of interpreting the behaviour of traffic participants. We begin by pre-processing the images to remove noise and unwanted artefacts. We then use labelled and unlabelled data to train a deep learning algorithm to accurately classify and predict human behaviour. To account for imprecise information, we incorporate probabilistic models into the algorithm to allow it to make predictions with uncertainty. To evaluate the performance of our proposed approach, we use standard metrics, such as precision, recall, and F1 score, and compare our results to existing methods. We also identify the limitations of existing approaches and propose new methods for interpreting and understanding images that can improve their accuracy and reliability. Our proposed approach has the potential to provide a more accurate and robust method for interpreting and understanding images of human behaviour in monitoring systems. It can be used in various applications like traffic monitoring, security systems, and surveillance. Our results demonstrate the importance of incorporating imprecise information into deep learning algorithms to improve their performance and highlight the potential for future research in this area. © 2023 IEEE.

关键词： autonomous car computer vision pedestrian safety smart city

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image Based Classification of Methods-Time Measurement Operations in Assembly Using Recurrent Neuronal Networks 6th

Image Based Classification of Methods-Time Measurement Opera...

引用

6th International conference on System-Integrated Intelligence (SysInt)

作者： Rueckert, Patrick Birgy, Katrin Tracht, Kirsten Univ Bremen Bremen Inst Mech Engn BIME Badgasteiner Str 1 D-28359 Bremen Germany

ISBN: (纸本)9783031162817;9783031162800

image based classification enables the acquisition and transfer of data from manual assembly workstations into a digital environment. Based on the Methods-Time Measurement method, assembly processes are transformed into short, discrete basic operations that are recognised by means of image processing and used as input data for a multilayer neural network. A recurrent neural network algorithm is investigated for its applicability in combination with the sensor data. The five basic MTM operations reaching, grasping, bringing, releasing, and positioning are classified and additional influencing factors, as well as the implementation of an object recognition, are investigated. The following paper addresses the question of the extent to which manual assembly processes can be reliably derived from visual sensor data and classified by machine learning algorithms.

关键词： Neuronal networks Assembly image processing

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Digital image processing for the Diagnosis of the Cold Inducible Urticaria by Photoplethysmography-Based Methods

Digital image Processing for the Diagnosis of the Cold Induc...

引用

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

作者： Guryleva, Anastasia Fomina, Daria Volkov, Mikhail Serdoteckova, Sofia Danilycheva, Inna Russian Academy of Sciences Acousto-Optic Spectroscopy Lab Scientific and Technological Center of Unique Instrumentation Moscow Russia Moscow State Scientific and Practical Center for Allergology and Immunology City Clinical Hospital No 52 Ministry of Healthcare of Moscow Russia Sechenov University Department of Clinical Immunology and Allergology Moscow Russia ITMO University Faculty of Physics and Technology Saint-Petersburg Russia NRC Institute of Immunology FMBA of Russia Moscow Russia

ISBN: (纸本)9798350348293

The objectivity and accuracy of the diagnosis of the inducible urticaria can be improved through the analysis of microcirculation parameters. Photoplethysmography is an easily implemented non-invasive method able to collect valuable diagnostic data on the state and function of the vessels. Existing descriptions of photoplethysmography image processing algorithms are limited to the basic principles and general mathematical expressions. At the same time, the algorithm options can vary significantly and affect the data quality. In this paper, we study the optimal characteristics of the algorithms for photoplethysmography-based skin inspection for the diagnosis of the cold inducible urticaria. Presented approach provides automatic, fast and repeatable processing under various input data. © 2023 IEEE.

关键词： image processing

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YOLORe-IDNet: An Efficient Multi-camera System for Person-Tracking 1

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8th International conference on Computer vision and image processing (CviP)

作者： Gautam, vipin Prasad, Shitala Sinha, Sharad Indian Inst Technol Goa Sch Math & Comp Sci Farmagudi India

ISBN: (数字)9783031581816

ISBN: (纸本)9783031581809;9783031581816

The growing need for video surveillance in public spaces has created a demand for systems that can track individuals across multiple camera feeds in real-time. While existing tracking systems have achieved impressive performance using deep learning (DL) models, they often rely on pre-existing images of suspects or historical data. However, this is not always feasible in cases where suspicious individuals are identified in realtime and without prior knowledge. We propose a person-tracking system that combines correlation filters and Intersection Over Union (IOU) constraints for robust tracking, along with a DL model for cross-camera person re-identification (Re-ID) on top of YOLOv5. The proposed system quickly identifies and tracks suspect in real-time across multiple cameras and recovers well after full or partial occlusion, making it suitable for security and surveillance applications. It is computationally efficient and achieves a high F1-Score of 79% and an IOU of 59% comparable to existing state-of-the-art algorithms, as demonstrated in our evaluation on a publicly available OTB-100 dataset. The proposed system offers a robust and efficient solution for the real-time tracking of individuals across multiple camera feeds. Its ability to track targets without prior knowledge or historical data is a significant improvement over existing systems, making it well-suited for public safety and surveillance applications.

关键词： Realtime systems Person Tracking Person Re-identification Multi-camera tracking Correlation filter tracking

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Building Structures image Matching Using Convolutional Neural Network

Building Structures Image Matching Using Convolutional Neura...

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2024 systems of Signals Generating and processing in the Field of on Board Communications, SOSG 2024

作者： Andriyanov, N.A. Magdeev, R.G. Dementyiev, V.E. Financial University under the Government of the Russian Federation Moscow Russia Ulyanovsk State Technical University Ulyanovsk Russia National Research Mordovia State University Saransk Russia

ISBN: (纸本)9798350383614

The paper examines the features of combining multi-temporal and multi-angle images of building structures in order to identify critical changes. It is proposed to carry out such a combination on the basis of high-speed pseudogradient algorithms. The focus of the work is the problem of identifying areas of attention that are the source of combined points. It is proposed to perform such selection using convolutional neural networks of the YOLO family. The consistency of this approach is shown and estimates of the resulting gains are also calculated. It is also suggested to use different optimization methods for improving the processing speed. So the YOLO detector will be very high performance and efficient real time detector. ©2024 IEEE.

关键词： Object detection

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SuperTA-Net: A Supervised Transmission-Augmented Network for image Compressive Sensing Reconstruction 6

SuperTA-Net: A Supervised Transmission-Augmented Network for...

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6th International conference on Data-Driven Optimization of Complex systems (DOCS)

作者： Zhang, Zhijie Bai, Huang Stankovic, Ljubisa Sun, Junmei Li, Xiumei Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Univ Montenegro Fac Elect Engn Podgorica Montenegro

ISBN: (纸本)9798350377859;9798350377842

Compressive sensing (CS) has seen extensive use in signal processing, particularly in tasks related to image reconstruction. CS simplifies the sampling and compression procedures, but leaves the difficulty to the nolinear reconstruction. Traditional CS reconstruction algorithms are usually iterative, having a complete theoretical foundation. However, these iterative algorithms are constrained by significant computational complexity. While modern deep network-based methods can achieve high-precision reconstruction in compressed sensing (CS) with satisfactory speed, they often lack theoretical analysis and interpretability. To leverage the strengths of both types of CS methods, the deep unfolding networks (DUNs) have been developed. In this paper, a novel DUN named supervised transmission-augmented network (SuperTA-Net) is proposed. Based on the framework of our previous work PIPO-Net, the multi-channel transmission strategy is put forward to reduce the influence of critical information loss between modules and improve the reliability of data. Besides, in order to avoid the issues such as high information redundancy and high computational burden when too many channels are set, the attention based supervision scheme is presented to dynamically adjust the weight of each channel and remove the redundant information. Through experiments focused on reconstructing CS images, the proposed neural network architectures are shown to be highly effective.

关键词： Compressive sensing deep unfolding network multi-channel transmission attention based supervision alternating optimization

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Utilising Machine Learning for Modulation and Signal processing 2

Utilising Machine Learning for Modulation and Signal Process...

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2nd IEEE World conference on Communication and Computing (WCONF)

作者： Singh, Daljeet Pal Solanki, Madhav Singh Jyothi, Renuka S. Maharishi Univ Informat Technol Maharishi Sch Engn & Technol Uttar Pradesh India Sanskriti Univ Dept Comp Sci & Engn Uttar Pradesh India Jain Deemed Be Univ Dept Biotechnol & Genet Sch Sci Bangalore Karnataka India

ISBN: (纸本)9798350395334;9798350395327

Device studying (ML) is an effective device that has been used in many packages, consisting of virtual sign processing, communications structures, and modulation reputation. In particular, ML has been extensively studied for modulation recognition, which is a hard hassle because of the always-changing and unpredictable nature of the sign. With the aid of leveraging ML strategies, which can be used to quantify and check specific houses of a signal, reliable modulation popularity and signal processing strategies can be evolved. For instance, ML can be used to automatically discover modulation class when the image shape of the signal is unknown. It is achieved via reading the sign with device learning techniques consisting of Fourier transform;waveletbased totally function extraction, and convolutional neural networks. Different applications encompass channel estimation, signal detection, and optimization of verbal exchange networks. Further to recognizing modulation codecs, ML-based algorithms may be used to enhance signal processing and communique systems. It could be done with the aid of developing smarter systems that are capable of studying global data. ML can be used to robotically apprehend anomalous alerts and allow networks to quickly adapt to converting situations. ML techniques can also be used to optimize community parameters, which include power management, coding schemes, and modulation codecs, so as to maximize their performance.

关键词： Consisting Communications Modulation Techniques Parameters Management

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The Study of the Unsupervised Classification Method Using the K-means Algorithm by a Proposition of a Simple Initialization Technique 7th

The Study of the Unsupervised Classification Method Using ...

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7th International conference on Smart City Applications, SCA 2022

作者： Ouchani, Rahma Merzougui, Mohammed Nasri, M’barek BV Mohammed VI B.P. 524 Oujda60000 Morocco

ISBN: (纸本)9783031268519

image segmentation is a fundamental step in image processing, The existing segmentation techniques are numerous, but they are generally grouped into three main approaches which are the contour approach, the region approach and the cooperative approach. The segmentation by region approach aims to group the pixels with the same characteristics into homogeneous regions. It is characterized by the measure of uniformity of the regions constructed into the image. Generally, we distinguish three families of algorithms for the region approach: classification methods, region growth methods, and methods that divide or merge regions according to the chosen criterion. Our study is based on the unsupervised classification method using the K-means algorithm. This is the most known and used clustering algorithm, because of its simplicity of implementation. We know that this algorithm still suffers from several drawbacks such as The number of classes must be fixed at the beginning, The result depends on initial draw or starting values of the centers of the classes, so a main problem in the initialization phase. For this we propose to improve this behind by a simple initialization technique based on the optimization of the distance between the objects inside each class to obtain a set of compact and clearly separated clusters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： K-means clustering

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Feature identification for parameter extraction and defect detection using machine learning 38

Feature identification for parameter extraction and defect d...

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conference on Metrology, Inspection, and Process Control XXXviII

作者： Guo, Y. Pahlavani, H. Khachaturiants, A. Elsayed, K. van de Laar, J. Simons, E. Saikumar, N. Sadeghian, H. Nearfield Instruments BV Rotterdam Netherlands

ISBN: (纸本)9781510672178;9781510672161

Process control of advanced semiconductor nodes is not only pushing the limits of metrology equipment requirements in terms of resolution and throughput but also in terms of the richness of data to be extracted to enable engineers to fine-tune the process steps for increased yield. The move towards 3D structures requires extraction of critical dimension parameters from structures which can vary largely from layer to layer. For in-line process control, the necessary automation forces the development of layer and equipment-specific dedicated image processing algorithms. Similarly, with the increase in stochastic defects in the EUV era, detection of defects at the nm scale requires the identification of features captured in low resolution to meet the throughput requirements of HVM fabs, which can again lead to custom algorithm development. With the emergence of ML-based image processing methods, this process of algorithm development for both cases can be accelerated. In this work, we provide the general framework under which the images obtained from high-speed scanning probe microscopy-based systems can be used to train a network for either feature detection for parameter extraction or defect identification.

关键词： Scanning Probe Microscopy image processing Parameter extraction Defect detection Machine learning

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