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

共 12948 条记录，以下是311-320 订阅

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Split Computing based Quantized PoseNet Model for Distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

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15th International conference on Communication systems and Networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, Internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep Neural Network (DNN) models on them. To address this, we introduce Split Computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called Distributed Artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. The QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. The result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling image edge detection Computer architecture Bandwidth Inference algorithms Partitioning algorithms

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Research on Traditional Art image Reconstruction Method Based on Computer Vision Analysis 2

Research on Traditional Art Image Reconstruction Method Base...

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2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Supriya, M. Anvesh, Kuncham Muruganandham, B. Manasa Krishnan, K. Muthu, R. Murugan, K. Kpr Institute of Engineering and Technology Department of Ece India

ISBN: (纸本)9798350369960

In the framework of the digital era, the technology of image processing is one of the technologies that is being used increasingly often in all aspects of modern life. image correction may be handled using algorithms based on computer vision, which can increase the correction's influence on the image as a whole. The conventional method of identifying structural systems collects data via the use of wired or wireless measuring systems. The process of establishing sensors and the equipment used for data collection is laborious and takes a lot of time. The process has two stages: the first is the extraction of the line drawing, and the second is the rendering of the line drawing. An edge detection approach that integrates low-level and middle-level visual information is used to extract the line drawing in the process of on-line drawing extraction. This method is used to extract the drawing. The edges are then simplified and linked in accordance with the computer vision theory. Additionally, the image processing technology is used as the research object in order to develop a scientific model for the distorted image. These steps are based on the findings of traditional edge detection. image restoration can be helped along by filtering samples of the image and getting rid of any noise that is there. The fundamentals of computer vision and the technologies that are connected to it are broken down in detail, and the behavioral patterns of visual interaction are investigated. The image of Chinese traditional art serves both the role of art communication and as a medium and symbol for cultural communication. At the same time, it carries on the legacy of a fine traditional culture that has been passed down from generation to generation in China. The image processing technology that is based on computer vision algorithms provides a bigger number of benefits, in addition to a better degree of accuracy. © 2023 IEEE.

关键词： Computer Vision feedback image reconstruction Traditional art Visual analysis

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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...

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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...

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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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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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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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A Sequential Method for Enhancing the image Quality of Rice Leaf Diseases by Employing Various Preprocessing Techniques 9

A Sequential Method for Enhancing the Image Quality of Rice ...

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9th IEEE International conference on Smart Structures and systems, ICSSS 2023

作者： Govindarajan, S. Mary Vennila, S. Presidency College Pg and Research Department of Computer Science Chennai India

ISBN: (纸本)9798350384208

Rice is a fundamental staple crop worldwide, and monitoring the health and growth of rice plants is crucial for ensuring optimal yield and quality. Automated rice leaf segmentation from images plays a vital role in plant health assessment and disease detection, enabling early intervention and management. This study presents an approach for rice leaf segmentation using image processing techniques, aiming to automate the analysis of rice plant health. The proposed methodology involves several key steps. First, pre-processing techniques such as grayscale conversion, noise reduction, and contrast enhancement are applied to prepare the input images. Subsequently, various segmentation algorithms, including thresholding, edge detection, and region-based methods, are employed to isolate the rice leaves from the background. Feature extraction is then performed to capture relevant characteristics of the leaves, facilitating a detailed analysis. © 2023 IEEE.

关键词： image segmentation

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Content-Based Retinal image Analysis: Exploring the Impact of Similarity Measures and Machine Learning on LTP Features

Content-Based Retinal Image Analysis: Exploring the Impact o...

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2024 IEEE Students conference on Engineering and systems, SCES 2024

作者： Santra, Arpita Singh, Vibhav Prakash Dewang, Rupesh Kumar MNNIT Allahabad Computer Science Engineering Prayagraj India

ISBN: (纸本)9798350374711

Diabetic Retinopathy (DR) is a retinal condition resulting in damage to blood vessels within the eye, serving as a leading cause of vision impairment or blindness when not addressed. Manual identification of diabetic retinopathy is labor-intensive and susceptible to human error due to the intricate nature of the eye's structure. This work offers a comprehensive method that uses image pre-processing techniques, local ternary pattern (LTP) features, machine learning and different similarity measures to improve content based retinal image analysis and retrieval from fundus images. With an emphasis on feature extraction, subtle texture details were extracted from retinal fundus images using Local Ternary Patterns (LTP) with different radius values, specifically concentrating on radius 1, 2, and 3. This study includes two parts: first, it assesses machine learning algorithms for the classification of diabetic retinopathy. Of these, Random Forest performed the best, with an accuracy of 92.77%. Second, the research works on retinal image retrieval and compares various LTP variants' abilities to different retrieval tasks. To evaluate model performance for particular disease classes, class-wise image retrieval was carried out, providing information on individual class precision. Several similarity measures were investigated for retinal image retrieval, demonstrating the effectiveness of Cosine Similarity in achieving better retrieval results. © 2024 IEEE.

关键词： Decision trees

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