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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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MUWS 2024: The 3rd International Workshop on Multimodal Human Understanding for the Web and Social Media 24

MUWS 2024: The 3rd International Workshop on Multimodal Huma...

引用

4th Annual International conference on Multimedia Retrieval (ICMR)

作者： Kastner, Marc A. Cheema, Gullal S. Hakimov, Sherzod Garcia, Noa Kyoto Univ Kyoto Japan Leibniz Univ Hannover L3S Res Ctr Hannover Germany Univ Potsdam Potsdam Germany Osaka Univ Osaka Japan

ISBN: (纸本)9798400706028

Multimodal human understanding and analysis are emerging research areas that cut through several disciplines like Computer vision (CV), Natural Language processing (NLP), Speech processing, Human-Computer Interaction (HCI), and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights on perceptual understanding through signs and symbols across multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop is an interactive event and includes keynotes by relevant experts, a poster session, research presentations and discussion.

关键词： multimodality machine learning image-text relations social media web human understanding semiotics

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Research on Water Conservancy Project Monitoring and Intelligent Identification Method Based on Computer vision Technology

Research on Water Conservancy Project Monitoring and Intelli...

引用

2024 International conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Zhu, Jin Yang, Feng Zhejiang Province Water Conservancy and Hydropower Technology Consultation Center Zhejiang Hangzhou310009 China Zhejiang Zhongshui Engineering Technology Co. Ltd. Hangzhou310020 China

ISBN: (纸本)9798350369212

Monitoring and maintenance of water resources projects is essential to ensure project safety and environmental protection. Traditional monitoring methods often rely on manual inspections and sensor data, but these methods suffer from high cost, low efficiency and limited monitoring range. With the rapid development of computer vision technology, its potential for application in water conservancy engineering has gradually emerged. The purpose of this paper is to study the water conservancy project monitoring and intelligent identification methods based on computer vision technology. We first outline the basic concepts of computer vision and its application scenarios in water conservancy engineering, including structural health monitoring, environmental monitoring and disaster management. Then, the specific applications of image processing techniques, machine learning and deep learning in intelligent recognition are described in detail, especially how these techniques can be utilized to achieve an efficient and accurate real-time monitoring system. Through several case studies, this paper demonstrates the effects and advantages of the application of computer vision technology in actual water conservancy projects. Finally, it summarizes the main findings of the current study and looks forward to the future technological development and application prospects, pointing out the direction of further research. The findings of this paper show that computer vision-based monitoring and intelligent identification methods not only improve the accuracy and efficiency of monitoring, but also provide new technical means for the management and maintenance of water conservancy projects. © 2024 IEEE.

关键词： Environmental monitoring

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New Indicators and Standards for Measuring of the End Mill's Helical Groove by image processing 10

New Indicators and Standards for Measuring of the End Mill's...

引用

conference on Optical Metrology and Inspection for Industrial applications X

作者： Pivkin, Petr M. Ershov, Artem A. Grechishnikov, Vladimir A. Kuznetsov, Vladimir A. Nazarenko, Ekaterina S. Malysheva, Elena Yu. Nadykto, Alexey B. Moscow State Univ Technol STANKIN Lab Micromachining Technol Moscow 127055 Russia Moscow State Univ Technol STANKIN Dept Cutting Tools & Machining Technol Moscow 127055 Russia

ISBN: (纸本)9781510667877;9781510667884

The studies will be carried out using optical metrology methods on a Walter Helicheck inspection machine in reflected light and a number of images were stored to form a statistical sample. Established new indicators and criteria for grinding efficiency based on image processing of the helical groove of the end mill. As a result, recommendations for the selection of optical control techniques were made for the first time at the intermediate stage of technological preparation for production, in real time, and after processing. In this work, for the first time, we prove the possibility of determining the camera displacement pith distance during continuous scanning of the profile of a helical surface in a radial section, the measurement accuracy and recreating a three-dimensional model of the object. As a result of the work of the new algorithm using the Haar-wavelet with new indicators, it was established that the actual one is located inside the focal zone, which proves the possibility of applied application of the method of monitoring the shape of helical flute of end mills using computer vision. The measurement accuracy of the helical flute increased from 4 to 12% along its profile.

关键词： image processing standards for measuring cutting tools optical metrology new indicators helical groove Haar-like feature

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image processing Technique for Tracking and Counting of Vehicles Using ROI

Image Processing Technique for Tracking and Counting of Vehi...

引用

2023 International conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar, Ankit Chandel, Ritika Singh, Monika Apex Institute of Technology Chandigarh University Mohali India

ISBN: (纸本)9798350357769

This research paper presents a novel approach for vehicle tracking and counting utilizing the advanced object detection model YOLOv8 in the field of image processing. The accurate monitoring of vehicular traffic is crucial for various applications, including traffic management, urban planning, and safety analysis. Existing methods often struggle with real-time tracking and counting of vehicles due to challenges such as occlusions, varying lighting conditions, and complex traffic scenarios. In our proposed methodology, we leverage the power of YOLOv8, a deep learning-based object detection model, uses real-time detection to identify and monitor vehicles, ensuring high accuracy and robustness in various traffic environments. The proposed approach provides a valuable tool for traffic management authorities and urban planners, offering precise data for traffic flow analysis and infrastructure development. © 2023 IEEE.

关键词： Deep Learning image processing machine vision Object Detection ROI (Region of Interest) Vehicle Detection YOLO (You Only Look Once)

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Development of an Environmentally Friendly IA Building Energy-Saving machine Robot with Computer vision and Data Mining Algorithms 4

Development of an Environmentally Friendly IA Building Energ...

引用

4th International conference on Inventive Research in Computing applications, ICIRCA 2022

作者： Dong, Zhao Hu, Yunpeng Chen, Xin Li, Min Qiu, Qingling School of Mechanical and Electrical Engineering Wuhan Business University Hubei Wuhan430056 China

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Development of an environmentally friendly IA building energy-saving machine robot with computer vision and data mining algorithms are discussed in the paper. For the intelligent system, the computer vision should be then integrated. The general process of vision based non-contact measurement can be divided into image acquisition, image preprocessing, target feature extraction, target feature analysis, etc. With this flow model, the novel energy-saving machine robot is considered with computer vision and data mining algorithm. The camera calibration is used to reduce the measurement error caused by lens distortion, calibrate again to obtain the corresponding relationship between pixel size and physical size. This designed model will better assist the analysis of the environmentally friendly IA building energy-saving machine. Through the test on the performance, the effectiveness is validated. © 2022 IEEE.

关键词： Computer vision

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2023 5th International conference on Artificial Intelligence and Computer applications, ICAICA 2023

2023 5th International Conference on Artificial Intelligence...

引用

2023 5th International conference on Artificial Intelligence and Computer applications, ICAICA 2023

ISBN: (纸本)9798350323313

The proceedings contain 173 papers. The topics discussed include: restricted area sign detector using YOLO v5;research on distance teaching course interactive system based on computer algorithm research data;APT detection and attack scenario reconstruction based on big data analysis;new image processing: VGG image style transfer with gram matrix style features;trajectory measurement and positioning of underwater vehicle based on monocular stereo vision;the importance of multi feature extraction and fusion for prediction of protein subcellular localization;design and implementation of FPGA-based four-dimensional ultra chaotic system;flocking towards a robust mobile network topology;real time speech recognition method for online complaints from power grid customers based on improved residual network;optimization of parking space detection system based on ZigBee wireless sensor network;and a wire drawing defect detection approach for FDM 3D printing based on machine vision technology.

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LoG-VMamba: Local-Global vision Mamba for Medical image Segmentation 17th

LoG-VMamba: Local-Global Vision Mamba for Medical Image Seg...

引用

17th Asian conference on Computer vision, ACCV 2024

作者： Dang, Trung DQ. Nguyen, Huy Hoang Tiulpin, Aleksei University of Oulu Oulu Finland

ISBN: (纸本)9789819609000

Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language processing and general sequence modeling. Various attempts have been made to adapt Mamba to Computer vision tasks, including medical image segmentation (MIS). vision Mamba (VM)-based networks are particularly attractive due to their ability to achieve global receptive fields, similar to vision Transformers, while also maintaining linear complexity in the number of tokens. However, the existing VM models still struggle to maintain both spatially local and global dependencies of tokens in high dimensional arrays due to their sequential nature. Employing multiple and/or complicated scanning strategies is computationally costly, which hinders applications of SSMs to high-dimensional 2D and 3D images that are common in MIS problems. In this work, we propose Local-Global vision Mamba, LoG-VMamba, that explicitly enforces spatially adjacent tokens to remain nearby on the channel axis, and retains the global context in a compressed form. Our method allows the SSMs to access the local and global contexts even before reaching the last token while requiring only a simple scanning strategy. Our segmentation models are computationally efficient and substantially outperform both CNN and Transformers-based baselines on a diverse set of 2D and 3D MIS tasks. The implementation of LoG-VMamba is available at https://***/Oulu-IMEDS/LoG-VMamba. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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An Intelligent Self-Driving Car’s Design and Development, Including Lane Detection Using ROS and machine vision Algorithms 1

引用

3rd International conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Sujatha, E. Sundar, J. Sathiya Jeba Raju, D. Naveen Lakshminarayanan, S. Suganthi, N. Thandalam Chennai India Department of Computer Science and Engineering Velammal Institute of Technology Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India Sri Sairam Engineering College Chennai India SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

It is challenging to find a solution for lane detection. It has aroused the curiosity of the computer vision field for many years. It has been found that computer vision and machine learning algorithms struggle to tackle the multi-feature identification problem known as lane detection. Even though there are a few different machine learning approaches that may be used for lane identification, these approaches are often employed for classification rather than feature development. On the other hand, contemporary techniques of machine learning may be used to discover features that have a high recognition value, and they have shown success in feature identification tests. These strategies haven’t been applied correctly, which compromises their efficiency and accuracy when it comes to lane recognition. In this study, we provide a fresh approach to solving the problem. A brand-new preprocessing and Region of Interest (ROI) selection method is presented in this article. The major objective is to extract white features by making use of the HSV color transformation, adding preliminary edge feature detection while doing preprocessing, and then selecting ROI based on the preprocessing that was proposed. With the help of this cutting-edge preprocessing strategy, the lane may be found. The integrated autonomous vehicle that we envision is one that is controlled by a Robotic Operating System and that is capable of making intelligent driving choices. The unique filtering and noise reduction techniques that were used on the visual feedback by means of the processing unit served as the basis for the digital image-processing algorithm that was responsible for the greatest performance achieved by the autonomous vehicle. Within the control system, we used two separate control units, one of which was a master and the other of which was a slave. The master control unit is in charge of the visual processing and filtering, while the slave control unit is in charge of the vehicle’s propulsio

关键词： Autonomous vehicles

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Padding-Free Convolution Based on Preservation of Differential Characteristics of Kernels 22

Padding-Free Convolution Based on Preservation of Differenti...

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22nd IEEE International conference on machine Learning and applications, ICMLA 2023

作者： Leng, Kuangdai Thiyagalingam, Jeyan Science and Technology Facilities Council Scientific Computing Department Didcot United Kingdom

ISBN: (纸本)9798350345346

Convolution is a fundamental operation in image processing and machine learning. Aimed primarily at maintaining image size, padding is a key ingredient of convolution, which, however, can introduce undesirable boundary effects. We present a non-padding-based method for size-keeping convolution based on the preservation of differential characteristics of kernels. The main idea is to make convolution over an incomplete sliding window 'collapse' to a linear differential operator evaluated locally at its central pixel, which no longer requires information from the neighbouring missing pixels. While the underlying theory is rigorous, our final formula turns out to be simple: the convolution over an incomplete window is achieved by convolving its nearest complete window with a transformed kernel. This formula is computationally lightweight, involving neither interpolation or extrapolation nor restrictions on image and kernel sizes. Our method favours data with smooth boundaries, such as high-resolution images and fields from physics. Our experiments include: i) filtering analytical and non-analytical fields from computational physics and, ii) training convolutional neural networks (CNNs) for the tasks of image classification, semantic segmentation and super-resolution reconstruction. In all these experiments, our method has exhibited visible superiority over the compared ones. © 2023 IEEE.

关键词： computer vision convolutional neural network differential operator machine learning padding

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Heart Signal processing Using Wavelet Analysis and Deep Learning Algorithms Based on Artificial Intelligence

Heart Signal Processing Using Wavelet Analysis and Deep Lear...

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International conference on applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Zilaie, Morteza Mohammadkhani, Zohreh Asrari, Keyvan Azimi Talebiyan, Seyed Reza Islamic Azad University Science and Research Branch Khorasan Razavi Mashhad Iran The University of Pavia Pavia Italy Imam Reza International University Khorasan Razavi Mashhad Iran

ISBN: (纸本)9783031840999

The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different heart failures produces different types of this signal. In this research, the cardiac signals of the patients are analyzed by an artificial intelligence neural network, and the type of the person’s disease is diagnosed. The artificial intelligence in question can be learned and can be used as an assistant ECG signal is calculated using continuous wavelet transform and then it is subjected to learning and evaluation using a deep convolutional neural network. In the architecture of artificial intelligence, two well-known neural networks GoogLeNet and SqueezeNet are used, which have been sufficiently trained in similar applications such as image processing and machine vision. In the end, the type of heart failure is diagnosed and classified. The cardiac signals of the patients used in the simulation were extracted from the PhysioNet medical engineering standard dataset of MIT University. In the end, the simulation results of the research are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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