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检索条件"任意字段=Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing."

共 8406 条记录，以下是331-340 订阅

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Online Extraction of Electrochemical Impedance Spectroscopy pattern Based on EV Load Profile and Short Time Fourier Transform for Diagnosis of Lithium-ion Battery Safety 14

Online Extraction of Electrochemical Impedance Spectroscopy ...

引用

14th Annual ieee Applied Power Electronics conference and Exposition, APEC 2025

作者： Lee, Miyoung Lee, Dongcheol Bae, Youngmin An, Jongchan Yang, Garam Na, Woonki Kim, Jonghoon Chungnam National University Department of Electrical Engineering Daejeon Korea Republic of California State University Department of Electrical and Computer Engineering Fresno United States

ISBN: (纸本)9798331516116

Batteries which convert chemical energy into electrical energy and play an important role in our daily lives and across various industries. The chemical reaction that occurs within a battery during operation of an electric vehicle (EV) depends on environmental conditions such as temperature, load, and humidity, and these variables greatly affect battery performance and life. However, there is challenging to analyze these electrochemical phenomena with only simple electric signals, such as direct current (DC) signal. For compensation, passive electrochemical impedance spectroscopy (PEIS) is derived by applying a signal processing.algorithm that converts time series signals into frequency-domain signals in this study. Based on this, we propose to learn a siamese convolutional neural network (SCNN) algorithm on the converted PEIS pattern to the image and to effectively diagnose the state of the battery through similarity evaluation. © 2025 ieee.

关键词： Network coding

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Detecting the Foreground Dynamic Scenes using Gaussian Mixture Model Analysis Compared with Novel Principal Component Analysis 2

Detecting the Foreground Dynamic Scenes using Gaussian Mixtu...

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2nd International conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Baswaraju, Swathi New Horizon College of Engineering Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350325799

Many computer vision applications rely on object recognition in video streams, and the most common technique for doing so is called foreground subtraction. updated backdrop model with a new frame. However, precise object identification is challenging in static and dynamic foregrounds. This study proposes a novel approach. Recently, Novel Principal Component Analysis, also known as (NPCA), has been used extensively across the industry for dimension reduction in image processing. presentation, and pattern identification to this issue that is based on the Gaussian Mixture Model (GMM) technique. A new updating method has been implemented to achieve a more stable model for the dynamic zones. This study provides a comprehensive overview and evaluation of other subtraction methods. The experimental results demonstrate the superiority of the proposed method over numerous top-tier object detection methods. © 2023 ieee.

关键词： Principal component analysis

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Progressive Semantic Segmentation

Progressive Semantic Segmentation

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Chuong Huynh Anh Tuan Tran Khoa Luu Minh Hoai VinAI Res Hanoi Vietnam VinUniversity Hanoi Vietnam Univ Arkansas Fayetteville AR 72701 USA SUNY Stony Brook Stony Brook NY 11790 USA

ISBN: (纸本)9781665445092

The objective of this work is to segment high-resolution images without overloading GPU memory usage or losing the fine details in the output segmentation map. The memory constraint means that we must either downsample the big image or divide the image into local patches for separate processing. However, the former approach would lose the fine details, while the latter can be ambiguous due to the lack of a global picture. In this work, we present MagNet, a multi-scale framework that resolves local ambiguity by looking at the image at multiple magnification levels. MagNet has multiple processing.stages, where each stage corresponds to a magnification level, and the output of one stage is fed into the next stage for coarse-to-fine information propagation. Each stage analyzes the image at a higher resolution than the previous stage, recovering the previously lost details due to the lossy downsampling step, and the segmentation output is progressively refined through the processing.stages. Experiments on three high-resolution datasets of urban views, aerial scenes, and medical images show that MagNet consistently outperforms the state-of-the-art methods by a significant margin. Code is available at https://***/VinAIResearch/MagNet.

关键词： image segmentation computer vision image resolution Codes Magnetic separation Magnetic resonance imaging Semantics

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Smart Monitoring and Diagnostics for Fault Detection in Power Plant Equipment 3

Smart Monitoring and Diagnostics for Fault Detection in Powe...

引用

3rd ieee International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kala, S. Padma Kumar S, Pradeep Meerimatha, G. Almusawi, Muntather Gurumoorthy, Sasikumar Dept Of Cse Tamilnadu Chennai India Nitte Meenakshi Institute Of Technology Department Of Ece Bengaluru India Department Of Eee Ananthapuramu India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq School Of Engineering And Technology Dhanalakshmi Srinivasan University Computer Science And Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350318609

In recent years, Fault Diagnosis and Identification (FDI) has grown significantly. Generally used methodology in these types of frameworks include model-based approaches, fault approaches, and pattern recognition techniques. However, it was essential to society's energy supply, therefore maintaining its safe and effective functioning is of utmost importance. This research used sophisticated Fault Detection and Diagnosis (FDD) methods to diagnose the problems in contemporary power plants in order to decrease maintenance shutdowns and expenses. Therefore, this research applied deep learning to image processing.using efficient Long Short-Term Memory (LSTM)-based methods for object recognition and categorization. Using the observed frequency as input, the trained LSTM is utilized to detect power variations in real time. Control devices, such as synchronous generators and Energy Storage Systems (ESSs), was maintain a steady frequency by using the detected power variations. According to the results, the suggested approach demonstrated superior performance in terms of LG faults, with 0.427%, LLG faults of 0.800%, LGG faults of 0.186 %, and LL faults of 0.741%. Compared with existing methods namely Sparrow Search Algorithm (SSA) and Deep Long Short-Term Memory (LSTM) it shows that suggested methods are achieved better performances with less faults respectively. © 2024 ieee.

关键词： Long short-term memory

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Tweeting from Left to Right: Tracing the Dynamics of Emotional Messaging and Public Engagement During the 2018 vs. 2022 US Campaigns 21

Tweeting from Left to Right: Tracing the Dynamics of Emotion...

引用

21st ieee International Symposium on Parallel and Distributed processing.with Applications, 13th ieee International conference on Big Data and Cloud Computing, 16th ieee International conference on Social Computing and Networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Meng-Jie University of Canterbury School of Psychology Speech and Hearing Christchurch New Zealand

ISBN: (纸本)9798350329223

Despite numerous studies delving into social media politicking, there is, thus far, little understanding of how distinct emotions spread online. Much of the prior work has focused on positive vs. negative diffusion or measured emotional rhetoric without considering public engagement on such sites. Since elected officials increasingly turn to social media, even less is known about the impact of distinct emotional messaging on mobilizing users, attracting supporters, or disseminating political speeches. Moving beyond merely a valence-based approach, the study here seeks to address this gap by analyzing over 23,000 tweets posted by Senate candidates during the 2018 and 2022 US midterm elections through natural language processing. Specifically, leveraging the IBM Tone Analyzer, the present work implements a cross-sectional examination to explore: a) whether certain emotions - joy, confidence, anger, fear, and sadness - conveyed in candidates' tweets might be more likely to garner public attention;and b) if so, does this pattern vary across the political spectrum, illuminating the ways in which Democratic vs. Republican candidates adopt to expand their online reach over time? © 2023 ieee.

关键词： Natural language processing.systems

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A Lung Cancer Tumor image Segmentation Method of A CFC-MSPCNN based on PET/CT 3

A Lung Cancer Tumor Image Segmentation Method of A CFC-MSPCN...

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3rd International conference on computer Vision and pattern Analysis, ICCPA 2023

作者： Lian, Jing Yang, Xuefeng Niu, Xiaojiao Cao, Yuzhu Lanzhou Jiaotong University School of Electronic and Information Engineering No. 88 West Anning Road Gansu Lanzhou China

ISBN: (纸本)9781510667563

To address the problems of low segmentation accuracy and machine complexness of ancient pulse coupled neural network (PCNN) in medical image process, a Converged-FCMSPCNN (CFC-MSPCNN) model is projected. Compared with earlier PCNN models, this model additionally optimizes and enhances the synaptic weight matrix, link strength and dynamic threshold, simplifies the parameter settings and reduces the quantity of iterations. In addition, we add a balance parameter Q to regulate the dynamic threshold to improve the model's control over neuronal image processing. Through relevant experiments, we demonstrate that our algorithm has higher results compared with alternative algorithms to accurately section carcinoma lots and considerably reduces the randomness and unpredictability of firing neurons. © 2023 SPIE.

关键词： image segmentation

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Study on the Spatial pattern of Small Towns Based on computer image processing.and image recognition - Taking Guanhaiwei Town of Cixi as an Example

Study on the Spatial Pattern of Small Towns Based on Compute...

引用

2023 International conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Yu, Qiqi School of Tourism and Urban-Rural Planning Zhejiang Gongshang University Hangzhou310018 China

ISBN: (纸本)9781510668898

This paper uses GIS and Fragstats4.2 software to quantitatively analyze the landscape space of Guanhaiwei Town, and visualize the data combined with image processing.technology to enhance its spatial expression, analyze the hydrological characteristics of river corridors in Guanhaiwei Town, and the accessibility of traffic corridors, etc., and summarize the current problems. It is found that the construction land patches are concentrated on the traffic arteries and there are construction land disturbances within the woodland patches;the construction land is widely distributed, with scattered scattered points and irregular shapes. The woodland is mainly distributed in the south of the town, and the green patches in the town are less and the layout of green space is not reasonable enough, and the articulation with the surrounding landscape is weak. In the traffic land, the patches are highly fragmented and the landscape area is small. The patches of agricultural land are the landscape matrix, but their edges are highly fragmented, with small patches distributed sporadically and irregular in shape. The water patches were highly fragmented and no isolation zone was established. The study used GIS for image processing.and spatial analysis, which fully reflects the importance and application of computer technology in geographic research. © 2023 SPIE.

关键词： Geographic information systems

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Edge Guided Progressively Generative image Outpainting

Edge Guided Progressively Generative Image Outpainting

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Lin, Han Pagnucco, Maurice Song, Yang Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9781665448994

Deep-learning based generative models are proven to be capable for achieving excellent results in numerous image processing.tasks with a wide range of applications. One significant improvement of deep-learning approaches compared to traditional approaches is their ability to regenerate semantically coherent images by only relying on an input with limited information. This advantage becomes even more crucial when the input size is only a very minor proportion of the output size. Such image expansion tasks can be more challenging as the missing area may originally contain many semantic features that are critical in judging the quality of an image. In this paper we propose an edge-guided generative network model for producing semantically consistent output from a small image input. Our experiments show the proposed network is able to regenerate high quality images even when some structural features are missing in the input.

关键词： Measurement computer vision image edge detection Computational modeling image processing conferences Semantics

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MLP-GAN for Brain Vessel image Segmentation 48

MLP-GAN for Brain Vessel Image Segmentation

引用

48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Xie, Bin Tang, Hao Duan, Bin Cai, Dawen Yan, Yan Illinois Institute of Technology Department of Computer Science ChicagoIL United States ETH Computer Vision Lab Zurich Switzerland University of Michigan Department of Cell and Developmental Biology Ann ArborMI United States

ISBN: (纸本)9781728163277

Brain vessel image segmentation can be used as a promising biomarker for better prevention and treatment of different diseases. One successful approach is to consider the segmentation as an image-to-image translation task and perform a conditional Generative Adversarial Network (cGAN) to learn a transformation between two distributions. In this paper, we present a novel multi-view approach, MLP-GAN, which splits a 3D volumetric brain vessel image into three different dimensional 2D images (i.e., sagittal, coronal, axial) and then feed them into three different 2D cGANs. The proposed MLP-GAN not only alleviates the memory issue which exists in the original 3D neural networks but also retains 3D spatial information. Specifically, we utilize U-Net as the backbone for our generator and redesign the pattern of skip connection integrated with the MLP-Mixer [1] which has attracted lots of attention recently. Our model obtains the ability to capture cross-patch information to learn global information with the MLP-Mixer. Extensive experiments are performed on the public brain vessel dataset [2] that show our MLP-GAN outperforms other state-of-the-art methods. © 2023 ieee.

关键词： image segmentation

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Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in computer Vision Tasks for Robotic Grasping on the Edge

Fast GraspNeXt: A Fast Self-Attention Neural Network Archite...

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2023 ieee/CVF conference on computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Wong, Alexander Wu, Yifan Abbasi, Saad Nair, Saeejith Chen, Yuhao Shafiee, Mohammad Javad Vision and Image Processing Research Group University of Waterloo Canada Waterloo Artificial Intelligence Institute WaterlooON Canada DarwinAI Corp. WaterlooON Canada

ISBN: (纸本)9798350302493

Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architectural and computational complexity can result in poor suitability for deployment on embedded devices that are typically leveraged in robotic arms for real-world manufacturing and warehouse environments. As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments. Motivated by this, we propose Fast GraspNeXt, a fast self-attention neural network architecture tailored for embedded multi-task learning in computer vision tasks for robotic grasping. To build Fast GraspNeXt, we leverage a generative network architecture search strategy with a set of architectural constraints customized to achieve a strong balance between multitask learning performance and embedded inference efficiency. Experimental results on the MetaGraspNet benchmark dataset show that the Fast GraspNeXt network design achieves the highest performance (average precision (AP), accuracy, and mean squared error (MSE)) across multiple computer vision tasks when compared to other efficient multi-task network architecture designs, while having only 17.8M parameters (about >5× smaller), 259 GFLOPs (as much as >5× lower) and as much as >3.15× faster on a NVIDIA Jetson TX2 embedded processor. © 2023 ieee.

关键词： computer vision

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