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检索条件"任意字段=Conference on Real-Time Image and Video Processing 2011"

共 8702 条记录，以下是331-340 订阅

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7th International conference on Computer Vision and image processing, CVIP 2022

7th International Conference on Computer Vision and Image Pr...

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7th International conference on Computer Vision and image processing, CVIP 2022

ISBN: (纸本)9783031314063

The proceedings contain 54 papers. The special focus in this conference is on Computer Vision and image processing. The topics include: CT image Synthesis from MR image Using Edge-Aware Generative Adversarial Network;Modified Scaled-YOLOv4: Soccer Player and Ball Detection for real time Implementation;candidNet: A Novel Framework for Candid Moments Detection;cost Efficient Defect Detection in Bangle Industry Using Transfer Learning;single image Dehazing Using Multipath Networks Based on Chain of U-Nets;leveraging Tri-Planar Views and Weighted Average Fusion Technique to Classify Lung Nodule Malignancy;a Bayesian Approach to Gaussian-Impulse Noise Removal Using Hessian Norm Regularization;deepTemplates: Object Segmentation Using Shape Templates;Data-Centric Approach to SAR-Optical image Translation;MIS-Net: A Deep Residual Network Based on Memorised Pooling Indices for Medical image Segmentation;linear and Non-Linear Filter-based Counter-Forensics Against image Splicing Detection;Ischemic Stroke Lesion Segmentation in CT Perfusion images Using U-Net with Group Convolutions;Multi-generator MD-GAN with Reset Discriminator: A Framework to Handle Non-IID Data;video Colorization Using Modified Autoencoder Generative Adversarial Networks;real-time Violence Detection Using Deep Neural Networks and DTW;skin Disease Detection Using Saliency Maps and Segmentation Techniques;an Alternate Approach for Single image Haze Removal Using Path Prediction;Detecting Tropical Cyclones in INSAT-3D Satellite images Using CNN-Based Model;Two Stream RGB-LBP Based Transfer Learning Model for Face Anti-spoofing;Logarithmic Progressive-SMOTE: Oversampling Minorities in Retinal Fundus Multi-disease image Dataset;HD-VAE-GAN: Hiding Data with Variational Autoencoder Generative Adversarial Networks;sequence Recognition in Bharatnatyam Dance;Multi-modality Fusion for Siamese Network Based RGB-T Tracking (mfSiamTrack).

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A New Era in Railway Track Inspection: Drone based image processing integrated with IoT 10

A New Era in Railway Track Inspection: Drone based Image pro...

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10th International conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Mortezaei, Alireza Mirahmadi, Seyed Shayan Derakhshan, Farnaz Qazvin Iran

ISBN: (纸本)9798350348873

In contemporary times, with the increasing demand for rail transportation, the preservation of the integrity and overall condition of railways holds paramount importance. This is because any damage to these routes can lead to irreparable disasters. Methods such as human inspecting, due to the drawbacks it entails, is not optimal nowadays. On the other hand, more modern approaches, such as installing cameras on trains and carts, face limitations like blocking the path and being dependent on train movement, which constrains the freedom of inspector movement. In the proposed method of this paper, the assessment of railway health is conducted through the processing of video footage captured by a camera mounted on a quadcopter, also known as a drone. In this method, the drone employs LiDAR technology for environmental scanning, which is performed to maintain a consistent altitude and protect the drone from external hazards. Drone-based image processing technique, combined with an understanding of the operational environment, allows for a considerably more cost-effective, accurate, and, of course, quicker assessment of the railway tracks health. However, factors such as lack of inter-drone communication for enhanced coordination, absence of data archiving, unavailability of missions history, and periodic health status reporting are considered weaknesses that challenge the scalability of this method on a larger scale with various complexities. Some of the key points of this study is about accessing real-time data about each drone during the mission. Moreover, within the dedicated profile of each drone developed within the central control system, detailed information on all completed missions, identified damaged points on railways, and periodic health reports for each drone are provided. With this process, the management of railway line health and safety will be significantly more straightforward and adaptable through the use of this new method. ©2024 IEEE.

关键词： Fault detection

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Proceedings of the 22nd International conference on image Analysis and processing, ICIAP 2023

Proceedings of the 22nd International Conference on Image An...

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Proceedings of the 22nd International conference on image Analysis and processing, ICIAP 2023

ISBN: (纸本)9783031431470

The proceedings contain 92 papers. The special focus in this conference is on image Analysis and processing. The topics include: An Effective CNN-Based Super Resolution Method for video Coding;medical Transformers for Boosting Automatic Grading of Colon Carcinoma in Histological images;FERMOUTH: Facial Emotion Recognition from the MOUTH Region;consensus Ranking for Efficient Face image Retrieval: A Novel Method for Maximising Precision and Recall;towards Explainable Navigation and Recounting;towards Facial Expression Robustness in Multi-scale Wild Environments;depth Camera Face Recognition by Normalized Fractal Encodings;automatic Generation of Semantic Parts for Face image Synthesis;improved Bilinear Pooling for real-time Pose Event Camera Relocalisation;continual Source-Free Unsupervised Domain Adaptation;End-to-End Asbestos Roof Detection on Orthophotos Using Transformer-Based YOLO Deep Neural Network;OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data;UAV Multi-object Tracking by Combining Two Deep Neural Architectures;GLR: Gradient-Based Learning Rate Scheduler;a Large-scale Analysis of Athletes’ Cumulative Race time in Running Events;uncovering Lies: Deception Detection in a Rolling-Dice Experiment;active Class Selection for Dataset Acquisition in Sign Language Recognition;MC-GTA: A Synthetic Benchmark for Multi-Camera Vehicle Tracking;a Differentiable Entropy Model for Learned image Compression;learning Landmarks Motion from Speech for Speaker-Agnostic 3D Talking Heads Generation;self-Similarity Block for Deep image Denoising;SCENE-pathy: Capturing the Visual Selective Attention of People Towards Scene Elements;not with My Name! Inferring Artists’ Names of Input Strings Employed by Diffusion Models;benchmarking of Blind video Deblurring Methods on Long Exposure and Resource Poor Settings;LieToMe: An LSTM-Based Method for Deception Detection by Hand Movements;spatial Transformer Generative Adversarial Network for image Super

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7th International conference on Computer Vision and image processing, CVIP 2022

7th International Conference on Computer Vision and Image Pr...

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7th International conference on Computer Vision and image processing, CVIP 2022

ISBN: (纸本)9783031314162

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Pedestrian Detection and Tracking for Driver Assistance Systems 3

Pedestrian Detection and Tracking for Driver Assistance Syst...

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3rd International conference for Advancement in Technology, ICONAT 2024

作者： Muddebihal, Amaresh Chandru, P. Nisanth, V. Babu, Tina Alliance University Department of Information Technology ACED Bengaluru India Alliance University Bengaluru Department of Computer Science and Engineering ACED India Alliance University Dept of Electronics and Communication ACED Bengaluru India

ISBN: (纸本)9798350354171

Pedestrian detection and tracking, particularly in low-light conditions, is critical for ensuring road safety, especially for vulnerable populations like senior citizens. Traditional methods often struggle with multiple moving objects and poor visibility, leading to increased noise and false alarms. To address these challenges, this project proposes a novel approach combining a wavelet-based contrast change detector and locally adaptive thresholding for real-time detection and tracking of multiple moving objects during night-time driving scenarios. The initial phase utilizes a contrast change detector to identify potential moving objects by detecting local changes in contrast over time. False alarms are suppressed using motion prediction and spatial nearest neighbor data association. Furthermore, a sophisticated change detection algorithm is employed to segment video sequences effectively, minimizing noise and enhancing the quality of outputs. Incorporating the latest advancements, such as the YOLO V8 module, this project focuses on leveraging deep learning techniques for pedestrian detection. The YOLO V8 module, a state-of-the-art variant of the YOLO series, is utilized to enhance accuracy and runtime efficiency. By harnessing a Large-Field Of-View (LFOV) deep network approach, the project achieves high detection accuracy while processing larger image areas at faster speeds compared to traditional deep networks. © 2024 IEEE.

关键词： image segmentation

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real-time VL/IR video fusion leveraging multiple VL apertures on resource-constrained computing platform 48

Real-time VL/IR video fusion leveraging multiple VL aperture...

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conference on Infrared Technology and Applications XLVIII Part of SPIE Defense and Commercial Sensing conference

作者： Svagzdys, Anna Demar, Richard W. Hewitt, Gregory F. S. Bruneau, Mark Deep Analyt LLC 56 Coll StSuite 201 Montpelier VT 05602 USA

ISBN: (纸本)9781510650916;9781510650909

We present an approach to automatic visible light/infrared (VL/IR) image registration that leverages multiple visible light apertures for fast computation on resource-constrained systems. VL/IR registration is computationally challenging due to the different modalities of image generation. Although feature-based algorithms for direct registration exist, these methods proved too complex to reliably perform registration on low-cost, embedded processors in real time. We instead employed a second VL camera to dynamically estimate 2D translations aligning the brightest (warmest) objects in the IR video stream with their counterparts in the first VL video stream. Regions of interest are first selected based on the brightest areas in the IR image, as our application is primarily concerned with detecting objects warmer than background. The same broad region - e.g. the lower-left quadrant of the frame - is then selected in the VL1 and VL2 images. The translation that best registers the first VL ROI to the second is estimated through template matching. Because all apertures in our camera system are fixed and coplanar relative to one another, the translation that best aligns the IR ROI to the VL1 ROI can be estimated from the translation from the VL2 ROI to the VL1 ROI. This approach provides dynamic registration of 1080P video at upwards of 10Hz on an ODROID-XU4 single-board computer, while also allowing the processor time to render the IR-augmented video stream at 20Hz. imagery collected using Deep Analytics' IR Boom Camera will be presented to demonstrate the approach.

关键词： image registration infrared imaging image fusion image processing

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16th International Workshop on Design and Architectures for Signal and image processing, DASIP 2023

16th International Workshop on Design and Architectures for ...

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16th International Workshop on Design and Architectures for Signal and image processing, DASIP 2023

ISBN: (纸本)9783031299698

The proceedings contain 9 papers. The special focus in this conference is on Design and Architectures for Signal and image processing. The topics include: Brain Blood Vessel Segmentation in Hyperspectral images Through Linear Operators;Neural Network Predictor for Fast Channel Change on DVB Set-Top-Boxes;AINoC: New Interconnect for Future Deep Neural Network Accelerators;real-time FPGA Implementation of the Semi-global Matching Stereo Vision Algorithm for a 4K/UHD video Stream;TaPaFuzz - An FPGA-Accelerated Framework for RISC-V IoT Graybox Fuzzing;Adaptive Inference for FPGA-Based 5G Automatic Modulation Classification;High-Level Online Power Monitoring of FPGA IP Based on Machine Learning.

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EMOD: Efficient Moving Object Detection via image Eccentricity Analysis and Sparse Neural Networks

EMOD: Efficient Moving Object Detection via Image Eccentrici...

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23rd IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Li, Xiaomin Nabati, Ramin Singh, Kunjan Corona, Enrique Metsis, Vangelis Parchami, Armin Texas State Univ San Marcos TX 78666 USA Ford Motor Co Dearborn MI 48121 USA

ISBN: (纸本)9798350320565

This paper proposes an efficient moving objects detection pipeline focusing on dynamic object detection on video streams captured by traffic monitoring cameras. While developing autonomous vehicle systems, we found that views from self-driving vehicles can be occluded by dynamic or static objects on the street. Whereas infrastructure nodes such as traffic monitoring cameras having broader fieldof-views and better perspectives can be used as auxiliary sensors to share traffic information with nearby self-driving cars in real-time. However, these infrastructure cameras usually have constrained computation resources, and detecting hundreds of static background objects in consecutive video frames is wasteful. In our detection pipeline, we leverage the image eccentricity analysis as a pre-processing step to fast generate moving objects segmentation maps. These maps are used to mask the original images to get images that only contain the moving objects in the scene. These sparse images are then passed to an object detection model built with a sparse convolution backbone network, resulting in significant reduction in computational costs. Our quantitative experiments illustrate that the proposed detection pipeline can achieve up to 50% inference speedup with negligible detection accuracy drop in images obtained from traffic monitoring cameras.

关键词： Convolution Computational modeling Pipelines Object detection Object segmentation Streaming media Cameras

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Research on the lane line detection method based on YOLOv5 5

Research on the lane line detection method based on YOLOv5

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5th International conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Gu, Xiang Shu, Wenjie School of Computer and Information Engineering Nantong Institute of Technology Jiangsu Nantong China School of Information Science and Technology Nantong University Jiangsu Nantong226001 China

ISBN: (纸本)9781510668621

Lane-line detection is one of the key technologies in the field of intelligent driving. This paper changes the anchor box regression loss function of the YOLOv5s to the EIoU function to construct a kind of lane-line detection model. The experimental results show that the improved detection model is more accurate in lane-line detection. The improved model improved mAP by about 11.1% compared to the conventional YOLOv5 model. At the same time, it can solve the problems of traditional models such as low real-time performance and underfitting to curved lane lines. © 2023 SPIE. All rights reserved.

关键词： Education and training Object detection Data modeling Detection and tracking algorithms image processing Deep learning Performance modeling Roads video video acceleration

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Achieving Effective Artifact Subspace Reconstruction in EEG Using real-time video-Based Artifact Identification

Achieving Effective Artifact Subspace Reconstruction in EEG ...

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2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Kang, Sunghyun Won, Kyungho Kim, Heegyu Baek, Jihoon Ahn, Minkyu Jun, Sung Chan Gwangju Korea Republic of School of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of Gwangju Korea Republic of

ISBN: (纸本)9798350337020

Identifying and minimizing physiological artifacts in EEG is challenging because these artifacts may corrupt the underlying brain activity severely. In this work, we proposed a hybrid approach to detect/reduce EEG artifacts by combining the MediaPipe face mesh model and artifact subspace reconstruction (ASR). Four types of artifacts, eye blinking, horizontal/vertical eye movements, and jaw movement during EEG measurement were generated to test our approach. We observed that real-time video-based artifact identification achieved over 95% accuracy in detecting eye blinking, horizontal eye movement, and jaw movement. Moreover, the targeted noise reduction was effective in analyzing the signal-to-noise ratio (SNR) for each specific artifact. This work may contribute to improving the reliability and accuracy of EEG data analysis in the real-world and online scenarios by providing a practical and effective approach to identifying and reducing physiological artifacts in real-time. © 2023 IEEE.

关键词： artifact detection artifacts electroencephalography (EEG) image processing

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