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检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

共 5156 条记录，以下是211-220 订阅

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Online vision-Based 3D Reconstruction for Shared Human-Robot Workspaces 4

Online Vision-Based 3D Reconstruction for Shared Human-Robot...

引用

4th international conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Keller, Philip Rivera, Genessis Perez Schnell, Tristan Dillmann, Rudiger FZI Forschungszentrum Informatik Karlsruhe Germany

ISBN: (纸本)9798350391183

In applications where robot arms collaborate with humans, the corresponding shared workspaces are very dynamic and not as predictable as purely robotic ones. Unknown obstacles, such as arbitrary boxes or tools placed by humans, might appear at any time and require the robot to react accordingly. Often, expensive Lidar-or depth-sensors are applied in these scenarios. Instead, we propose an approach to check for obstacles and prevent potential collisions in the corresponding workspace that is entirely based on less expensive RGB cameras. the workspace is monitored with an extrinsically calibrated multi-view camera setup. Our proposed hybrid approach combines learning-based feature matching and traditional image processing methods to achieve online 3D reconstruction. Even less textured objects and surfaces can be perceived and modelled as simplified collision objects. We present several experiments and show the effectiveness of learning-based feature matching and the advantages of combining them with accurate computer vision methods. Further, we show that our approach runs in near real-Time on our demonstrator using a multi-view setup with three cameras. © 2024 IEEE.

关键词： Stereo image processing

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Borrowing from yourself: Faster future video segmentation with partial channel update 26

Borrowing from yourself: Faster future video segmentation wi...

引用

26th international conference on pattern recognition / 8th international Workshop on image Mining - theory and Applications (IMTA)

作者： Courdier, Evann Fleuret, Francois Idiap Res Inst EPFL Martigny Switzerland Univ Geneva Idiap Res Inst Geneva Switzerland

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Semantic segmentation is a well-addressed topic in the computer vision literature, but the design of fast and accurate video processing networks remains challenging. In addition, to run on embedded hardware, computer vision models often have to make compromises on accuracy to run at the required speed, so that a latency/accuracy trade-off is usually at the heart of these real-time systems' design. For the specific case of videos, models have the additional possibility to make use of computations made for previous frames to mitigate the accuracy loss while being real-time. In this work, we propose to tackle the task of fast future video segmentation prediction through the use of convolutional layers with time-dependent channel masking. this technique only updates a chosen subset of the feature maps at each time-step, bringing simultaneously less computation and latency, and allowing the network to leverage previously computed features. We apply this technique to several fast architectures and experimentally confirm its benefits for the future prediction subtask.

关键词： Heart computer vision Computational modeling Semantic segmentation Modulation Streaming media Predictive models

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Real-Time Detection of Wrong-Way Vehicles Using YOLO and image processing Techniques 4

Real-Time Detection of Wrong-Way Vehicles Using YOLO and Ima...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Celin, J. Jeya A Jeswin, J. Faizaan, A. Mohamed Babu, A.P. Chandru Anisto, M. Arul Kalasalingam Academy of Research and Education Department of Information Technology Tamil Nadu Virudhunagar India

ISBN: (纸本)9798331523923

Due to delayed notice and incorrect identification, wrong-way driving is a major traffic safety concern that frequently results in accidents. We describe a computer vision and deep learning based real-time wrong-way vehicle identification system to tackle these issues. To identify cars in video footage and follow their movements within predetermined zones, the system makes use of the YOLO (You Only Look Once) algorithm. It correctly detects instances of driving in the incorrect direction by analysing direction, and it promptly issues warnings by sending out email notifications with pictures of the vehicles and sounding an alert. According to evidence from experiments, the system is a useful tool for increasing road safety because it improves the efficiency of detection, decreases error rates, and speeds up response times. © 2025 IEEE.

关键词： Deep learning

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Ensemble Learning with Mixup Style for Ultrasound image Classification 4th

Ensemble Learning with Mixup Style for Ultrasound Image Clas...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Alblwi, Abdalrahman Barner, Kenneth E. Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9783031821493;9783031821509

Early detection of breast cancer has become an essential medical procedure to reduce mortality rates in patients. Although various deep learning methods enhance cancer analysis and detection, including feature extraction techniques, transfer learning, image fusion, and data augmentation, challenges such as limited data and class imbalance impede progress in early cancer detection. this study introduces a new approach that employs a dual ensemble learning and MixUp data techniques to simultaneously (1) tackle unbalanced classes and (2) improve diversity in ultrasound images. the proposed training methodology involves various strategies, integrating original and augmented datasets to maintain model training and increase performance. through comprehensive evaluations using benchmark breast ultrasound images (BUSI), the proposed approach demonstrates a substantial improvement and feasibility in the multi-classification of breast cancer.

关键词： Ensemble learning Breast cancer Class imbalance, Multi-Classification MixUp

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Intelligent tunnel fire emergency linkage measures based on image recognition 4

Intelligent tunnel fire emergency linkage measures based on ...

引用

4th international conference on computer vision and Data Mining, ICCVDM 2023

作者： Jin, Yanjun Wu, Futao Zhu, Xihao Yu, Hancheng Xia, Minyao Liu, Haiping Zhejiang Institute of Mechanical & Electrical Engineering Zhejiang China

ISBN: (纸本)9781510674448

An accurate and fast fire smoke detection algorithm is urgently needed to solve the emergency linkage measures to prevent early fire spread and after the fire. In this paper, the depth matrix of motion difference is constructed for background modeling, the moving target is extracted using flame smoke motion characteristics, and the suspected area of flame smoke is obtained through corrosion and expansion. the unique characteristics of flame and smoke are then extracted, and the smoke flame recognition model is built using a BP neural network optimized by a genetic algorithm. the experimental results show that the algorithm can reliably eliminate the interference of moving vehicles and lights in the tunnel, detect flames and smoke, and generate an alarm, and that it can be applied to fire smoke detection in real-world scenes. Finally, the emergency linkage measures in the corresponding area are automatically initiated based on the position of the fire point via information interaction with the fire smoke detection and alarm system. © 2024 SPIE.

关键词： Smoke

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Hierarchical Unified Spectral-Spatial Aggregated Transformer for Hyperspectral image Classification 26

Hierarchical Unified Spectral-Spatial Aggregated Transformer...

引用

26th international conference on pattern recognition / 8th international Workshop on image Mining - theory and Applications (IMTA)

作者： Zhou, Weilian Kamata, Sei-Ichiro Luo, Zhengbo Chen, Xiaoyue Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Japan

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

vision Transformer (ViT) has recently been introduced into the computer vision (CV) field with its self-attention mechanism and gotten remarkable performance. However, simply applying ViT for hyperspectral image (HSI) classification is not applicable due to 1) ViT is a spatial-only self-attention model, but rich spectral information exists in HSI;2) ViT needs sufficient training samples, but HSI suffers from limited samples;3) ViT does not well learn local features;4) multi-scale features for ViT are not considered. Furthermore, the methods which combine convolutional neural network (CNN) and ViT generally suffer from a large computational burden. Hence, this paper tends to design a suitable pure ViT based model for HSI classification as the following points: 1) spectral-only vision transformer with all tokens' aggregation;2) spatial-only local-global transformer;3) cross-scale local-global feature fusion, and 4) a cooperative loss function to unify the spectral and spatial features. As a result, the proposed idea achieves competitive classification performance on three public datasets than other state-of-the-art methods.

关键词： Training computer vision Computational modeling Transformers pattern recognition Convolutional neural networks Information exchange

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New Energy Prediction Method under the Influence of Meteorological Factors Based on Generative AI 4

New Energy Prediction Method under the Influence of Meteorol...

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4th international conference on Energy Engineering and Power Systems, EEPS 2024

作者： Gao, Linqi Yu, Qiang School of Computer and Information Engineering Tainjin Normal University Tianjin China Tianjin data management center Tianjin China

ISBN: (纸本)9798350366914

New energy prediction is an important issue in energy management and optimization. By analyzing the difficulties of new energy prediction under the influence of meteorological factors and the advantages of generative AI technology in the field of new energy prediction, a new energy prediction method based on generative AI is proposed. the innovations of proposed method are integrating natural language processing and numerical prediction, fine-tuning the ChatGPT pre-training model and complex pattern recognition. the mothed can improve the accuracy and timeliness of new energy prediction. © 2024 IEEE.

关键词： Prediction models

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Survey on Advanced Techniques for Cardiac Arrest Prediction Using Machine Learning 4

Survey on Advanced Techniques for Cardiac Arrest Prediction ...

引用

4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Periasamy, J.K. Raagavi, K. Tarun Ashwanth, P. Sri Sai Ram Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Cardiac arrest has been a leading cause of mortality worldwide, with limited opportunities for intervention. this project introduces a novel machine-learning approach to predict and process cardiac arrest risk in high-risk patients. Our system integrates 1. Advanced signal processing techniques for ECG analysis 2. Deep learning algorithms for pattern recognition 3. Real-time monitoring and alert system. By analyzing ECG data, medical history, and vital signs, our system provides personalized risk scores, enabling early intervention and improving patient outcomes. Our approach has the potential to revolutionize cardiac arrest prediction, saving lives and reducing healthcare costs. © 2024 IEEE.

关键词： Electrocardiography

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Exploring the Capabilities of GPT4-vision as OCR Engine 28th

Exploring the Capabilities of GPT4-Vision as OCR Engine

引用

28th international conference on Linking theory and Practice of Digital Libraries (TPDL)

作者： Ghiriti, Alex Goderle, Wolfgang Kern, Roman Graz Univ Technol Inst Interact Syst & Data Sci Graz Austria Univ Innsbruck Hist Inst Dept Austrian Hist Innsbruck Austria Max Planck Inst Geoanthropol Dept Struct Changes Technosphere Jena Germany Know Ctr Graz Austria

ISBN: (纸本)9783031724398;9783031724404

Many museums and libraries conducted efforts to digitize their assets, and many historic documents are now available as digital images. However, these documents are not directly accessible to retrieval systems that rely on written text and not images. In this study, the novel GPT4-vision is being studied for its ability of optical character recognition (OCR), in cases where established methods, such as Tesseract may have difficulties. We find that GPT4-vision provides excellent results even in cases where even humans struggle. We also identified a number of key limitations, including the long runtime implying high energy requirements, the lack of handling of rotated images, the necessity for layout hints, and limitations regarding image size. Even with these limitations, it is expected that large language models and vision transformers will play an important role to make historical documents more accessible for further processing, or directly to users.

关键词： OCR Historic Documents vision Transformer. GPT4-vision Tesseract Comparison OCR Benchmark

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image Fusion Based on Memristive Neural Network 4

Image Fusion Based on Memristive Neural Network

引用

4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Ding, Huan Li, Tong Liu, Zelong Feng, Ziwei School of Electronic Engineering Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9798350326444

image fusion aims to integrate complementary information of images collected by multiple source channels into a single fusion image. It can extract the favorable information in each channel to the maximum extent and generate high quality image. this technology is widely used in various fields, among which the neural network is the most widely used. However, there are still many problems to be solved, such as the removal of redundant information and the retention of effective information. this paper proposes to add memristors on the basis of pulse coupled neural network to realize the adaptive change of neural network parameters, which breaks the shackles of traditional fixed parameters. this allows for better image fusion. For the two processing results, this paper combines the fusion image and grid sampling graph to analyze the processing effect. the results of pulse coupled neural network and memristive neural network were compared and analyzed from three aspects: information entropy, spatial frequency and average gradient. the experimental results show that the image fusion using memristive neural network is better and the resulting image is clearer. the feasibility and superiority of memristive neural network are verified. © 2023 IEEE.

关键词： image fusion

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