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检索条件"任意字段=2024 International Conference on Computer Vision and Image Processing, CVIP 2024"

共 15414 条记录，以下是1-10 订阅

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international conference on computer vision and image processing, cvip 2024

International Conference on Computer Vision and Image Proces...

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2024 international conference on computer vision and image processing, cvip 2024

ISBN: (纸本)9781510688384

The proceedings contain 39 papers. The topics discussed include: a zero-reference nighttime road image enhancement method;SFPCDDFuse: an enhanced CDDFUSE infrared and visible image fusion model;recognition of side-scan sonar images under long-tail distribution;comparison of different masking strategies of MAE;real-time traffic sign recognition for driving: a hybrid approach integrating efficient Mamba models with dilated convolution;Mask R-CNN-based method for recognizing external breakage of transmission lines;light-aware luminance adaptive enhancement network for RGBT video object detection;and wireless multi-path VR audio and video file synchronous transmission technology based on two-way transmission.

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28th international conference on image processing, computer vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

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28th international conference on image processing, computer vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859328

The proceedings contain 28 papers. The special focus in this conference is on image processing, computer vision, and Pattern Recognition. The topics include: image-Based Seal Recognition: Approaches and Challenges in Current Automated Systems;Deep Learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM Data;silicon Wafer Map Defect Classification Using Artificial Intelligence Models;low Light image Enhancement Using Autoencoder-Based Deep Neural Networks;towards Elephants Intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view Applications in Hand-Drawn Sketch images;residential Real Estate image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color images;lalitha: A Hand Gesture-Based computer Control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with Intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional Neural Network (CNN) Based Algorithms;Early Detection of Lameness in Dairy Cattle Using Activity Data, image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with Data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and image processing Techniques;using Linkage Context for Automated Correction in Unsupervised Entity Resolution;the Rising Threat Against Modern Technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain Technology in the Transmission of Semiconductor Process Recipes;the Use of Social Media and the Internet for the Facilitation of Human Trafficking.

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Laser spot array detection for structured light calibration

Laser spot array detection for structured light calibration

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2024 international conference on computer vision and image processing, cvip 2024

作者： Jiang, Yubo Zhao, Zetong Shuai, Jiasheng Meng, Cai School of Astronautics Beihang University Beijing China Jiasheng Test Engineering Co. Ltd. Beijing China

ISBN: (纸本)9781510688384

Target detection and recognition are crucial research areas in computer vision, focusing on the identification of specific objects or targets within images or videos. This technology is vital for numerous real-world applications. In the engineering sector, Laser Spot Detection and Recognition Technology significantly enhances both productivity and accuracy. This research introduces a novel method for detecting and recognizing structured light array spots during calibration. The proposed technique leverages an adaptive threshold segmentation algorithm for image segmentation. It utilizes the energy distribution and positional relationships of the arrayed spots to accurately localize the spot array. Furthermore, it iteratively refines the position and distribution pattern of the spots using a planar projective relationship. This method can detect and identify regular arrays of light spots on calibration planes with varying reflectance and surface absorptivity, achieving detection accuracy up to 98% and localization accuracy at the sub-pixel level. © 2025 SPIE.

关键词： image segmentation

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SFPCDDFuse: an enhanced CDDFUSE infrared and visible image fusion model

SFPCDDFuse: an enhanced CDDFUSE infrared and visible image f...

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2024 international conference on computer vision and image processing, cvip 2024

作者： Yang, Tao Meng, Kun Si, Jiarui Tian, Hong Dalian Jiaotong University Liaoning Dalian116028 China

ISBN: (纸本)9781510688384

Multimodal image fusion integrates data from multiple sensors to produce richer and more informative images. One notable instance is the combination of visible and infrared pictures, which combines the fine-grained texture of a visible picture with the heat emission of an ir image. How well the image fusion works is extremely critical for the initial feature extraction. We have designed a SAFMB module that allows for better extraction of image features at the initial stage, i.e., separating the visible and infrared pictures separated both high- and low-frequency components. In the subsequent encoder part, the FPU-NET structure is used to obtain multiple feature layers for better fusion of up-sampling and down-sampling. To get fused infrared and visible maps, the picture is rebuilt from the new low and high frequency portions. The model was executed on the RoadScene dataset, improving standard metrics such as SD, EN and SSIM. © 2025 SPIE.

关键词： image fusion

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CAU-Net: U-Shaped medical image segmentation network based on compound attention

CAU-Net: U-Shaped medical image segmentation network based o...

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2024 international conference on computer vision and image processing, cvip 2024

作者： Liu, Jiaqi Mo, Jiaqing Xinjiang University School of Computer Science and Technology Key Laboratory of Signal Detection and Processing No.777 Huarui Street Shuimogou District Xinjiang Urumqi830017 China

ISBN: (纸本)9781510688384

In the context of the ongoing advancement of deep learning technologies, traditional image segmentation approaches are gradually become difficult for adapted to the requirements of medical imaging. The importance of medical image segmentation models is increasing, and they play a key role in accurately delineating anatomical structures and lesion regions. This is crucial for accurate diagnosis of diseases, formulation of treatment plans, and monitoring of disease course. In this paper, we propose a compound attention image segmentation model, CAU-Net, which aims to improve the segmentation accuracy of skin lesion images by introducing the receptive field module (RFB), the double squeezing-excitation (DSE) module, and our innovatively proposed compound attention (CA) module lesion image segmentation accuracy. The design of CAU-Net focuses on enhancing the feature extraction capability and contextual information capturing ability, and improving the edge segmentation capability and play a strong role in the processing of medical images with complex edges and chaotic backgrounds. The model reached an mDice to 0.9408 and a recall to 0.9418 on the ISIC 2018 dataset. Future work can further optimize these modules, extend to more medical image tasks. © 2025 SPIE.

关键词： image segmentation

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Daytime overexposed image processing for autonomous driving based on improved multi-scale Retinex with color restoration

Daytime overexposed image processing for autonomous driving ...

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2024 international conference on computer vision and image processing, cvip 2024

作者： Abuduaini, Subi University of Electronic Science and Technology Qingshui River Campus 2006 Xiyuan Avenue Hi-tech West District Sichuan Province Chengdu City China

ISBN: (纸本)9781510688384

In situations where vehicles are traveling against the light during midday, objects that need to be identified can become obscured by the glare, making it challenging for autonomous driving systems to accurately recognize them. Research on the processing of overexposed images in such scenarios remains relatively limited. Using the Road Vehicle images Dataset, we conducted experiments to assess the performance of SSR, MSR, and MSRCR in handling overexposed images, with MSRCR yielding the best results. Building on MSRCR, we further enhanced the images by applying gamma correction. First, we simulated overexposure, then applied MSRCR to recover the image details, followed by gamma correction to adjust the brightness of the enhanced images. The results were evaluated using SSIM and PSNR metrics. The average SSIM value exceeded 70%, and the average PSNR value was above 10. Compared to images processed with MSRCR alone, the addition of gamma correction resulted in a 5% improvement in SSIM and an increase of approximately 3 decibels in PSNR. © 2025 SPIE.

关键词： image enhancement

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SparseNetYOLOv8: Integrating vision transformers and dynamic probing for enhanced sparse object detection

SparseNetYOLOv8: Integrating vision transformers and dynamic...

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2024 international conference on computer vision and image processing, cvip 2024

作者： Wang, Lurui Lu, Yanfeng Mechanical Engineering Department University of Toronto TorontoM5T1R5 Canada Beijing100190 China

ISBN: (纸本)9781510688384

In this study, we propose SparseNetYOLOv8, an improved YOLOv8n model for small-object detection in sparse space. The main changes to the original architecture were incorporating GhostConv, CBAM, MobileViTBlock, and DWC within the Backbone, and also BiFPN and DySnakeConv in the Neck for improved feature fusion and edge detection respectively. These improvements together give a 10.6% mAP@0.5 then a mAP@0.5-0.95 increase of 7.8%and SAHI in Head which minimizes target omissions. These enhancements collectively yield a 10.6% improvement in mAP@0.5 and a 7.8% increase in mAP@0.5–0.95, while SAHI in the Head minimizes target omissions. Experimental results demonstrate the robustness of SparseNetYOLOv8 in comparison with other YOLO variants. © 2025 SPIE.

关键词： Distribution transformers

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Binocular vision measurement of robot end position based on orthogonal iteration to suppress covariance error

Binocular vision measurement of robot end position based on ...

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2024 international conference on computer vision and image processing, cvip 2024

作者： Xu, Jingjing Deng, Xiping School of Artificial Intelligence Anhui Polytechnic University Wuhu China Zhejiang Tengen Electric Co. Ltd. Wenzhou China

ISBN: (纸本)9781510688384

Aiming at the problem of insufficient accuracy of existing robot position measurement, a binocular vision measurement method of robot end position based on orthogonal iteration to suppress the covariance error is studied. Firstly, a binocular measurement system is built, a binocular measurement model is derived, and the model is calibrated;secondly, a cooperative logo for measuring the robot position is designed, and the coordinates of the logo feature points in the image space are extracted by using sub-pixel straight line fitting;then, the feature points are passed through the binocular measurement model, and the spatial position of the feature points under the camera coordinate system is calculated, and the orthogonal iteration is used to suppress the conversion error and improve the settlement accuracy. Finally, the positional solution of the robot end is realized. Taking the six-degree-of-freedom robot as the experimental object, the end position measurement experiments are carried out, and the results show that the average error of the method in this paper for measuring the robot's end position is reduced from 0.165mm to 0.123mm, and the average errors of the posture in the X, Y, and Z directions are 0.135°, 0.194°, and 0.173°. © 2025 SPIE.

关键词： Robot vision

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Real-Time traffic sign recognition for driving: a hybrid approach integrating efficient Mamba models with dilated convolution

Real-Time traffic sign recognition for driving: a hybrid app...

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2024 international conference on computer vision and image processing, cvip 2024

作者： Ma, Shibo Li, Wenxuan Department of Computer Science Wenzhou Kean University Wenzhou325060 China

ISBN: (纸本)9781510688384

The rapid advancement of autonomous driving technologies necessitates efficient and reliable traffic sign recognition systems, especially under the constraints of limited computational resources and real-time decision-making demands. This paper proposes a novel hybrid model, integrating Dilated Convolutional Neural Networks with an Efficient Mamba framework to address these challenges. The proposed system leverages pre-processing techniques, such as noise reduction through Gaussian or median filters, to minimize false detections. A selective state space model further refines the recognition process, while the Efficient Mamba architecture, optimized for low-resource environments, combines lightweight neural networks with CNN-based feature extraction. Our approach is evaluated on the GTSRB dataset, focusing on accuracy, precision, recall, and F1 score, with a particular emphasis on prediction latency to meet real-time operational requirements. Comprehensive experiments, including k-fold cross-validation, demonstrate the superior efficiency and effectiveness of our model compared to baseline methods. © 2025 SPIE.

关键词： Median filters

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A zero-reference nighttime road image enhancement method

A zero-reference nighttime road image enhancement method

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2024 international conference on computer vision and image processing, cvip 2024

作者： Xu, Kun Zhang, Yeran Li, Jiahao Cheng, Xin Liu, Zhanwen School of Information Engineering Chang'an University Xi’an710018 China

ISBN: (纸本)9781510688384

The majority of existing low-light image enhancement methods are based on uniform low illumination, they are prone to issues such as overexposure, dark area noise amplification, when applied to nighttime road images with various lighting and high noise levels. This paper proposes a zero-reference two-stage nighttime road image enhancement method. In the first stage, a lightweight visual attention network (LVAN) is developed to generate the dark-aware attention map, effectively avoiding the problems of overexposure and underexposure. The second stage is enhanced with a zero-reference depth image enhancement network in CIELab color space (ZeroDIE_Lab), which enhance image brightness significantly, suppresses noise and artefacts effectively while maintaining color consistency. In comparison with established low-light image enhancement approaches, such as Zero-DCE, the experimental results demonstrate that the proposed method not only enhances the visual clarity and structural details of the image, but also exhibits notable advantages in quantitative evaluation metrics, including image quality assessment and computational complex. © 2025 SPIE.

关键词： attention map CIELab color space lightweight nighttime road images zero-reference

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