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检索条件"任意字段=IEEE Visual Communications and Image Processing Conference"

共 25558 条记录，以下是411-420 订阅

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LED Strip Quality Detection Based on OpenCV 22

LED Strip Quality Detection Based on OpenCV

引用

22nd International conference on Optical communications and Networks, ICOCN 2024

作者： Liu, Hao Huang, Qihao Liu, Honglin Lang, Tingting College of Optoelectronic Technology China Jiliang University Hangzhou China Hangzhou Leaper Technology Co. Ltd Hangzhou China

An image processing algorithm for real-time examination of LED light strips is proposed, which enables quick detection of blind LED beads in strips. It is successfully used in production line to replace manual inspect... 详细信息

ISBN: (纸本)9798350367652

关键词： image processing

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AG-Mono: A Monocular Dataset for Unmanned Air Vehicles 30

AG-Mono: A Monocular Dataset for Unmanned Air Vehicles

引用

30th ieee Signal processing and communications Applications conference (SIU)

作者： Simsek, Bugra Giden, Ibrahim Halil Bilge, Hasan Sakir ASELSAN Girisimcilik Merkezi Ankara Turkey Gazi Univ Ankara Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450928

One of the most important information needed while performing unmanned aerial vehicles (UAV) operations is about the platform location and the environment. Such platforms mostly use GNSS signals outdoors. However, in indoor areas where GNSS signals cannot be received or in situations where signals are jammed, it is not possible to obtain location information using these signals. For that reason, alternative navigation systems have become so crucial. One of the most preferred systems among navigation technologies is the visual simultaneous localization and mapping (vSLAM) method performed using RGB cameras on the UAVs. In this study, an open monocular image dataset called AG-Mono was created and published online to test the performance of vSLAM algorithms. This dataset was created at three different exposure times using a handheld platform, and it includes video sequences at 640x480 image resolution. The experimental area where the images were created is a closed corridor with 16.5 x 4.5 meters and four sharp corners.

关键词： Unmanned Aerial Vehicles (UAVs) visual SLAM (vSLAM) Monocular image Dataset

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PTS-LIC: Pruning Threshold Searching for Lightweight Learned image Compression

PTS-LIC: Pruning Threshold Searching for Lightweight Learned...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Luo, Ao Sun, Heming Liu, Jinming Lin, Fangzheng Katto, Jiro Yokohama National University Faculty of Engineering Kanagawa Japan Shanghai Jiao Tong University China Waseda University Department of Computer Science and Communication Engineering Tokyo Japan Waseda Research Institute for Science and Engineering Tokyo Japan

ISBN: (纸本)9798350359855

Learned image Compression (LIC), which uses neural networks to compress images, has experienced significant growth in recent years. The hyperprior-module-based LIC model has achieved higher performance than classical codecs. However, the LIC models are too heavy (in calculation and parameter amounts) to apply to edge devices. To solve this problem, some former papers focus on structural pruning for LIC models. However, they either cause noticeable performance decrement or neglect the appropriate pruning threshold for each LIC model. These problems keep their pruning results sub-optimal. This paper proposes a Pruning Threshold Searching on the hyperprior module for different-quality LIC models. Our method removes most parameters and calculations while keeping the performance the same as the models before pruning. We removed at least 49.8% of parameters and 28.5% of calculations for the Channel-Wise-Context-Model-based models and 29.1% of parameters for the Cheng-2020 models. © 2023 ieee.

关键词： image compression

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Filtering Methods, Assessment Criteria, and Prospects for image Denoising

Filtering Methods, Assessment Criteria, and Prospects for Im...

引用

2025 ieee International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Wandile, Piyush S Singh Kirar, Bhupendra Kirar, Seema Indian Institute of Information Technology Computer Science and Engineering Bhopal India Indian Institute of Information Technology Electronics and Communications Engineering Bhopal India Bansal Institute of Science and Technology Electronics and Communications Engineering Bhopal India

ISBN: (纸本)9798331529833

image denoising is a crucial step in image acquisition and processing that helps improve the image quality by removing the unwanted noise. In this paper Gaussian and median filters are used as denoiser and performance is evaluated using some of the key metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Feature Similarity Index Measure (FSIM), Structural Similarity Index Measure (SSIM), Mean Absolute Error (MAE), and Correlation Coefficient. A denoiser with low MSE, high PSNR, values of 1 for SSIM, FSIM, and Correlation Coefficient. Reported results are better in comparison to existing for better image quality. © 2025 ieee.

关键词： Wiener filtering

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A multi-modal feature fusion-based approach for Chest X-ray Report Generation 11

A multi-modal feature fusion-based approach for Chest X-ray ...

引用

11th International conference on Wireless Networks and Mobile communications (WINCOM) - Inclusive and Intelligent Connectivity

作者： Cheddi, Fatima Habbani, Ahmed Nait-Charif, Hammadi Mohammed V Univ ENSIAS Smart Syst Lab Rabat Morocco ParisSaclay Univ Paris France Evry Univ Evry France Univ Bordeaux CNRS LaBRI Bordeaux INP Talence France Bournemouth Univ Natl Ctr Comp Animat Bournemouth Dorset England

ISBN: (纸本)9798350377873;9798350377866

Given the growing dependence on medical imaging, there is a significant requirement for automated report generation, which can save the radiologist's time and reduce the possibility of diagnostic errors. Existing approaches face various difficulties, including insufficient professionalism, a variety of diseases, and fluency in reports. These problems are the result of the use of an encoder-decoder deep learning architecture to establish a uni-directional image-to-report relationship and neglect the bidirectional connections between images and reports, making it challenging to establish the intrinsic medical correlations between them. To this end, we propose a novel approach for chest radiology report generation based on multimodal feature fusion. Our method uses textual and visual features that are taken from medical chest X-ray images and their real reports. Firstly, we use a vision transformer to extract visual features from medical images;on the other hand, we use the Word2Vec model to extract semantic features from textual medical reports. Additionally, we employ advanced techniques such as channel attention networks and cross- modal information fusion modules to enhance the quality and coherence of the generated reports. We have evaluated our proposed approach on two publicly available chest X-ray datasets, IU X-ray and NIH. The results show that our approach outperforms state-of-the-art methods. Particularly in the ROUGE metric and BLEU metric.

关键词： Chest X-ray report generation Multi-modal learning medical image Deep learning Transformers Medical report

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DVI-SLAM: A Dual visual Inertial SLAM Network

DVI-SLAM: A Dual Visual Inertial SLAM Network

引用

ieee International conference on Robotics and Automation (ICRA)

作者： Peng, Xiongfeng Liu, Zhihua Li, Weiming Tan, Ping Cho, SoonYong Wang, Qiang Samsung R&D Inst China SAIT China Lab Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Samsung Adv Inst Technol Multimedia Syst TU Seoul South Korea

ISBN: (纸本)9798350384581;9798350384574

Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress. However, how to make full use of visual information as well as better integrate with inertial measurement unit (IMU) in visual SLAM has potential research value. This paper proposes a novel deep SLAM network with dual visual factors. The basic idea is to integrate both photometric factor and re-projection factor into the end-to-end differentiable structure through multi-factor data association module. We show that the proposed network dynamically learns and adjusts the confidence maps of both visual factors and it can be further extended to include the IMU factors as well. Extensive experiments validate that our proposed method significantly outperforms the state-of-the-art methods on several public datasets, including TartanAir, EuRoC and ETH3D-SLAM. Specifically, when dynamically fusing the three factors together, the absolute trajectory error for both monocular and stereo configurations on EuRoC dataset has reduced by 45.3% and 36.2% respectively.

关键词： Stereo image processing

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EFFICIENT SCENE TEXT image SUPER-RESOLUTION WITH SEMANTIC GUIDANCE 49

EFFICIENT SCENE TEXT IMAGE SUPER-RESOLUTION WITH SEMANTIC GU...

引用

49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： TomyEnrique, Leowu Du, Xiangcheng Liu, Kangliang Yuan, Han Zhou, Zhao Jin, Cheng Fudan Univ Sch Comp Sci Shanghai Peoples R China Videt Technol Shanghai Peoples R China MCT Innovat Ctr Callig & Painting Creat Technol Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Scene text image super-resolution has significantly improved the accuracy of scene text recognition. However, many existing methods emphasize performance over efficiency and ignore the practical need for lightweight solutions in deployment scenarios. Faced with the issues, our work proposes an efficient framework called SGENet to facilitate deployment on resource-limited platforms. SGENet contains two branches: super-resolution branch and semantic guidance branch. We apply a lightweight pre-trained recognizer as a semantic extractor to enhance the understanding of text information. Meanwhile, we design the visual-semantic alignment module to achieve bidirectional alignment between image features and semantics, resulting in the generation of high-quality prior guidance. We conduct extensive experiments on benchmark dataset, and the proposed SGENet achieves excellent performance with fewer computational costs.

关键词： Scene text image super-resolution efficient model semantic guidance

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Feature Skeletons-based Model Retrieval for Bolus Shaping in Cancer Care 9

Feature Skeletons-based Model Retrieval for Bolus Shaping in...

引用

9th International conference on Smart and Sustainable Technologies (SpliTech)

作者： Li, Rui Peng, Qingjin Univ Manitoba Dept Mech Engn Winnipeg MB Canada

ISBN: (纸本)9798350390797;9789532901351

Bolus covers the patient's skin surface in cancer care for desired dose distribution and minimal damage to the healthy tissue. The existing bolus shaping method is mainly a manual process which is inaccurate and inefficient. This paper proposes a model retrieval method based on feature skeletons of the model and model image. Mesh nodes in a bolus model are embedded into a feature space by the spectral analysis. Skeletons are formed from features of the model to build a skeleton base. visual entropies are applied to detect edges of the model image. The edges are then classified into the object and background pixels for contours of the object using a spectral clustering method. The skeleton of the image is compared with skeletons in the model skeleton base to find the best-matched bolus model using an iterative closest point method. The proposed method is verified in the case studies.

关键词： Model retrieval Cancer care image processing Geometry modeling visual entropy Skeleton extraction

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SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing image Dehazing Using Satellite-to-Ground NDVI Knowledge

SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remot...

引用

2023 ieee International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Liu, Zepeng Yang, Zhicheng Zhu, Mingye Wong, Andy Wei, Yibing Han, Mei Yu, Jun Lai, Jui-Hsin University of Science and Technology of China China Ping An Technology China PAII Inc. United States University of Wisconsin - Madison United States

ISBN: (纸本)9798350313154

image dehazing is a meaningful low-level computer vision task and can be applied to a variety of contexts. In our industrial deployment scenario based on remote sensing (RS) images, the quality of image dehazing directly affects the grade of our crop identification and growth monitoring products. However, the widely used peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) provide ambiguous visual interpretation. In this paper, we design a new objective metric for RS image dehazing evaluation. Our proposed metric leverages a ground-based phenology observation resource to calculate the vegetation index error between RS and ground images at a hazy date. Extensive experiments validate that our metric appropriately evaluates different dehazing models and is in line with human visual perception. © 2023 ieee.

关键词： Remote sensing

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DEEP UNROLLING NETWORK FOR SAR image DESPECKLING 49

DEEP UNROLLING NETWORK FOR SAR IMAGE DESPECKLING

引用

49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chen, Che Chen, Lin Jiang, Xue Liu, Xingzhao Zoubir, Abdelhak M. Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Tech Univ Darmstadt Signal Proc Grp D-64283 Darmstadt Germany

ISBN: (纸本)9798350344868;9798350344851

Synthetic aperture radar (SAR) images are inherently affected by speckle noise. Deep learning-based methods have shown good potential in image denoising task. Most deep learning methods for denoising focus on additive Gaussian noise removal. However, SAR images are usually contaminated by non-Gaussian multiplicative speckle noise. In this paper, we propose a novel deep unrolling network named SAR-DURNet to deal with the SAR image despeckling problem. We establish optimization problem of speckle noise removal by using the priori of noise distribution, which can be sovled by half-quadratic splitting (HQS) method with iterative steps. We unroll the iterative process into a trainable deep unrolling network(SAR-DURNet). The parameters of the SAR-DURNet are trained end-to-end with simulated SAR image dataset. Experimental results on simulated test data and real SAR data show that the proposed approach has superior results in terms of quantitative performance metrics and the preservation of intricate visual details, compared to several well-known SAR image despeckling methods.

关键词： SAR image processing deep unrolling speckle noise removal machine learning

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