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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

共 1410 条记录，以下是31-40 订阅

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A zero-reference nighttime road image enhancement method

A zero-reference nighttime road image enhancement method

引用

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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Remote sensing image super-resolution reconstruction using hybrid dilated convolution and adaptive pruning

Remote sensing image super-resolution reconstruction using h...

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

作者： Liu, Huan Li, Yunhong Chen, Weizhong Feng, Zhunruo School of Electronics and Information Xi’an Polytechnic University Xi’an710048 China

ISBN: (纸本)9781510688384

Compared to natural images, remote sensing images suffer from more severe detail loss and face greater challenges in image reconstruction. We proposes a super-resolution network for remote sensing images based on hybrid dilated convolution and adaptive pruning. The network expands the receptive field through hybrid dilated convolution to extract more detailed image features. Bilinear interpolation is employed to upsample the extracted features, while residual connections are introd uced to reduce information loss in the convolutional layers. A multi-scale attention fusion block is employed to enhance both local and global information, allowing for improved recovery of the details and overall structure of remote sensing images. The increased computational complexity and parameter count of the fused network make deployment on mobile and edge devices challenging. Therefore, a dual-channel adaptive pruning module is employed to prune redundant channel features, which enhances the model's lightweight nature and practicality while preserving image details. The experimental results indicate that the proposed method effectively reconstructs texture details in remote sensing images while maintaining a balanced trade-off between computational efficiency and the quality of super-resolution reconstruction. © 2025 SPIE.

关键词： image reconstruction

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Research on anti-breakout system of power optical fiber communication network based on remote sensing image target detection

Research on anti-breakout system of power optical fiber comm...

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

作者： Chen, Shaoxin Chen, Jinwang Shi, Mingxing Lin, Shu Zhang, Wei Liu, Xinyu State Grid Xiamen Electric Power Supply Company Xiamen361004 China School of Electrical Engineering and Automation Fuzhou University Fuzhou350100 China

ISBN: (纸本)9781510688384

Remote sensing images play a crucial role in large-scale surveillance and data analysis. To address challenges in monitoring and managing external threats to power optical fiber communication networks, this study integrates remote sensing images for network inspection. It proposes an anti-external damage system for power optical fiber communication networks based on remote sensing image target detection. The study enhances the YOLOv8 algorithm for improved object detection and utilizes a linear threshold model with specific risk factors to analyze and establish a robust anti-breakout system. Experimental tests and algorithm comparisons validate the system's feasibility and effectiveness. © 2025 SPIE.

关键词： Optical fiber communication

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Video object segmentation quality assessment without groundtruth

Video object segmentation quality assessment without groundt...

引用

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

作者： Hu, Hang Xu, Jinzhong Zhang, Yunfei Wang, Junxiang Li, Ye Xu, Lele Yan, Zhen Wang, Hongfei Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China

ISBN: (纸本)9781510688384

Despite recent advances in Video Object Segmentation (VOS), state-of-the-art models still face challenges with occlusion, fast motion, and long-term tracking. These difficulties often result in a noticeable degradation of segmentation accuracy as the video progresses, leading to poor performance over extended periods. To tackle these persistent issues, we propose an innovative approach that enhances video segmentation by introducing predictive segmentation heads. Building upon the Cutie model, our method enables the model to predict Intersection over Union (IoU) results without ground truth, thereby enhancing video segmentation performance in challenging scenarios. © 2025 SPIE.

关键词： image segmentation

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ADLT: a novel method for effective detection of adversarial attacks in image retrieval

ADLT: a novel method for effective detection of adversarial ...

引用

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

作者： Xu, Shiyue Zhang, Yu College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300350 China

ISBN: (纸本)9781510688384

The versatile application of deep learning has made it prone to vulnerabilities, particularly adversarial attacks, which have been increasingly prevalent as its scope expands. Attack methods based on adversarial queries pose significant challenges to image retrieval models, disrupting their functionality. While numerous defense methods have been proposed for adversarial query attacks in image classification, approaches to detecting or defending against such attacks in image retrieval remain limited due to inherent challenges in adapting these methods. Based on these observations, we introduce the Augmentation Detection based on Top-K-List (ADTL) method, the first effective approach for detecting adversarial attacks in image retrieval. ADTL applies image augmentation to a query and determines its adversarial nature by calculating the divergence in the Top-K list before and after augmentation. This paper presents a method that employs suitable image augmentation techniques and K-Reciprocal clustering for pre-classifying images in the gallery to ease divergence calculations, which has been tested on the Oxford5k and Paris6k datasets and has shown remarkable effectiveness with a high detection accuracy of 87.3%. © 2025 SPIE.

关键词： Adversarial perturbation Attack detection image augmentation image retrieval

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B-H-Deformable-DETR: H-Deformable-DETR model based on Bayesian neural network optimization in Few-Shot Object Detection

B-H-Deformable-DETR: H-Deformable-DETR model based on Bayesi...

引用

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

作者： Chen, Yijun Zeng, Shenglin Li, Muyang Guo, Yizhe Zhao, Fayang School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (纸本)9781510688384

In this paper, we propose an improved B-H-Deformable-DETR (Bayesian H-Deformable-DETR) model for the problem of insufficient accuracy and generalization ability in the task of Few-Shot Object Detection. In our study, we adopt Bayesian MLP (Multi-Layer Perceptron based on Bayesian Linear Layers) as the prediction layer of bounding box regression. By introducing prior distribution and uncertainty estimation, we transform the neural network with deterministic parameters into a probabilistic neural network with stochastic properties. This probabilistic neural network can handle the variability and sparsity of data more effectively with the expectation of improving the model's performance in dealing with uncertainty and generalization. As a result, under the setting of using ResNet50 and Swin-Transformer as backbone and training on the first 5000 images of the COCO dataset, the AP (average precision) of our model improves by +5.4 AP and +4.6 AP compared to the original H-Deformable-DETR model. These results indicate that our model has significantly improved prediction accuracy and enhanced generalization ability on small sample datasets. © 2025 SPIE.

关键词： Average Precision (AP) B-H-Deformable-DETR Bayesian MLP Few-Shot Object Detection Generalization Capability

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Proceedings of 3rd international conference on computer vision and image processing 1

引用

丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Bidyut B. Chaudhuri Masaki Nakagawa Pritee Khanna Sanjeev Kumar

ISBN: (数字)9789813292918

ISBN: (纸本)9789813292901

This book is a collection of carefully selected works presented at the Third international conference on computer vision & image processing (cvip 2018). The conference was organized by the Department of computer Science and Engineering of PDPM Indian Institute of Information Technology, Design & Manufacturing, Jabalpur, India during September 29 - October 01, 2018. All the papers have been rigorously reviewed by the experts from the domain. This 2 volume proceedings include technical contributions in the areas of image/Video processing and Analysis; image/Video Formation and Display; image/Video Filtering, Restoration, Enhancement and Super-resolution; image/Video Coding and Transmission; image/Video Storage, Retrieval and Authentication; image/Video Quality; Transform-based and Multi-resolution image/Video Analysis; Biological and Perceptual Models for image/Video processing; Machine Learning in image/Video Analysis; Probability and uncertainty handling for image/Video processing; andMotion and Tracking.

关键词： Signal, image and Speech processing image processing and computer vision Biometrics

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Proceedings of international conference on computer vision and image processing 1

引用

丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Balasubramanian Raman Partha Pratim Roy Debashis Sen Sanjeev Kumar

ISBN: (数字)9789811021077

ISBN: (纸本)9789811021060

This edited volume contains technical contributions in the field of computer vision and image processing presented at the First international conference on computer vision and image processing (cvip 2016). The contributions are thematically divided based on their relation to operations at the lower, middle and higher levels of vision systems, and their applications.;The technical contributions in the areas of sensors, acquisition, visualization and enhancement are classified as related to low-level operations. They discuss various modern topics – reconfigurable image system architecture, Scheimpflug camera calibration, real-time autofocusing, climate visualization, tone mapping, super-resolution and image resizing.;The technical contributions in the areas of segmentation and retrieval are classified as related to mid-level operations. They discuss some state-of-the-art techniques – non-rigid image registration, iterative image partitioning, egocentric object detection and video shot boundary detection.;The technical contributions in the areas of classification and retrieval are categorized as related to high-level operations. They discuss some state-of-the-art approaches – extreme learning machines, and target, gesture and action recognition. A non-regularized state preserving extreme learning machine is presented for natural scene classification. An algorithm for human action recognition through dynamic frame warping based on depth cues is given. Target recognition in night vision through convolutional neural network is also presented. Use of convolutional neural network in detecting static hand gesture is also discussed.;Finally, the technical contributions in the areas of surveillance, coding and data security, and biometrics and document processing are considered as applications of computer vision and image processing. They discuss some contemporary applications. A few of them are a system for tackling blind curves, a quick reaction target acquisition and track

关键词： Signal, image and Speech processing image processing and computer vision Biometrics

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Multi-phase difference enhanced fusion for tumor and pancreas segmentation

Multi-phase difference enhanced fusion for tumor and pancrea...

引用

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

作者： He, Chongyu Zhou, Fugen Liu, Bo Li, Jiaping Zhou, Dinghua Image Processing Center School of Astronautics Beihang University Beijing100191 China Department of Interventional Oncology The First Affiliated Hospital Sun Yat-Sen University Guangdong Guangzhou China Department of Hepatobiliary Surgery PLA Rocket Force Characteristic Medical Center Beijing China

ISBN: (纸本)9781510688384

Multi-phase contrast-enhanced computed tomography (CECT) has been shown to effectively refine the segmentation accuracy for both tumors and organs. Nevertheless, enhancing the segmentation accuracy of the pancreas and pancreatic tumors using multi-phase CECT remains a challenge, which is crucial for subsequent clinical analysis and treatment. Current methods either simply concatenate multi-phase features along the channel direction or utilize attention mechanisms to perform weighted summation of features across phases, neglecting the contribution of phase-specific difference features to segmentation accuracy. In view of this, this paper proposes a multi-phase segmentation method for the pancreas and pancreatic tumors, improving segmentation accuracy by enhancing the phase-specific difference features. Additionally, we propose a training strategy to improve the segmentation accuracy of model when only single-phase CECT is available. The process achieved by distilling the learned knowledge of a segmentation model trained on multi-phase CECT. The proposed method and training strategy proved effective through experiments conducted on a privately collected multiphase CECT dataset of pancreatic tumors. © 2025 SPIE.

关键词： computerized tomography

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Efficient LED image reflection removal via mixture-of-experts

Efficient LED image reflection removal via mixture-of-expert...

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

作者： Yao, Ziyu Sun, Xinzhong Wang, Liqiang Zhao, Lihong Zhang, Kai Lin, Junjie Wang, Yong Zou, Yuexian School of Electronic and Computer Engineering Peking University Beijing China Shenzhen AOTO Electronics Co. Ltd. Shenzhen China

ISBN: (纸本)9781510688384

Recently, XR virtual shooting has developed rapidly, with LED displays playing a pivotal role in promoting the prosperity of the film and television industry. However, the necessity for additional lighting to achieve fill light often results in specular or diffuse reflections from the LED displays, which can be prominently visible in the video data, thus affecting the overall visual quality. Meanwhile, given the strict time constraints in most XR scenes, lightweight approaches are indispensable. To benchmark single reflection removal of LED image, we collect and introduce a new dataset named AOTO Denoising Dataset, comprising 5500 LED images with white reflection areas. Furthermore, we propose a novel lightweight denoising model called MoE-LED-light for efficient LED image reflection removal. Specifically, we innovatively adopt the proposed mixture-of-experts strategy to enhance the performance of reflection removal without compromising inference efficiency. Experimental results on our AOTO denoising dataset and four public datasets have demonstrated that our MoE-LED-light model not only effectively eliminates white reflection areas in images but also achieves efficient performance, operating at over 50 frames per second. © 2025 SPIE.

关键词： Light emitting diodes

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