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

共 15494 条记录，以下是11-20 订阅

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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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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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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 2nd international conference on computer vision & image processing: cvip 2017, Volume 2

引用

2018年

作者： Bidyut Baran Chaudhuri Mohan Kankanhalli Balasubramanian Raman

ISBN: (纸本)9789811078972

The book provides insights into the Second international conference on computer vision & image processing (cvip-2017) organized by Department of computer Science and Engineering of Indian Institute of Technology Roorkee. The book presents technological progress and research outcomes in the area of image processing and computer vision. The topics covered in this book are 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; motion and tracking; segmentation and recognition; shape, structure and stereo.

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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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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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Light-aware luminance adaptive enhancement network for RGBT video object detection

Light-aware luminance adaptive enhancement network for RGBT ...

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

作者： Ma, Shuchen Jiang, Yafei Gao, Liuxuanqi Li, Yipei Han, Baojing SDIC FINANCE Co. Ltd. Beijing100032 China School of Computer Science and Technology Anhui University Hefei230039 China Shanxi Yitong Power Grid Protection Automation Co. Ltd. Taiyuan030025 China State Grid Shanxi Electric Power Company Taiyuan030025 China

ISBN: (纸本)9781510688384

Visible-thermal Video Object Detection (RGBT VOD) aims to detect objects in RGB and thermal video sequences and predict their categories and locations. One of the core aspects of this task is how to efficiently and effectively perform the fusion of multiple modalities. Due to the limitations of the imaging mechanism, under poor lighting conditions, the image quality of the RGB modality will dramatically decline, reducing the detection performance. Supplementing with thermal data can effectively mitigate these challenges. To fully utilize the complementary information of the two modalities, existing works on feature fusion of RGB and thermal images require an extremely complex modality interaction process, which is time and resource-intensive. When converting the data of the two modalities to the YCbCr color space represented by luminance and color, only luminance information exists in the thermal image. In contrast, the RGB image is rich in luminance and color information. However, due to the imaging mechanism, the luminance information of the RGB modality can easily be affected by bad lighting. To simplify the design of the multimodal interaction module, reduce model complexity, and take full advantage of complementary multimodal information, we consider multi-modality fusion in terms of luminance information for multimodal data and propose a Light-aware Luminance Adaptive Enhancement Network (LA2ENet). Specifically, we design a Light-aware Luminance Adaptive Enhancement Module (LA2EM), which can sense the light information in the scene. When the luminance information in the RGB image is drastically affected due to bad lighting, the module will adaptively introduce the luminance information in the stabilized thermal infrared image to supplement and improve the quality of the RGB image. After that, we only use the luminance-enhanced RGB image as the input to the model, making full use of the modal complementary information while reducing the complexity of the model.

关键词： Video recording

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8th international conference on computer vision and image processing, cvip 2023

8th International Conference on Computer Vision and Image Pr...

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8th international conference on computer vision and image processing, cvip 2023

ISBN: (纸本)9783031585340

The proceedings contain 140 papers. The special focus in this conference is on computer vision and image processing. The topics include: A Novel Framework for Cognitive Load Estimation from Electroencephalogram Signals Utilizing Sparse Representation of Brain Connectivity;MCCNet: A Multi-scale Cross Connection Network for Low-Light image Enhancement;feature Fusion and Multi-head Attention Based Hindi Captioner;Payload Length and Location Identification Using a Novel CNN and Re-embedding Strategy in Stego images Created by Content Adaptive Steganographic Algorithms;ConvMTL: Multi-task Learning via Self-supervised Learning for Simultaneous Dense Predictions;driver Drowsiness Detection Using vision Transformer;automated Cricket Commentary Generation for Videos;image Captioning with Visual Positional Embedding and Bi-linear Pooling;Cartilage Segmentation from MRI images Towards Prediction of Osteoarthritis;Reinforcement Algorithm-Guided ROI Extraction of Fingerprint Biometric Data;improved Metric Space for Shape Correspondence;an Integrated Approach: Combining GrabCut and Contour-Matching for Hand Gesture Segmentation in Indian Sign Language;identity Preserved Expressive Talking Faces with Synchrony;isolated Sign Language Recognition Using Deep Learning;a Gradient-Based Approach to Interpreting Visual Encoding Models;adjust Your Focus: Defocus Deblurring from Dual-Pixel images Using Explicit Multi-Scale Cross-Correlation;Efficient Seizure Prediction from images of EEG Signals Using Convolutional Neural Network;coping with Increased Levels of Label Noise in Facial Expression Recognition;experimental Evaluation of Needle Tip Prediction Using Kalman Filtering Approach;a Deep Face Antispoofing System with Hardware Implementation for Real-Time Applications;Bit Plane Segmentation and LBP-Based Coverless Video Steganography for Secure Data Transmission;a Deep Learning Approach to Enhance Semantic Segmentation of Bacteria and Pus Cells from Microscopic Urine Smear images Using

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