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共 25554 条记录，以下是281-290 订阅

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GABOR FEATURE NETWORK FOR TRANSFORMER-BASED BUILDING CHANGE DETECTION MODEL IN REMOTE SENSING 31

GABOR FEATURE NETWORK FOR TRANSFORMER-BASED BUILDING CHANGE ...

引用

2024 International conference on image processing

作者： Osa, Priscilla Indira Zerubia, Josiane Kato, Zoltan Univ Genoa DITEN Dept Genoa Italy Univ Cote Azur INRIA Nice France Univ Szeged Inst Informat Szeged Hungary J Selye Univ Komarno Slovakia

ISBN: (纸本)9798350349405;9798350349399

Detecting building change in bitemporal remote sensing (RS) imagery requires a model to highlight the changes in buildings and ignore the irrelevant changes of other objects and sensing conditions. Buildings have comparatively less diverse textures than other objects and appear as repetitive visual patterns on RS images. In this paper, we propose Gabor Feature Network (GFN) to extract the distinctive repetitive texture features of buildings. Furthermore, we also design Feature Fusion Module (FFM) to fuse the extracted multiscale features from GFN with the features from a Transformer-based encoder to pass on the texture features to different parts of the model. Using GFN and FFM, we design a Transformer-based model, called GabFormer for building change detection. Experimental results on the LEVIR-CD and WHU-CD datasets indicate that GabFormer outperforms other SOTA models and in particular show significant improvement in the generalization capability. Our code is available on https://***/Ayana-Inria/GabFormer.

关键词： Transformer Gabor feature building change detection remote sensing image analysis

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Improved PSP-Net Segmentation Network for Automatic Detection of Neovascularization in Color Fundus images

Improved PSP-Net Segmentation Network for Automatic Detectio...

引用

ieee International conference on visual communications and image processing (VCIP)

作者： Liu, Qiuming Wang, Shen Dai, Yulan Zhang, Jintao Wang, Yichen Zhou, Ruoxuan Jiangxi Univ Sci & Tech Sch Software Engn Ganzhou Jiangxi Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Jiangxi Normal Univ Sch Comp Info Engn Nanchang Jiangxi Peoples R China Nanchang Key Lab Virtual Digital Factory & Cultur Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665475921

Proliferative Diabetic Retinopathy (PDR) is a serious retinal disease threatening diabetic patients. Intense retinal neovascularization in the retinal image is the most important clinical symptom of PDR, leading to visual distortion if not controlled. Accurate and timely detection of neovascularization from retinal images allows patients to receive adequate treatment to avoid further vision loss. In this work, we propose a retinal neovascularization automatic segmentation model based on improved Pyramid Scene Parsing Network (PSP-Net). To improve the accuracy of the model, we introduce the proposed channel attention module into the model. The network is evaluated with color fundus images from practice. Evaluation results show the network is superior to FCN, SegNet, U-Net and PSP-Net in accuracy and sensitivity. The model could achieve accuracy, sensitivity, specificity, precision and Jaccard similarity score of 0.9832, 0.9265, 0.9897, 0.9116 and 0.8501, respectively. This paper proves through plenty of experimental results that the network model is able to improve the accuracy of segmentation, relieve the workload of doctors, and is worthy of further clinical promotion.

关键词： fundus images image segmentation diabetic retinopath neovascularization deep learning

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GENERALIZED NESTED LATENT VARIABLE MODELS FOR LOSSY CODING APPLIED TO WIND TURBINE SCENARIOS 31

GENERALIZED NESTED LATENT VARIABLE MODELS FOR LOSSY CODING A...

引用

2024 International conference on image processing

作者： Perez-Gonzalo, Raul Espersen, Andreas Agudo, Antonio CSIC UPC Inst Robot & Informat Ind Barcelona Spain Wind Power LAB Copenhagen Denmark

ISBN: (纸本)9798350349405;9798350349399

Rate-distortion optimization through neural networks has accomplished competitive results in compression efficiency and image quality. This learning-based approach seeks to minimize the compromise between compression rate and reconstructed image quality by automatically extracting and retaining crucial information, while discarding less critical details. A successful technique consists in introducing a deep hyperprior that operates within a 2-level nested latent variable model, enhancing compression by capturing complex data dependencies. This paper extends this concept by designing a generalized L-level nested generative model with a Markov chain structure. We demonstrate as L increases that a trainable prior is detrimental and explore a common dimensionality along the distinct latent variables to boost compression performance. As this structured framework can represent autoregressive coders, we outperform the hyperprior model and achieve state-of-the-art performance while reducing substantially the computational cost. Our experimental evaluation is performed on wind turbine scenarios to study its application on visual inspections.

关键词： image Compression Rate-distortion Loss Nested Models Wind Turbine Blade Inspections

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Breast Cancer Detection Using Transformer and BiLSTM Based Ensemble Learning 31

Breast Cancer Detection Using Transformer and BiLSTM Based E...

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31st ieee conference on Signal processing and communications Applications (SIU)

作者： Yilmaz, Rabia Eda Serbes, Görkem Yildiz Tekn Univ Biyomed Muhendisligi Bolumu Istanbul Turkiye

ISBN: (纸本)9798350343557

Breast cancer is one of the most common types of cancer among women, and early diagnosis is essential for effective treatment. In recent years, deep learning techniques have shown promising results in solving image classification problems. In this study, labeled image patches obtained from whole slide images in the BACH dataset were used for breast cancer classification. The performances of pre-trained deep learning models for breast cancer classification on labeled image patches were compared. Vision Transformer (ViT) gives better performance with the local features extracted by focusing on the important regions in the image due to the transformer structure and its Attention mechanism. Moreover, the model extending the Xception backbone architecture with Bidirectional Long Short-Term Memory (BiLSTM) layers, XceptionBiLSTM, significantly improved classification performance by learning the spatial relationships between image patches. Furthermore, data augmentation techniques were applied to dataset containing a limited number of image patches, which increased the models' generalization capacity and prevented overfitting. With the proposed architecture, which is the result of combining the predictions of the individual Xception, ViT, and XceptionBiLSTM models used with the ensemble learning approach, 90% accuracy in the hidden test dataset, 95% accuracy and 94.9% F1-Score in the validation dataset were obtained. The results obtained demonstrate the significant potential of the proposed ensemble learning-based architecture for breast cancer classification.

关键词： breast cancer image classification deep learning BACH dataset medical imaging

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Multi-Resolution Subspace Reconstruction Through Density Measure - Application to image processing

Multi-Resolution Subspace Reconstruction Through Density Mea...

引用

2024 ieee Pacific Rim conference on communications, Computers and Signal processing, PACRIM 2024

作者： Robaei, Mohammadreza University of Tabriz Tabriz Iran

ISBN: (纸本)9798350362312

A novel image subspace reconstruction has been proposed. The suggested inverse problem regularization algorithm approximates a class of differentials of function through multi-resolution density measure over level sets to learn high order- TV regularization operator, progressively. For this purpose, a differential spectral spectrum decomposition based on modulation theorem has been developed. The proposed methodology employs the relaxed upper gradient definition in the bounded variation and topological spaces to prove the stability and reliability of the proposed algorithm. We show that the resultant density measurement over sets define the higher-order bounded variation space such that the sequence of mean-value solutions are related to the relaxed upper gradient. Finally, numerical evaluations show the versatility of the proposed regularization algorithm applied to the image processing problems. © 2024 ieee.

关键词： image reconstruction

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Improved Multi-modal image Fusion with Attention and Dense Networks: visual and Quantitative Evaluation 8th

Improved Multi-modal Image Fusion with Attention and Dense N...

引用

8th International conference on Computer Vision and image processing (CVIP)

作者： Banerjee, Ankan Patra, Dipti Roy, Pradipta Natl Inst Technol Rourkela India DRDO Integrated Test Range Candipur India

ISBN: (纸本)9783031585340;9783031585357

This article introduces a novel multi-modal image fusion approach based on Convolutional Block Attention Module and dense networks to enhance human perceptual quality and information content in the fused images. The proposed model preserves the edges of the infrared images and enhances the contrast of the visible image as a pre-processing part. Consequently, the use of Convolutional Block Attention Module has resulted in the extraction of more refined features from the source images. The visual results demonstrate that the fused images produced by the proposed method are visually superior to those generated by most standard fusion techniques. To substantiate the findings, quantitative analysis is conducted using various metrics. The proposed method exhibits the best Naturalness image Quality Evaluator and Chen-Varshney metric values, which are human perception-based parameters. Moreover, the fused images exhibit the highest Standard Deviation value, signifying enhanced contrast. These results justify the proposed multi-modal image fusion technique outperforms standard methods both qualitatively and quantitatively, resulting in superior fused images with improved human perception quality.

关键词： image fusion attention human perception Convolutional Block Attention Module

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FG-PIH: A Fusion of Fresnelet Transform and Gradient Directional Pattern for Perceptual image Hashing 2

FG-PIH: A Fusion of Fresnelet Transform and Gradient Directi...

引用

2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and communications Systems, ICMACC 2024

作者： Meesala, Pavani Thounaojam, Dalton Meitei Computer Science and Engineering National Institute of Technology Silchar Computer Vision Laboratory Silchar India

ISBN: (纸本)9798350366570

Perceptual image hashing is pivotal in various image processing applications, including image authentication, content-based image retrieval, tampered image detection, and copyright protection. This paper proposes a novel approach for perceptual image hashing by combining the Fresnelet Transform with Gradient Directional Patterns. Using the FG-PIH technique, the proposed method achieves superior robustness against common image processing attacks while maintaining perceptual similarity for near-duplicate images. Experimental results on standard benchmark datasets demonstrate the effectiveness and efficiency of the proposed Fresnelet Transform-based perceptual image hashing scheme. Furthermore, comparative analysis against state-of-the-art methods underscores the competitiveness of our approach in terms of hash quality and computational complexity. © 2024 ieee.

关键词： Hamming distance

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Variable-rate Learned image Compression with Adaptive Quantization Step Size

Variable-rate Learned Image Compression with Adaptive Quanti...

引用

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

作者： Mei, Feihong Li, Li Liu, Dong USTC Deep Space Exploration Laboratory Hefei China

ISBN: (纸本)9798350359855

Nowadays, learned image compression has outperformed traditional coding methods like VVC. However, learned image compression methods are often optimized for specific rates and lack support for variable rate compression. Though some efforts have been made to address variable rate compression, striking the right balance between performance and complexity remains a challenge. To address this, we propose the Adaptive Quantize Step Size (AQS) technique, allowing continuous variable rate within a single model. Unlike methods that learn to scale the latent representation, AQS enables learning of different quantization step sizes for different rates, preserving the latent representation's distribution while only affecting the quantization step size, which is beneficial for the entropy model. As a result, our method achieves superior performance compared to existing methods while maintaining similar complexity. It delivers comparable results across a wide range of rates using a single model with minimal additional parameters. © 2023 ieee.

关键词： image compression

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SEGMENT ANYTHING MODEL MEETS image HARMONIZATION 49

SEGMENT ANYTHING MODEL MEETS IMAGE HARMONIZATION

引用

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

作者： Chen, Haoxing Li, Yaohui Gu, Zhangxuan Xu, Zhuoer Lan, Jun Li, Huaxiong Ant Grp Hangzhou Peoples R China Nanjing Univ Nanjing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

image harmonization is a crucial technique in image composition that aims to seamlessly match the background by adjusting the foreground of composite images. Current methods adopt either global-level or pixel-level feature matching. Global-level feature matching ignores the proximity prior, treating foreground and background as separate entities. On the other hand, pixel-level feature matching loses contextual information. Therefore, it is necessary to use the information from semantic maps that describe different objects to guide harmonization. In this paper, we propose Semantic-guided Region-aware Instance Normalization (SRIN) that can utilize the semantic segmentation maps output by a pre-trained Segment Anything Model (SAM) to guide the visual consistency learning of foreground and background features. Abundant experiments demonstrate the superiority of our method for image harmonization over state-of-the-art methods.

关键词： image harmonization segment anything instance normalization

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SPGFUSION: A SEMANTIC PRIOR GUIDED INFRARED AND VISIBLE image FUSION NETWORK 49

SPGFUSION: A SEMANTIC PRIOR GUIDED INFRARED AND VISIBLE IMAG...

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

作者： Xiao, Quanquan Jin, Haiyan Su, Haonan Zuo, Fengyuan Zhang, Yuanlin Xiao, Zhaolin Wang, Bin Xian Univ Technol Fac Comp Sci & Engn Xian Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Infrared and visible image fusion is an important multimodal image processing task that aims to enhance computer vision performance by effectively fusing infrared and visible images. Although in recent years, many deep learning-based methods for infrared and visible image fusion have emerged. Howeve, most of these methods ignore the important role of semantic information in image fusion. Therefore, this paper proposes a semantic priori guided infrared and visible image fusion network called SPGFusion. It uses an adversarial generative network framework based on semantic priors to guide the infrared and visible image fusion process by combining a semantic feature-aware module and semantic generative adversarial loss. Experimental results demonstrate that the SPGFusion method yields more visually appealing fusion results and outperform state-of-the-art image fusion algorithms in visual quality and quantitative evaluation. The source code is available at https://***/tianzhiya/SPGFusion.

关键词： image fusion semantic prior multimodal perception adversarial learining attention mechainism

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