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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

共 20446 条记录，以下是211-220 订阅

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CONTEXT-AWARE TRANSFORMER FOR SINGLE image RAIN STREAKS REMOVAL 49

CONTEXT-AWARE TRANSFORMER FOR SINGLE IMAGE RAIN STREAKS REMO...

引用

49th ieee international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Chen, Zhihua Liang, Lei Huang, Yeting Dai, Lei Li, Ran Sheng, Bin East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Deep learning based image deraining has been widely researched. However, rain streaks are hard to differentiate with similar textures of background without context knowledge. In this paper, a novel Context-Aware Transformer (CAT) is proposed for single image deraining where both local and global context within the input rainy image are utilized for better background reconstruction performance. The proposed CAT perceives a comprehensive context view through efficient self-attention mechanism and dilated convolutions in the Context-Aware Transformer Block (CATB). The Rain-Aware Feature Selection module (RAFS) generates feature blending coefficients adaptively to filter out rain streaks components and preserves clear background in hierarchical features of CAT. Meanwhile, a High-Frequency Preserved Loss (HFPL) provides further supervision on training and promotes reserving clearer structures and sharper details. Experiments on synthesized and real-world benchmarks illustrate the outstanding performance over state-of-the-art methods and pleasing visual results in various scenes.

关键词： image deraining Global and local context Feature selection High frequency preservation.

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HIQ: ONE-SHOT NETWORK QUANTIZATION FOR HISTOPATHOLOGICAL image CLASSIFICATION 49

HIQ: ONE-SHOT NETWORK QUANTIZATION FOR HISTOPATHOLOGICAL IMA...

引用

49th ieee international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Chen, Xinrui Yan, Renao Wang, Yizhi Li, Jiawen Cheng, Junru Guan, Tian He, Yonghong Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9798350344868;9798350344851

To deploy neural networks on clinical edge devices, quantization is the most commonly used method to compress the models, which requires a calibration set of hundreds of real images. However, due to privacy concerns, the scarcity of private histopathological images hinders the application of quantization. To address this issue, we develop HIQ, a novel one-shot quantization framework for histopathological image classification networks, which requires only one real image per class for calibration. To compensate for data scarcity, sample BNS alignment is introduced to generate synthetic images with similar distribution to the real ones. To improve the diversity of synthetic images, fine-grained diversity enhancement that provides fine-grained enhancement intensity for different classes and network layers is proposed, based on the observation of the class-wise and layer-wise fine-grained data. Finally, the asymptotic enhancement strategy is highlighted to achieve a trade-off between inter-class distance and intra-class diversity of synthetic images, based on the insight of the smaller inter-class distance of histopathological images than that of natural ones. Extensive experiments on the BRACS dataset show that our method achieves an extremely low accuracy loss even compared to the full precision model in low-bit cases and maintains robustness when missing classes of real images.

关键词： One-shot Quantization Diversity Enhancement Fine-grained Information Histopathological image Classification

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DeepEyes: A Deep Vision Indoor Positioning System with Individual Movement Recognition based on IoT

DeepEyes: A Deep Vision Indoor Positioning System with Indiv...

引用

ieee international conference on Pervasive Computing and Communications (PerCom)

作者： Chen, Lien-Wu Huang, Wei-Chu Lai, Chi Feng Chia Univ Dept Informat Engn & Comp Sci Taichung 407 Taiwan

ISBN: (纸本)9798350304367;9798350304374

This paper designs and implements a deep vision indoor positioning system with individual movement recognition, called DeepEyes, based on internet of Things localization. DeepEyes integrates deep learning with pedestrian dead reckoning, enabling the recognition of individual walking distances and moving angles. DeepEyes addresses the accuracy challenges in step length and heading direction estimation encountered by pedestrian dead reckoning methods, which often result in positioning errors that accumulate over time, impacting subsequent localization. In DeepEyes, we design deep vision indoor positioning using existing surveillance cameras for high-precision real-time localization and correction. To the best of our knowledge, this is the first centimeter-level positioning solution to combine deep neural networks with pedestrian dead reckoning methods to recognize individual movement distances and heading angles. In particular, an Android-based prototype with web cameras is implemented to verify the feasibility and performance of our DeepEyes system.

关键词： Deep Learning Indoor Positioning internet of Things Pedestrian Dead Reckoning image Recognition

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Robust image Watermarking based on Texture Guidance and Dynamic Multi-Spectrum Attention 16

Robust Image Watermarking Based on Texture Guidance and Dyna...

引用

16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Yang, Fengting Xue, Weikang Cao, Honglong Hu, Jianling School of Electronic and Information Engineering Soochow University Suzhou China Wuxi University Wuxi China

ISBN: (纸本)9798350390643

Tracking image sources and verifying copyright information is crucial in digital media communication. Digital image watermarking technology, widely used for copyright protection and source tracking, faces challenges in balancing imperceptibility and robustness under a certain embedded capacity. To address this problem, this paper proposes a robust image watermarking framework based on a deep invertible network, named DRIN. based on the frequency-domain characteristics of robust steganography, we parallelly introduce a texture guidance module (GLCM) and a dynamic fine-grained DCT representation-based channel attention module (DFscaNet) into the network. These modules guide the model to effectively locate the texture features where high-frequency energy is concentrated in the cover image, improving the quality of the watermarked image and enhancing the robustness of the watermark. Experiments have demonstrated that the proposed method has superior performance, higher visual quality, and stronger robustness compared to the state-of-the-art methods. © 2024 ieee.

关键词： Steganography

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Proximal Femur Segmentation from the Fluoroscopy image Using DeeplabV3+ Model 2

Proximal Femur Segmentation from the Fluoroscopy Image Using...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Naik, Niva Mehta, Mayuri A. Joshi, Vivek Sardar Vallabhbhai National Institute of Technology Computer Science and Engineering Surat India Sarvajanik College of Engineering and Technology Computer Engineering Department Surat India R.N.G.Patel Institute of Technology Information Technology Bardoli India

ISBN: (纸本)9798350366570

Segmenting the proximal femur from a fluoroscopy image is crucial for precise surgical treatment of proximal femur fracture. Accurate segmentation of the proximal femur helps the surgeon to insert screws and metal pins correctly during the treatment of the proximal femur fracture. Traditional machine learning-based segmentation methods have limitations in precisely capturing the complex and variable anatomy of the proximal femur. Deep learning-based segmentation methods offer promising solutions to overcome the limitations of machine learning-based methods. However, they struggle with problems like hyperparameter tuning and spatial context handling. Hence, this paper proposes a novel proximal femur segmentation approach utilizing encoder-decoder-based architecture. The proposed segmentation approach has been evaluated utilizing three encoder-decoder-based architectures, namely U-net, U-net++, and DeeplabV3+. Its performance is assessed using four metrics such as dice coefficient, Intersection over Union (IoU), precision, and recall. Among the three encoder-decoder-based architectures, DeeplabV3+ outperforms the U-net and U-net++. DeeplabV3+ yields a Dice coefficient of 0.9566 and an IoU of 0.9171 on the training dataset, surpassing the performance of U-net and U-net++. © 2024 ieee.

关键词： Deep learning

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TDMF: Text-Guided Denoising and Interactive Medical image Fusion

TDMF: Text-Guided Denoising and Interactive Medical Image Fu...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Dong, Aimei Xu, Jingyuan Wang, Long Lv, Guohua Zhao, Guixin Cheng, Jinyong Qilu University of Technology Shandong Academy of Sciences Jinan China Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during image acquisition, significantly diminishing their impact on visual quality. To address these challenges, we propose a semantically text-guided medical image fusion model, named TDMF. Specifically, TDMF guides classical image fusion through textual semantics and effectively coordinates the resolution of degradation and interaction issues during the fusion process. By integrating text encoders and interactive fusion modules, TDMF establishes a unified framework for denoising and interactive fusion of medical images. Extensive experiments have demonstrated that our proposed text-guided image fusion strategy offers significant advantages over state-of-the-art methods in medical image fusion performance. © 2025 ieee.

关键词： denoising Medical image fusion text-guided fusion

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Enhancing image Search Engine Accuracy Using Psychological Intention Diagrams 3

Enhancing Image Search Engine Accuracy Using Psychological I...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Aswathi, K.P. Yadwad, Sunita Raj, D. Nirmal Rastogi, Shailesh Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Pune India Pune India

ISBN: (纸本)9798331508456

As the internet becomes increasingly image-centric, users face substantial challenges in efficiently locating desired images due to the inherent limitations of image search engines in interpreting visual content and keyword ambiguity. This paper introduces an innovative solution employing psychological intention diagrams to enhance search accuracy. These diagrams harness historical data to depict the search intentions associated with specific keywords, reflecting both user and web designer perspectives. By integrating these diagrams, our proposed search engine significantly refines its ability to predict and retrieve the most relevant images. The implementation of this system involves constructing two types of intention diagrams: one based on past user queries and another derived from the intentions of web designers for cases without historical user data. This dual approach not only captures the semantic essence of images more effectively but also addresses the multi-faceted meanings of search keywords. Our experimental results confirm that incorporating psychological dimensions into image search mechanisms substantially improves precision, facilitating more accurate and user-aligned image retrieval. This pioneering approach is the first to apply psychological profiling in enhancing image search engine functionalities, marking a significant advancement in digital image retrieval technologies. © 2025 ieee.

关键词： Content based retrieval

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RGB-D image Modeling Method based on Transformer: RDT 3

RGB-D Image Modeling Method Based on Transformer: RDT

引用

3rd international conference on Artificial Intelligence, internet of Things and Cloud Computing technology, AIoTC 2024

作者： Teng, Qianqian He, Xianbo School of Computer Science China West Normal University Nanchong010080 China

ISBN: (纸本)9798331530280

In recent years, the widespread application of Transformers in multi-modal computer vision tasks has brought ncreasing attention to indoor scene image segmentation based on RGB-D images. Due to the significant differences in the feature types between RGB images and depth images, multi-modal image fusion has become a key area of research. To address the issue of internal interference in RGB-D images, this paper proposes a new Transformer-based RGB-D image modeling method, named RDT. RDT models RGB and RGB-D images separately by utilizing a Channel Transformer and a Spatial Transformer, effectively utilizing the auxiliary information from depth images while minimizing their interference. RDT aims to enhance performance through a simple approach and can be easily integrated into existing models. Experiments on the NYU Depth v2 dataset demonstrate that the enhanced model incorporating RDT achieves a 0.37% improvement in mIoU and faster convergence, and fewer parameters. © 2024 ieee.

关键词： Semantic Segmentation

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Digital image Encryption based On Elliptic Curve Cryptography 4

Digital Image Encryption Based On Elliptic Curve Cryptograph...

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4th ieee international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2024

作者： Hasan, Mahmoud H. S. Ahmed, Medht H. S. H. Alasmarya Islamic University College of Information Technology Dept. Computer Science Zliten Libya Higher Institute of Engineering Technology Dept. Communication Engineer Zliten Libya

ISBN: (纸本)9798350372632

Information security is currently a major problem for data communication. Cryptography has emerged as a treatment and is essential to information security systems. Techniques for encryption can effectively prevent attacks on images. Elliptic curve cryptography (ECC) is an approach of public-key encryption, depends on the algebraic grouping of elliptic curves over finite fields. The present study introduces an intriguing elliptic curve cryptography-based image encryption approach and applied it to various images. The proposed technique was tested. We computed the number of pixels change rate (NPCR), unified average changing intensity (UACI), and peak signal to noise ratio (PSNR) to assess the effectiveness of the suggested technique. © 2024 ieee.

关键词： Geometry

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TechGrow: IoT Application for Implementation of Smart Farming System 3

TechGrow: IoT Application for Implementation of Smart Farmin...

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3rd international conference on image Processing and Robotics, ICIPRoB 2024

作者： Sanjeetha, Mohamed Buhary Fathima Abeygunawardhana, Pradeep K.W. SLIIT Faculty of Graduate Studies and Research Malabe Sri Lanka

ISBN: (纸本)9798350374766

In recent years, there has been an increasing fascination in constructing intelligent agricultural systems. Implementing intelligent agricultural techniques has the potential to enhance crop yield while minimizing resource use. The majority of farmers are unaware of recent advancements in technology and innovative ways. This research introduces a novel internet of Things (IoT) based wireless assistant that is designed and implemented for outdoor operations. This proposed wireless helper is equipped with environmental sensors such as temperature, soil moisture, and humidity. Additionally, it integrates the model ESP 32. This cutting-edge wireless helper is capable of detecting moisture, measuring temperature, moving in both forward and backward directions, and illuminating a specific area. In addition, we use repeaters to extend the coverage of the Wi-Fi network to the neighboring farm. The proposed wireless helper was evaluated in real-world conditions and demonstrated outstanding performance. This technique is beneficial for intelligent agricultural systems. © 2024 ieee.

关键词： internet of things

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