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

共 20484 条记录，以下是441-450 订阅

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Vision in Versatility: Dual CCD-CMOS Imaging With Compressed Sensing for Sustainable IoT Surveillance Drones 10

Vision in Versatility: Dual CCD-CMOS Imaging With Compressed...

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10th ieee international conference on Electronics, Computing and Communication Technologies (ieee CONECCT)

作者： Gambheer, Ramachandra Bhat, M. S. Natl Inst Technol Karnataka Surathkal India

ISBN: (纸本)9798350385939;9798350385922

In the evolving IoT technology landscape, deploying surveillance drones with advanced imaging for security and efficiency is crucial, particularly in low-light conditions. Traditional imaging relies on either Charge-Coupled Device (CCD) sensors for their low-light prowess or Complementary Metal-Oxide-Semiconductor (CMOS) sensors for their energy efficiency. Our research introduces a novel approach by combining CCD and CMOS sensors into a single hardware platform. This allows for smart switching based on ambient light, optimizing energy use and improving image quality in varied environments. We tackle the high energy use of CCD sensors and the inconsistent performance of CMOS sensors under different lighting by applying compressed sensing (CS) techniques. These are designed to lower energy, bandwidth, and storage needs, making CCD sensors more efficient in the dark. Additionally, we've developed optimized sparse reconstruction algorithms to enhance this dual-sensor system's performance in IoT networks, ensuring high image quality with less resource use. This dual-sensor approach is a breakthrough in using CCD sensors for night surveillance, supporting sustainable IoT goals by saving energy and extending drone lifespans. Our research, backed by theoretical analysis, simulations, and empirical tests, proves our algorithms' ability to reconstruct high-quality images efficiently. Introducing this dual-sensor solution represents a significant step in sustainable IoT surveillance, offering potential for widespread use in various settings.

关键词： internet of Things Compressed Sensing Drones FPGA CCD Sensor CMOS Sensor SSIM PSNR image Compression (IC)-C-2 signals

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A Spatial Focal Modulation Network for image Restoration 3

A Spatial Focal Modulation Network for Image Restoration

引用

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

作者： Yang, Lei Liu, Jingyi Shi, Ze Shi, Caijuan Li, Liqi Meng, Fanyu College of Fine Arts Capital Normal University Beijing China School of Mathematical Sciences Capital Normal University Beijing China College of Computing and Automation Wuhan Technology and Business University Wuhan China College of Artificial Intelligence North China University of Science and Technology Tangshan China Information Engineering College Capital Normal University Beijing China Luannan Area Toll Station Hebei Qiancao Expressway Development Co. Ltd. Hebei China

ISBN: (纸本)9798331530280

Deep learning methods based on CNNs and Transformers have attained notable achievements in image restoration (IR). However, CNNs have limited receptive fields, which can be a constraint in IR tasks for specific input content. The self-attention mechanism of Transformers is affected by quadratic computational complexity, making it particularly noticeable in IR tasks with high-resolution inputs. This paper proposes a Spatial Focal Modulation Network (SFMN) for IR, attempting to introduce focal modulation to replace the selfattention mechanism for modeling. Experiments on the public datasets Set5, Set14 and Urban100 show that SFMN achieves competitive results. © 2024 ieee.

关键词： Restoration

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Fragile watermarking for medical image authentication based on DCT technique 5

Fragile watermarking for medical image authentication based ...

引用

5th international conference on Pattern Analysis and Intelligent systems, PAIS 2023

作者： Bouarroudj, Riadh Souami, Feryel Bellala, Fatma Zohra University of Science and Technology Houari Boumediene Computer Science Department Algiers Algeria

ISBN: (纸本)9798350381450

With the advancement of internet and computer technology in the last decade, the ease of editing and altering digital images has significantly increased. Therefore, it is now more crucial than ever for sensitive industries, such as the medical field, to employ image authentication techniques in order to ensure that the images have not been altered. This paper presents a non-blind fragile watermarking technique for medical image authentication. The watermark is generated by downsampling the cover image and embedded into the high-frequency coefficients after applying DCT to the host image. The experimental results demonstrate that the presented technique produces a high watermarked image quality and offers a high sensitivity to various attacks like JPEG compression, collage, and vector quantization. Furthermore, the model localizes with high accuracy the tampered areas within the attacked image. © 2023 ieee.

关键词： Authentication

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CLIP-MEDFAKE: SYNTHETIC DATA AUGMENTATION WITH AI-GENERATED CONTENT FOR IMPROVED MEDICAL image CLASSIFICATION 31

CLIP-MEDFAKE: SYNTHETIC DATA AUGMENTATION WITH AI-GENERATED ...

引用

2024 international conference on image Processing

作者： Chen, Honghui Zhao, Baoquan Yue, Guanghui Liu, Weide Lv, Chenlei Wang, Ruomei Zhou, Fan Sun Yat Sen Univ Sch Comp Sci & Engn Beijing Peoples R China Sun Yat Sen Univ Sch Artificial Intelligence Beijing Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen Peoples R China Harvard Univ Harvard Med Sch Cambridge MA USA Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Data augmentation is serving as a critical and fundamental technology to improve model generalization and performance in a wide spectrum of machine learning tasks. Despite the increasing interest in developing various pathways to artificially generate new data to reduce the overfitting issue during model training, enriching the diversity of training data in the field of medicine remains facing enormous challenges. By virtue of recent advancements in generative artificial intelligence, we present a novel data augmentation framework, CLIP-MedFake, to address the shortage of training data used in medical image classification. The proposed method first employs the Stable Diffusion model to generate new fake data based on a small amount of training data, and then adopts the paradigm of few-shot learning and uses the CLIP architecture as the backbone to pre-train the model with synthetic data and then fine-tune it with real medical images. Extensive experiment results on two publicly available datasets demonstrate the effectiveness of the proposed method in promoting medical image classification.

关键词： Medical image classification data augmentation artificial intelligence-generated content

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internet of Things Data Aggregation based on image Information Hiding 23

Internet of Things Data Aggregation Based on Image Informati...

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7th international conference on Digital signal Processing, ICDSP 2023

作者： Yang, Yanxiang University of Melbourne Australia

ISBN: (纸本)9781450398626

In the era of intelligent information technology of 5G, smart buildings, promoted by the internet of Things, enhance the ability of indoor monitoring and equipment management. The acquisition and transmission of Architectural Physical Environment Data (APED) is a crucial part of the internet of Things (IoT). Traditional methods rely on many sensors and multiple types of network channels, resulting in problems such as many data transmissions and increased communication bandwidth requirements. Therefore, a data aggregation strategy based on data hiding technology is proposed. All the temporal data is hidden in the existing image through a customized lightweight codec scheme and communication protocol based on binary coding, and the data is aggregated into image(s) for uploading without reducing the fidelity of the image, to improve the efficiency of data transmission. The paper uses APED to conduct simulation experiments to verify the feasibility of the technique. The scheme can be extended to other similar internet of Things applications. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： internet of things

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A Low-illumination image Enhancement Algorithm based on Retinex to Improve SIFT Matching in Similar Scenes 17

A Low-illumination Image Enhancement Algorithm Based on Reti...

引用

17th ieee international conference on signal Processing, ICSP 2024

作者： Tang, Zilin Zhang, Qizhi Sun, Xiang Hao, Tiankun Fan, Yongjie China Coal Technology & Engineering Group Shanghai Co. Ltd Shanghai China State Key Laboratory of Intelligent Coal Mining and Strata Control Shanghai China Yanzhou Coal Mining Energy Group Co. Ltd. China

ISBN: (纸本)9798350387384

A low-light enhancement algorithm based Retinex model is proposed, which has the effect of improving SIFT matching on low-light image. Firstly, according to Retinex model, Illumination map is obtained by Gaussian filtering. Secondly, the original image is subtracted from the illumination map, and the result of this processing is corrected using gamma correction. Finally, add the original image after Histogram Equalized to the image obtained by the second step. The results of Mikolajczy database show, the number of SIFT feature points were 2709, 2294, 1969, 1719, 1532, and 1240. After image processed by this algorithm, the SIFT feature points extracted are 6126, 5656, 5349, 4926, 4574 and 4202. The matching accuracy was also improved from 0.92, 0.91, 0.87, 0.86, 0.77 to 0.94, 0.93, 0.92, 0.89, and 0.86. © 2024 ieee.

关键词： image matching

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FUZZY-CONDITIONED DIFFUSION AND DIFFUSION PROJECTION ATTENTION APPLIED TO FACIAL image CORRECTION 30

FUZZY-CONDITIONED DIFFUSION AND DIFFUSION PROJECTION ATTENTI...

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30th ieee international conference on image Processing (ICIP)

作者： El Helou, Majed Swiss Fed Inst Technol Media Technol Ctr Zurich Switzerland

ISBN: (纸本)9781728198354

image diffusion has recently shown remarkable performance in image synthesis and implicitly as an image prior. Such a prior has been used with conditioning to solve the inpainting problem, but only supporting binary user-based conditioning. We derive a fuzzy-conditioned diffusion, where implicit diffusion priors can be exploited with controllable strength. Our fuzzy conditioning can be applied pixel-wise, enabling the modification of different image components to varying degrees. Additionally, we propose an application to facial image correction, where we combine our fuzzy-conditioned diffusion with diffusion-derived attention maps. Our map estimates the degree of anomaly, and we obtain it by projecting on the diffusion space. We show how our approach also leads to interpretable and autonomous facial image correction.

关键词： image-conditioned diffusion fuzzy conditioning diffusion projection autonomous image correction

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Joint Source-Channel Coding base on DL for Unmanned Aerial Vehicle image Transmission 13

Joint Source-Channel Coding base on DL for Unmanned Aerial V...

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13th ieee international conference on Communications, Circuits and systems (ICCCAS)

作者： Wu Yi-chen Du Yu Liao Jing Chen Hua-wei Univ Elect Sci & Technol China Sch Astronaut & Aeronaut Chengdu 611731 Peoples R China

ISBN: (数字)9798350386271

ISBN: (纸本)9798350386288;9798350386271

Traditional image transmission solutions incorporate source coding to minimize redundant information, addressing bandwidth and power constraints. Following this, channel coding is applied to introduce redundancy, ensuring the efficacy of data transmission. However, unmanned aerial vehicles (UAVs) encounter challenges such as limited signal coverage, poor channel conditions, and constraints on transmission bandwidth and energy during wireless image transmission. The utilization of traditional source-channel concatenated coding for data transmission incurs a high cost. To tackle this challenge, this paper proposes a Joint Source-Channel Coding (JSCC) image transmission solution based on deep learning. This approach directly maps image pixels to complex-valued channels, thereby reducing the overall cost of end-to-end transmission. Furthermore, an optimized JSCC method is introduced, integrating adaptive attention mechanisms, channel estimation, and channel equalization algorithms. Experimental results demonstrate that the proposed method not only outperforms traditional concatenated transmission schemes but also enhances the performance of JSCC in terms of transmission quality.

关键词： Joint source-channel coding Attention Channel estimation Channel equalization deep learning

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Raspberry Pi-based Monitoring System for Grid-Connected PV systems using Deep Learning Technique 10

Raspberry Pi-Based Monitoring System for Grid-Connected PV S...

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10th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Yahyaoui, Zahra Saadaoui, Wajdi Hajjil, Mansour Mansouri, Majdi Nounou, Mohamed Bouzrara, Kais Kairouan Univ Higher Inst Appl Sci & Technol Kasserine Res Unit Adv Mat & Nanotechnol Kairouan Tunisia Texas A&M Univ Qatar Dept Elect & Comp Engn Program Doha Qatar Natl Engn Sch Monastir Res Lab Automat Signal Proc & Image Monastir 5019 Tunisia

ISBN: (纸本)9798350373981;9798350373974

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring systems. The obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

关键词： Environmental monitoring

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An Effective Hybrid image Watermarking Encrypted and Decrypted Technique for Copyright Protection 2

An Effective Hybrid Image Watermarking Encrypted and Decrypt...

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2nd ieee international conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Al-Jawahry, Hassan M. Kumar, Bura Vijay Chandra, N Subhash Patil, Rajeshwari Unnisa, Raheem The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Cvr College of Engineering Telangana India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India Cmr Technical Campus Hyderabad India

ISBN: (纸本)9798350317558

image watermarking serves as a visual deterrent, signaling potential infringers that the image is protected by copyright. While various watermarking methods have been developed in the past, existing models encounter limitations such as a lack of suitability for diverse data types and ineffective data generalization. Additionally, the absence of standardization in watermarking techniques poses a significant challenge. In this proposed approach, images are input into an improved discrete Fourier transform (IDFT) to convert signals from time domain. The Arnold Transform is initially applied to convert images into a signal format. Subsequently, Walsh-Hadamard Transform (WHT) is employed for non-sinusoidal, orthogonal signal transformation. Finally, Singular Value Decomposition (SVD) is implemented to decrease number of dimensions in high-dimensional data, facilitating easier visualization and analysis. The average Peak signal-to-Noise Ratio (PSNR), Normalized Cross-Correlation (NCC), and Bit Error Rate (BER) of the hybrid IDFT-WHT-SVD watermarking scheme are reported as 56.58, 0.2651, and 0.9902, respectively. This proposed model underwent comparison with other existing models, including CNN-based robust watermarking, Hyper-Chaotically Encrypted, and Cryptographic Technique. © 2024 ieee.

关键词： Decrypting Embedding Improved Descried Fourier Transform Singular Value Decomposition Walsh-Hadamard Transform Watermarkingg

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