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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21-30 订阅

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Warship image Segmentation Network based on Void Convolution and Attention Mechanism

Warship Image Segmentation Network based on Void Convolution...

引用

9th international conference on signal and image Processing (ICSIP)

作者： Chen, Hongyan Yin, Donghui Qin, Zhen Wu, Qiong Acad PAP Dept Informat & Commun Hangzhou Peoples R China

ISBN: (纸本)9798350350920

In recent years, deep learning (DL) technology has developed rapidly in the field of image segmentation, which improves the accuracy of image segmentation compared with traditional image segmentation. This paper proposes an improved Mask R-CNN network based on cavity convolution and attention mechanism, which is more suitable for warship image segmentation. In addition, to solve the problem of insufficient data sets, the method of data enhancement is adopted to establish warship data sets. Through experiments, it is found that the warship image segmentation method in this paper can get more accurate segmentation effect on the warship image segmentation task, in which the average segmentation accuracy of small target warships is improved by about 8.5%.

关键词： mask R-CNN image segmentation empty convolution attention mechanism data enhancement

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image Transformation for IoT-based Anomaly Detection 32

Image Transformation for IoT-based Anomaly Detection

引用

32nd ieee signal Processing and Communications Applications conference (SIU)

作者： Bamus, Imran Okay, Feyza Yildirim Gazi Univ Comp Engn Dept Ankara Turkiye

ISBN: (纸本)9798350388978;9798350388961

In the rapidly evolving environment of the internet of Things (IoT), anomaly detection plays an important role in ensuring the security and reliability of connected systems. Traditional approaches often struggle to capture subtle patterns and anomalies in complex IoT data. This paper introduces a new perspective by utilizing image transformation techniques, specifically the Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plot (RP), coupled with deep learning models for better anomaly detection. This study presents a comparative study to evaluate the performance of these image transformation methods and assess their effectiveness in detecting anomalies in IoT data. The comparative results show that RP with CNN has the superior performance compared to other image transformation techniques.

关键词： image transformation IoT anomaly detection deep learning

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Acoustic User Authentication with Smartphones

Acoustic User Authentication with Smartphones

引用

ieee international conference on Microwaves, Communications, Antennas, Biomedical Engineering and Electronic systems (COMCAS)

作者： Gevirtzer, Neta Ben Yosef, Ofir Eilam, Alon Technion Israel Inst Technol Andrew & Erna Viterbi Fac Elect & Comp Engn Signal & Image Proc Lab SIPL Haifa Israel

ISBN: (纸本)9798350348194;9798350348187

This paper describes a work on an acoustic user authentication system using smartphones. The system implements two-factor authentication for Windows workstations, where the authentication procedure, including locking and unlocking the workstation is transparent to the user. Since workstations and smartphones have built-in microphones and speakers, the system does not require additional hardware. The uniqueness of the solution is being based on acoustic signals. These signals are transmitted by the user's smartphone and received by the workstation microphone. The system is "pure play acoustic" since no wiring or radio transmission is used. The system configuration supports multiple users in the same area. Eavesdropping prevention is provided by sequentially generated random one-time keys. Acoustic communication can be applied either in the audible range or beyond the human hearing range depending on the sampling rate of the smartphone and the workstation.

关键词： Acoustic Android signal Processing Smartphone Two Factor Authentication Windows

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Depth Map Estimation from a Single 2D image 17

Depth Map Estimation from a Single 2D Image

引用

17th international conference on signal-image technology and internet-based systems, SITIS 2023

作者： Suarez, Patricia L. Carpio, Dario Sappa, Angel Espol Polytechnic University Guayaquil Ecuador Computer Vision Center Bellaterra Barcelona08193 Spain

ISBN: (纸本)9798350370911

This paper presents an innovative architecture based on a Cycle Generative Adversarial Network (CycleGAN) for the synthesis of high-quality depth maps from monocular images. The proposed architecture leverages a diverse set of loss functions, including cycle consistency, contrastive, identity, and least square losses, to facilitate the generation of depth maps that exhibit realism and high fidelity. A notable feature of the approach is its ability to synthesize depth maps from grayscale images without the need for paired training data. Extensive comparisons with different state-of-the-art methods show the superiority of the proposed approach in both quantitative metrics and visual quality. This work addresses the challenge of depth map synthesis and offers significant advancements in the field. © 2023 ieee.

关键词： Generative adversarial networks

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Patch-based Transformer for Low-Light image Enhancement 16

Patch-Based Transformer for Low-Light Image Enhancement

引用

international conferences on internet of Things (iThings) / Green Computing and Communications (GreenCom) / Cyber, Physical and Social Computing (CPSCom) / Smart Data, SmartData and Congress on Cybermatics (Cybermatics)

作者： Zhang, Yu Jiang, Shan Tang, Xiangyun Minzu Univ China Key Lab Ethn Language Intelligent Anal & Secur Go Minist Educ Beijing 100081 Peoples R China Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

ISBN: (纸本)9798350309461

images captured in low-light settings often manifest issues, notably reduced brightness and amplified noise. Addressing these concerns, our research elucidates a sophisticated technique crafted to enhance the fidelity of images taken under such adverse circumstances. During the image acquisition phase, the camera sensor meticulously logs the raw RGB data. This information subsequently undergoes a comprehensive optimization and refinement process facilitated by the camera's image signal Processor (ISP), culminating in the finalized RGB depiction. We delve deeply into the intricacies of the ISP processing trajectory and unveil a methodology termed PLET (Patch-based Low-light Enhancement Transformer), dedicated to refining low-light image quality to mirror that of well-lit conditions. For an intricate processing and comprehensive analysis of the image constituents, we employ the image patch approach, achieving not only pinpointed image refinement but also a profound comprehension of image content. Rigorous experimental assessments reveal that our approach excels in elevating image luminance and curtailing noise, cementing its efficacy in low-light image enhancement endeavors.

关键词： low-light settings image signal processor RGB data image patch image luminance and noise reduction

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A Deep Learning-based Underwater image Enhancement Scheme for Turbid Underwater Aquaculture Environments 6

A Deep Learning-Based Underwater Image Enhancement Scheme fo...

引用

6th international conference on Computer Communication and the internet (ICCCI)

作者： Hu, Wu-Chih Chen, Liang-Bi Yu, Po-Ju Wu, Ming-Yuan Chen, Kai-Hung Huang, Xiang-Rui Natl Penghu Univ Sci & Technol Dept Comp Sci & Informat Engn Magong Penghu Taiwan

ISBN: (纸本)9798350351774;9798350351767

In outdoor aquaculture farms, farmers are often unable to observe the situation in aquaculture tanks due to poor water quality, which may cause them to fail to detect problems and take timely measures, especially for typical benthic aquatics such as white shrimp. For example, when the harmful substances in the water exceed the normal range, the farmer may not treat them quickly, and the white shrimp will grow slowly or die. Therefore, for underwater water quality image enhancement, this paper proposes an underwater image enhancement scheme for turbid underwater aquaculture environments based on deep learning. This scheme can effectively improve underwater images' clarity, thereby improving the effect of underwater monitoring. Through this technology, farmers can more clearly observe the conditions in aquaculture ponds, improving aquaculture efficiency and promoting the development of the aquaculture industry.

关键词： intelligent aquaculture underwater underwater image enhancement deep learning white shrimp turbid underwater

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DNA-Inspired image Encryption: Leveraging Chaos for Enhanced Security 20

DNA-Inspired Image Encryption: Leveraging Chaos for Enhanced...

引用

20th Annual international conference on Distributed Computing in Smart Sensor systems and the internet of Things (DCOSS-IoT)

作者： Alawida, Moatsum Aljaghbeir, Ahmad Nasser Albaz, Khaled Raed Alnuaimi, Rahaf Adam Abu Dhabi Univ CSIT Dept Coll Engn Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350369458;9798350369441

In the realm of the Industrial internet of Things (IIoT), safeguarding visual data is paramount to prevent unauthorized access and tampering. This paper presents a new approach to image encryption, harnessing the power of DNA sequences and chaos models tailored for IIoT environments. The proposed algorithm employs DNA trees to establish a unique correspondence between pixels and DNA bases, facilitating robust encryption. The secret key, an integral component, offers independence and flexibility in generating chaotic variables crucial for encryption. The encryption process encompasses DNA sequence conversion, chaos-based image generation, and multi-round permutation and substitution steps. Experimental results underscore the algorithm's efficacy, demonstrating better statistical security for cipher images within short timeframes. Histograms exhibit flat distributions with low correlation values, approaching theoretical entropy values. Comparative performance analysis reveals the algorithm's superiority over state-of-the-art counterparts. This innovative algorithm holds promise for embedding devices within IIoT environments, enabling efficient pre-processing steps and facilitating the generation of cipher images with minimal energy consumption. Its versatility extends to accommodating various image sizes, ensuring dynamic image cipher creation with high-quality security measures.

关键词： internet of Things (IoT) image Encryption DNA Sequences Chaos Models Cybersecurity

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ATTENTION-based SPATIAL-FREQUENCY INFORMATION NETWORK FOR UNDERWATER SINGLE image SUPER-RESOLUTION 49

ATTENTION-BASED SPATIAL-FREQUENCY INFORMATION NETWORK FOR UN...

引用

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

作者： Pramanick, Alik Megha, Dhruvil Sur, Arijit Indian Inst Technol Guwahati Dept Comp Sci & Engn Gauhati India

ISBN: (纸本)9798350344868;9798350344851

Underwater single image super-resolution (UISR) is a challenging task as these images frequently suffer from poor visibility. The best-published UISR works continue to suffer from color degradation, poor texture representation, and loss of finer (high-frequency) details. We propose a novel deep learning-based (DL) UISR model that incorporates spatial information as well as the transformed (wavelet) coefficient of degraded low-resolution (LR) underwater images by intelligent feature management. To ensure the visual quality of the super-resolved image, color channel-specific L1 loss, perceptual loss, and difference of Gaussian (DoG) loss are used in tandem with SSIM loss. We employ publicly available datasets, namely UFO-120 and USR-248, to evaluate the proposed model. The results of our experiments show that our model outperforms existing state-of-the-art methods (e.g., similar to 9.45%/similar to 1.77% in SSIM and similar to 0.91%/similar to 1.44% in PSNR on UFO-120/USR-248 x4, respectively), as demonstrated through quantitative measurements and visual quality assessments.

关键词： Underwater image super-resolution spatial domain frequency domain deep learning

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A Machine Learning-based Intelligent ID System for the internet of Things

A Machine Learning-based Intelligent ID System for the Inter...

引用

international conference on Advanced systems and Emergent Technologies (ICASET)

作者： Bacha, Sawssen Liouane, Noureeddine Univ Monastir Natl Engn Sch Monastir Lab Automat Signal & Image Proc Monastir Tunisia

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901;9798350384895

The internet of Things, or IoT, is a rapidly expanding field that has been integrated into numerous different industries. Thanks to this technology, devices may send, receive, and analyze data without the assistance of a human. IoT security and privacy concerns continue to be a significant obstacle, despite the fact that it has gained widespread acceptance in a number of important domains due to its ability to simplify human life and enhance service quality. To protect IoT networks from different attacks, an anomaly-based intrusion detection system (IDS) can be included as a security feature. In order to combat various cyberattacks in internet of Things environments, this study suggests an anomaly-based intrusion detection system (IDS). The suggested approach makes use of in order to enhance anomaly identification performance and reduce the dimension of the data characteristics..

关键词： internet of Things (IoT) Intrusion detection cybersecurity feature selection

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Automated Detection of image-based Abusive Bengali Memes Using Ensemble and Deep Learning Models

Automated Detection of Image-Based Abusive Bengali Memes Usi...

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2024 ieee international conference on signal Processing, Information, Communication and systems, SPICSCON 2024

作者： Rukaiya Amin, Md Rabiol Jahan, Israt Karim, Syed Anayet Department of CSE CCN University of Science and Technology Cumilla Bangladesh Department of CSE East West University Dhaka Bangladesh Department of Natural Science Port City International University Chattogram Bangladesh

ISBN: (纸本)9798331510213

In the realm of internet communication, memes have emerged as a powerful means of expression, swiftly disseminating emotions and ideas. The ability to classify memes, particularly in languages with regional nuances such as Bengali, poses unique challenges. The primary question of this study addresses how to efficiently classify Bengali memes, given their diverse and context-specific nature. The motivation for this research stems from the growing significance of memes as a form of internet communication and the need for effective methods to categorize and understand them. This work presents here a computational model of classifying Bengali memes using convolutional neural networks. Convolutional neural networks have the potential to learn, adapt, and rearrange themselves. Alongside employing traditional machine learning models—Support Vector Machine (SVM), Logistic Regression, and Random Forest for meme classification. The experiment illustrated a CNN model with Bi-LSTM layers is implemented, achieving a test accuracy of 69.49%. Additionally, Among the machine learning models, the Random Forest achieves the highest accuracy of 64%. Our study shows that this comparison between CNN-based and traditional models provides valuable insights for future research in meme analysis and content moderation. © 2024 ieee.

关键词： Convolutional neural networks

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