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

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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...

引用

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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LWT-based Watermarking Scheme for image Authentication 3rd

LWT-Based Watermarking Scheme for Image Authentication

引用

3rd international conference on Network Security and Blockchain technology

作者： Jana, Sachchidananda Pal, Pabitra Mahadani, Asim Kumar Chowdhuri, Partha Giri, Debasis Prabhat Kumar Coll Dept Comp Sci & Applicat Contai 721404 India Maulana Abul Kalam Azad Univ Technol Dept Comp Applicat Nadia 741249 India Bankura Unnayani Inst Engn Dept Comp Sci & Engn Bankura WB India Vidyasagar Univ Dept Comp Sci Midnapore 721102 India Maulana Abul Kalam Azad Univ Technol Dept Informat Technol Nadia 741249 India

ISBN: (纸本)9789819780501;9789819780518

Digital image authentication is a critical process in ensuring the integrity and ownership of digital images, especially in the age of widespread digital manipulation and unauthorized use. In this manuscript, an image watermarking scheme has been proposed by combining the Lifting Wavelet Transform (LWT) and the Least Significant Bit (LSB) for image authentication. The goal of the proposed scheme is to achieve robustness against various attacks while maintaining the high-quality visual appearance of the watermarked image. Experimental evaluations are conducted to assess the performance of the proposed hybrid watermarking scheme. Various metrics, including Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Correlation (NC), are employed to measure the visual quality, robustness, and authentication accuracy of the watermarked image. The results demonstrate the effectiveness of the proposed scheme in achieving high-quality visual appearance, robustness against attacks, and accurate image authentication.

关键词： Watermarking LWT LSB Authentication Secret key

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Comparative Evaluation of Fixed Windowing Strategies on CT Brain images Using Multiple Deep Learning Models 17

Comparative Evaluation of Fixed Windowing Strategies on CT B...

引用

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

作者： Viriyavisuthisakul, Supatta Kaothanthong, Natsuda Sanguansat, Parinya Yamasaki, Toshihiko Songsaeng, Dittapong Sirinhorn International Institute of Technology School of Management Technology Thammasat University Pathumthani Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand The University of Tokyo Department of Electrical Engineering Tokyo Japan Mahidol University Siriraj Hospital Faculty of Medicine Department of Radiology Bangkok Thailand

ISBN: (纸本)9798350370911

Window setting in CT brain images is the crucial pre-processing step to examine the abnormalities for diagnosing disease. Recently, many methods have been proposed to determine the suitable window automatically instead of fixing the window. However, fixed windowing methods may still be used in clinical practice due to their simplicity and ease of use. Here, we propose to evaluate the 45 different fixed windowing in noncontrast cranial computer tomography (NCCT) images without computer tomography perfusion (CTp). The 15 latest deep learning models are performed on all interested windows to classify between the hyperacute or acute phases of ischemic stroke and normal brain. The experiments can provide the reference fixed windowing value to optimize the deep learning model and help clinicians to choose the appropriate fixed windowing values. © 2023 IEEE.

关键词： image classification

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Learning CRF potentials through fully convolutional networks for satellite image semantic segmentation 17

Learning CRF potentials through fully convolutional networks...

引用

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

作者： Pastorino, Martina Moser, Gabriele Serpico, Sebastiano B. Zerubia, Josiane University of Genoa Diten Dept. Italy Inria Université Côte d'Azur Sophia-Antipolis France

ISBN: (纸本)9798350370911

This paper introduces a method to automatically learn the unary and pairwise potentials of a conditional random field (CRF) from the input data in a non-parametric fashion, within the framework of the semantic segmentation of remote sensing images. The proposed model is based on fully convolutional networks (FCNs) and fully connected neural networks (FCNNs) to extensively exploit the semantic and spatial information contained in the input data and in the intermediate layers of an FCN. The idea of the model is twofold: first to learn the statistics of a CRF via a convolutional layer, whose kernel defines the clique of interest, and, second, to favor the interpretability of the intermediate layers as posterior probabilities through the FCNNs. The method was tested with the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset, after modifying the ground truths to approximate the ones found in realistic remote sensing applications, characterized by scarce and spatially non-exhaustive annotations. The results confirm the effectiveness of the proposed technique for the semantic segmentation of satellite images. © 2023 IEEE.

关键词： Semantic Segmentation

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Compact colorful compressive spectral imager based on deep learning reconstruction

Compact colorful compressive spectral imager based on deep l...

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2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Li, Jinshan Ma, Xu Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781510680425

Leveraging the spatio-spectral modulation and sophisticated reconstruction algorithms, the colorful compressive spectral imaging (CCSI) method can reconstruct a three-dimensional spectral image from a single compressive *** CCSI systems enhance the modulation freedom through the combination of colorful coding mask (CCM) and dispersive element, but this kind of system has complex structure that limits the miniaturization of ***, the reconstruction quality of CCSI systems can be further improved by using deep learning *** paper proposes a compact CCSI method based on deep learning reconstruction, which tries to reduce the volume of system by attaching the CCM to the *** combination of CCM and RGB detector enhances the modulation ***, a Transformer-based deep learning algorithm is used to obtain promising reconstruction results of the target spectral *** of both simulations and experiments demonstrate the effectiveness of the proposed compact CSSI method. © 2024 SPIE.

关键词： image compression

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Boosting Guided Super-Resolution Performance with Synthesized images 17

Boosting Guided Super-Resolution Performance with Synthesize...

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

Guided image processing techniques are widely used for extracting information from a guiding image to aid in the processing of the guided one. These images may be sourced from different modalities, such as 2D and 3D, or different spectral bands, like visible and infrared. In the case of guided cross-spectral super-resolution, features from the two modal images are extracted and efficiently merged to migrate guidance information from one image, usually high-resolution (HR), toward the guided one, usually low-resolution (LR). Different approaches have been recently proposed focusing on the development of architectures for feature extraction and merging in the cross-spectral domains, but none of them care about the different nature of the given images. This paper focuses on the specific problem of guided thermal image super-resolution, where an LR thermal image is enhanced by an HR visible spectrum image. To improve existing guided super-resolution techniques, a novel scheme is proposed that maps the original guiding information to a thermal image-like representation that is similar to the output. Experimental results evaluating five different approaches demonstrate that the best results are achieved when the guiding and guided images share the same domain. © 2023 IEEE.

关键词： image enhancement

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Deep hash image retrieval based on multi-head attention mechanism 24

Deep hash image retrieval based on multi-head attention mech...

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2024 international conference on Virtual Reality, image and signal Processing, ICVISP 2024

作者： Yang, Jing Zhang, Sengbao Wang, Tianlin Gao, Shang Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798400710926

In the internet era, massive amounts of image data have em erged. How to efficiently perform accurate and efficient image retrieval from this data has become a crucial issue. Most methods are affected by the redundant information in the extracted features and And it cannot extract effective information from the image, resulting in a decrease in image retrieval performance and accuracy. To address this challenge, we introduce a deep hashing algorithm that integrates the multi-head attention mechanism. This mechanism is incorporated within a convolutional neural network, allowing the hash network to adaptively learn and focus on features from various distributions within images. Furthermore, we have redefined the hash loss function to enhance the effectiveness and expressive capability of hash encoding in feature representation. Extensive experiments conducted on CIFAR-10 and NUS-WIDE datasets demonstrate that our proposed method achieves superior performance compared to existing approaches. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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TGIEN: An Interpretable image Editing Method for IoT Applications based on Text Guidance 17

TGIEN: An Interpretable Image Editing Method for IoT Applica...

引用

IEEE Congress on Cybermatics: 17th IEEE international conference on internet of Things, iThings 2024, 20th IEEE international conference on Green Computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE international conference on Smart Data, SmartData 2024

作者： Wang, Yifei Pan, Mengzhu Li, Qianmu Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350351637

In response to the demands and challenges of image editing in the internet of Things (IoT) domain, we have proposed an interpretable image editing method guided by text(TGIEN), aiming to address the limitations of existing image editing models based on Generative Adversarial Networks (GANs) for better application in IoT. This method utilizes a Contrastive Language-image Pre-trained Model (CLIP) to guide the editing of the StyleGAN latent space, enabling manipulation of real images through text prompts. The model incorporates a mapping module conditioned on text embeddings to edit the latent space code of StyleGAN and learns an attention map corresponding to the text across all layers of StyleGAN. These attention maps highlight focus areas during editing, providing an intuitive explanation of the model's behavior. Additionally, during the image generation process, the attention map functions as a mask to guide local fusion between original and modified features. Furthermore, the model integrates editability losses to ensure that editing operations align closely with the text prompts. Through the aforementioned design, spatial disentanglement is explicitly achieved, enhancing the transparency and interpretability of the model and in turn increasing the capacity of image editing technology in the IoT domain for practical application and system optimization. Both qualitative and quantitative experiments demonstrate the superiority of this method over other state-of-the-art methods in terms of editing quality and decoupling of edits. In the IoT domain, this method holds promise for applications such as smart surveillance systems, smart healthcare and virtual reality, offering more intelligent and efficient image processing and analysis for these scenarios. © 2024 IEEE.

关键词： Generative adversarial networks

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Research on image signal Enhancement and Denoising Algorithms for Intelligent Communication systems

Research on Image Signal Enhancement and Denoising Algorithm...

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2024 international conference on Information technology, Communication Ecosystem and Management, ITCEM 2024

作者： Luo, Shenghan Wang, Xinyan Southeast University Nanjing211189 China Xuzhou Pharmaceutical Vocational College Xuzhou221116 China

ISBN: (纸本)9798331542696

The quality of image signals directly affects the performance of intelligent communication systems. This paper proposes a set of image enhancement and denoising algorithms to address image quality degradation in intelligent communication systems. For image enhancement, we designed an adaptive histogram equalization algorithm based on blocks and a contrast adaptive optimization method, and implemented a detail enhancement algorithm combining multi-scale edge detection and enhancement. In terms of image denoising, we proposed adaptive median filtering, improved soft-threshold wavelet domain denoising, and non-local means algorithms to effectively suppress various types of noise. The system is developed using a hybrid \mathrm{C}++ and Python framework with parallel processing achieved through multithreading technology. Experimental results show that the proposed algorithms significantly improve image quality, processing efficiency, and system stability, providing reliable image signal processing support for intelligent communication systems. © 2024 IEEE.

关键词： C++ (programming language)

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Lessons from applying SRGAN on Sentinel-2 images for LULC classification 17

Lessons from applying SRGAN on Sentinel-2 images for LULC cl...

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

作者： Goh, Yun Si Chua, Wen Qing Yean, Seanglidet Lee, Bu Sung Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350370911

Satellite images are commonly used to monitor land use land cover (LULC) changes. Unfortunately, publicly available images often lack the resolution required for detailed urban studies. In this study, we enhanced the resolution of Sentinel-2 (S2) satellite images from 10 meters to 2.5 meters using two super-resolution models: Real-SR and Real-ESRGAN. We tested the suitability of the enhanced images for LULC classification of an urban city, Singapore. From our results, colors have mostly been preserved and man-made objects have become sharper. However, the enhanced images also exhibit colour change, darkening, and salt-and-pepper effects. At this stage, there is no conclusive evidence that enhanced images can improve LULC classification. In fact, they have worsened classification accuracy by 17 - 30%, and the Kappa coefficient by 0.2 - 0.4. Although our application of super-resolution on LULC classification is not successful, it is a first attempt and could be further improved. © 2023 IEEE.

关键词： image classification

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