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International Deployment of visual IoT for Disaster Mitigation 33

International Deployment of Visual IoT for Disaster Mitigati...

引用

33rd International Telecommunication Networks and Applications conference, ITNAC 2023

作者： Murata, Ken T. Kikuta, Kazutaka Nagatsuma, Tsutomu Imanaka, Hideo Pavarangkoon, Praphan National Institute of Information and Communications Technology Tokyo Japan School of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9798350317138

This paper proposes a novel methodology that utilizes a newly developed visual Internet of Things (IoT) system for resilient natural disaster mitigation. This system enables the detection of disasters through remote control functions integrated with visual IoT sensors and artificial intelligence (AI)-based image processing of images captured by these sensors. The system is designed using commercial off-the-shelf (COTS) components to reduce installation costs, making it feasible for deployment worldwide, including in developing countries. In 2023, the proposal for this system was presented to the ITUD with the aim of widespread implementation in developing nations. The paper introduces innovative methodologies for visual IoT in international disaster mitigation, accompanied by use cases and detailed technological insights. © 2023 IEEE.

关键词： Internet of things

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High-Fidelity image Style Transfer by Hybrid Transformers1

High-Fidelity Image Style Transfer by Hybrid Transformers1

引用

IEEE visual communications and image processing (VCIP)

作者： Zhe-Wei Hsu Shih-Hsuan Yang Bo-Jiun Tung Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

image style transfer is a technique in computer vision by which the artistic style of one image is applied to the content of another while keeping the structural features. image style transfer finds applications in creating artwork, design and branding, entertainment and media, and many other fields. Current image style transfer methods fail to satisfactorily retain global characteristics and local details simultaneously. This paper proposes a hybrid transformer architecture that incorporates mixed convolutional network modules. By integrating the transformer and the convolutional modules, the global and local features are captured and fused. Experimental results demonstrate that the proposed method achieves more favorable visual fidelity, reducing the combined content and style loss by at least 10% as compared with the state-of-the-art approaches.

关键词： visualization Attention mechanisms visual communication image color analysis Computer architecture Media Transformers Feature extraction Convolutional neural networks Kernel

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Motion Estimation for Quanta image Sensors Using Spatio-Temporal Priors

Motion Estimation for Quanta Image Sensors Using Spatio-Temp...

引用

IEEE visual communications and image processing (VCIP)

作者： Hiroya Fukawa Kosuke Kurihara Yoshihiro Maeda Shunichi Sato Takayuki Hamamoto Tokyo University of Science Tokyo Japan Shibaura Insititute of Technology Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Quanta image sensors are a novel paradigm in image sensor technology. Their direct application to quanta image sensors-based imaging systems is challenging because a bit-plane image is a set of binary images. In this paper, we introduce spatio-temporal priors based on the intensity invariance and smoothness characteristics of the motion vector. Specifically, we model when the image sequences align with the correct motion vector, the spatiotemporal structure becomes more consistent. Moreover, the spatial smoothness prior is incorporated through the smoothing filtering of the evaluation metrics of motion vector candidates. The experimental results show that the proposed method is more effective than conventional methods.

关键词： image sensors Measurement Smoothing methods Accuracy visual communication Filtering Motion estimation Vectors Spatiotemporal phenomena image sequences

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Research on image Recognition Method Based on Improved Neural Network 4

Research on Image Recognition Method Based on Improved Neura...

引用

4th International conference on communications, Information System and Computer Engineering, CISCE 2022

作者： Yang, Shen Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

In the 1960s, Hubel et al. proposed the concept of receptive field through the study of cat visual cortex cells[1]. In the 1980s, Fukushima [2] proposed the concept of neurocognitive machine based on the concept of receptive field, which can be regarded as the first implementation of convolutional neural network (CNN). The neurocognitive machine will decompose a visual pattern into many sub-patterns (features), and then enter the hierarchical hierarchical connected feature plane for processing. It tries to model the visual system so that it can complete recognition even when the object has displacement or slight deformation. CNN is a variant of multi-layer perceptron (MLP), developed from biologists Huber and Wessel's [3] early research on the visual cortex of cats. There is a complex structure in the cells of the visual cortex, and these cells are very sensitive to the sub-regions of the visual input space, called the receptive field.[4] CNN was proposed by Yann Lecun of New York University in 1998 [5]. In essence, CNN is widely used neural network architecture. The success lies in the local connection and weight sharing adopted by CNN. On the other hand, the complexity of the model is reduced, that is, the risk of overfitting is reduced. In this paper, using CNN as the research method, image recognition is studied by illustrating a set of comparative experiments. In the comparative experiment, I will optimize the network model of previous experiments, and finally effectively and significantly improve the efficiency of model training. © 2022 IEEE.

关键词： image recognition

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MobileViT-GAN: A Generative Model for Low Bitrate image Coding

MobileViT-GAN: A Generative Model for Low Bitrate Image Codi...

引用

IEEE visual communications and image processing (VCIP)

作者： Yifei Pei Ying Liu Nam Ling Department of Computer Science and Engineering Santa Clara University Santa Clara CA USA

Effective image coding techniques are crucial for digital image storage and transmission. Traditional methods struggle to maintain high visual quality at low bitrates. In this paper, we present MobileViT-GAN, a novel generative adversarial network (GAN) architecture for low bitrate image compression. We propose a lightweight transformer-based discriminator to improve coding performance, compared to convolutional neural network-based discriminators. Additionally, we introduce a smoothness loss function to mitigate artifacts in decoded images, further improving visual quality in low bitrate image coding. We evaluate our proposed method against traditional and state-of-the-art GAN-based image compression techniques, showcasing its superiority in terms of compression ratio and decoded image quality.

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Tracking Eye Movement for Controlling Real-Time image-Abstraction Techniques 1

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15th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 15th International conference on Computer Vision Theory and Applications (VISAPP)

作者： Soechting, Maximilian Trapp, Matthias Univ Potsdam Fac Digital Engn Hasso Plattner Inst Potsdam Germany

ISBN: (数字)9783030948931

ISBN: (纸本)9783030948931;9783030948924

Acquisition and consumption of visual media such as digital image and videos is becoming one of the most important forms of modern communication. However, since the creation and sharing of images is increasing exponentially, images as a media form suffer from being devalued, as the quality of single images are getting less and less important, and the frequency of the shared content turns to be the focus. In this work, an interactive system which allows users to interact with volatile and diverting artwork based on their eye movement only is presented. The system uses real-time image-abstraction techniques to create an artwork unique to each situation. It supports multiple, distinct interaction modes, which share common design principles, enabling users to experience game-like interactions focusing on eye-movement and the diverting image content itself. This approach hints at possible future research in the field of relaxation exercises and casual art consumption and creation.

关键词： Eye-tracking image abstraction image processing Artistic image stylization Interactive media

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image processing using Cloud for Surveillance 5

Image Processing using Cloud for Surveillance

引用

5th International conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Tiwari, Shivam Phukan, Aarhee Vedhavathy, T.R. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798400709418

In the era of digitization and big data, the world is inundated with an ever-growing volume of visual content, be it images or videos. As organizations strive to harness the potential of these multimedia data sources, there is an increasing need for advanced image processing techniques that can automate the analysis and extraction of valuable information. Amazon Web Services (AWS) Rekognition emerges as a powerful solution in this landscape, offering a comprehensive system for image and video analysis through the lens of machine learning and computer vision. This paper delves into the realm of image processing using AWS Rekognition, unveiling the transformative capabilities of this cloud-based service and its applications in various domains. As we embark on this journey, we will explore the principles, methodologies, and real-world implications of leveraging AWS Rekognition for image analysis. © 2023 ACM.

关键词： Computer vision

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Programming to Identify Exfoliated 2D Nanosheets Using Machine Learning 1

引用

10th International conference on communications, Signal processing, and Systems

作者： Li, Zhibo He, Kexin Li, Xiao Zhang, Yinghui Wang, Cheng Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin 300387 Peoples R China Tianjin Normal Univ Coll Elect & Commun Engn Dept Commun Engn Tianjin 300387 Peoples R China Tianjin Normal Univ Coll Artificial Intelligence Dept Elect & Commun Engn Tianjin 300387 Peoples R China

ISBN: (数字)9789811903908

ISBN: (纸本)9789811903908;9789811903892

Programming through machine learning methods has been gradually replacing many repetitive and tedious works. In the mission of identifying of mechanically exfoliated 2Dmaterials usingmicroscope, which is a laboriouswork inappropriate to be manually executed, the machine learning methods perform attractive potentials in the rapid and accurate targeting of available productions. Based on the algorithm of image segmentation and target positioning, this paper discusses the feasibility of program-controlled searching of 2D nanosheets from miscellaneous cracks. By intruding visual GUI displaying, the program-enabled automatic recognition may emancipate humans from the hard work.

关键词： Machine learning image segmentation visual GUI 2D material Automatic targeting

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ABLATION-CAM++: GROUPED RECURSIVE visual EXPLANATIONS FOR DEEP CONVOLUTIONAL NETWORKS 29

ABLATION-CAM++: GROUPED RECURSIVE VISUAL EXPLANATIONS FOR DE...

引用

29th IEEE International conference on image processing, ICIP 2022

作者： Salama, Ahmed Adly, Noha Torki, Marwan Applied Innovation Center Ministry of Communications and Information Technology Egypt Computer and Systems Engineering Department Alexandria University Egypt

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Recently, providing explainable deep learning models has sparked a lot of attention. In this paper, we take a further step in this direction. We introduce a time-efficient method, called Ablation-CAM++, which can generate smooth visual explanations of CNN model predictions. Our approach uses the concept of studying the ablation analysis to determine the importance of activation maps w.r.t. the target class, similar to Ablation-CAM. However, instead of focusing on the individual importance of each activation map, we group activation maps using a clustering technique. Then, we construct a binary tree for each group by recursively splitting these groups, studying the ablation of each subgroup, and applying tree pruning. We perform qualitative and quantitative evaluations of our visual explanations against Ablation-CAM and Grad-CAM. Our approach can provide visual explanations in less than half of the time of Ablation-CAM. Using average drop and average increase evaluation metrics on 2000 images of the imageNet validation set, we provide a comparison of the effect of applying different clustering techniques in our method. © 2022 IEEE.

关键词： Computer vision

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iWave3D: End-to-end Brain image Compression with Trainable 3-D Wavelet Transform

iWave3D: End-to-end Brain Image Compression with Trainable 3...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Xue, Dongmei Ma, Haichuan Li, Li Liu, Dong Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728185514

With the rapid development of whole brain imaging technology, a large number of brain images have been produced, which puts forward a great demand for efficient brain image compression methods. At present, the most commonly used compression methods are all based on 3-D wavelet transform, such as JP3D. However, traditional 3-D wavelet transforms are designed manually with certain assumptions on the signal, but brain images are not as ideal as assumed. What's more, they are not directly optimized for compression task. In order to solve these problems, we propose a trainable 3-D wavelet transform based on the lifting scheme, in which the predict and update steps are replaced by 3-D convolutional neural networks. Then the proposed transform is embedded into an end-to-end compression scheme called iWave3D, which is trained with a large amount of brain images to directly minimize the rate-distortion loss. Experimental results demonstrate that our method outperforms JP3D significantly by 2.012 dB in terms of average BD-PSNR.

关键词： Brain image compression wavelet transform lifting scheme 3-D convolutional neural networks

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