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Effectiveness Evaluation of Basic Bitstream Features in Surveillance Video Recompression Forensics Using VVC

Effectiveness Evaluation of Basic Bitstream Features in Surv...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Gong, Yanchao Li, Kunlei Zhou, Xiaokai Jing, Jiaxi Yang, Kaifang Liu, Ying Xi'An University of Posts and Telecommunications School of Communications and Information Engineering Xi'an China Shaanxi Normal University School of Computer Science Xi'an China

ISBN: (纸本)9798350359855

Surveillance videos have been widely used in criminal investigation, judicial evidence collection, and other fields. The authenticity of surveillance video is a key prerequisite for whether the surveillance video is used legally. Video recompression forensics (VRF) is a widely recognized technique for video authenticity authentication. And basic bitstream feature (BBF) extraction is the primary step in VRF, which significantly affects the accuracy of VRF. The current VRF methods have not systematically focused on the different impact of different types of BBF on VRF. Most VRF methods directly extract the BBF from video bitstream to construct the advanced bitstream features, which implies that all BBF have an equal impact on the accuracy of VRF. This operation is obviously unreasonable. This paper focuses on VVC encoded surveillance videos, and an experimental scheme is first designed to evaluate the effectiveness of BBF. Based on the experimental data, the impact of different types of BBF on the accuracy of VRF is further analyzed and evaluated, and some important conclusions are drawn. These conclusions have important guiding significance for the subsequent development of efficient VRF methods suitable for surveillance videos using VVC. © 2023 IEEE.

关键词： Authentication

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Adaptive SLIC-Based Fuzzy Intensity Dissimilarity Thresholding for Color image Segmentation 5

Adaptive SLIC-Based Fuzzy Intensity Dissimilarity Thresholdi...

引用

5th International conference on Natural Language processing, ICNLP 2023

作者： Rong, Lan Danlin, Feng Feng, Zhao Haiyan, Yu Lu, Zhang Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an710121 China

ISBN: (纸本)9798350302219

In order to make full use of the color information of the image and improve the accuracy of color image segmentation, this paper proposes an adaptive SLIC-based fuzzy intensity dissimilarity thresholding for color image segmentation, which does not need gray conversion. Firstly, the proposed algorithm adaptively selects the number of super-pixels through the sum of image information and image complexity, and uses SLIC technology to extract image super-pixels;Then, the median value of each channel pixel in each super-pixel block is used as the super-pixel value to calculate the super-pixel intensity information, and the super-pixel intensity histogram is counted;Finally, an intensity dissimilarity function based on IT2FS is constructed to search the optimal threshold. On Berkeley images and Weizmann images, the proposed algorithm is compared with the five related algorithms. The experiments show that the proposed algorithm has achieved good results in terms of visual effects and evaluation indicators, which proves the effectiveness of the algorithm. © 2023 IEEE.

关键词： Pixels

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Deep Dilated Convolutional Network for Single image Dehazing 7th

Deep Dilated Convolutional Network for Single Image Dehazing

引用

7th International conference on Computer Vision and image processing, CVIP 2022

作者： Deivalakshmi, S. Sandana, J. Sudaroli Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031314162

The visual quality of the images gets decreased due to bad weather conditions. The image captured under hazy weather conditions have serious attenuation in terms of color and saturation. In addition, these hazy images have very low contrast and the visual quality will be drastically poor. Moreover, object detection in hazy environment is too challenging. So, single image dehazing is a demanding, challenging and ill-posed problem. In this paper, we propose a 9-layer convolutional neural network with deep dilated filters of different dilation rates to achieve an end-to-end mapping from haze image to haze free image. Exponential expansion of receptive field is possible with the dilated filters without increasing the model complexity. Furthermore, the dilated convolutional layers help for efficient model compactness. We did experiments on synthetic dataset and on naturally obtained hazy images. The results show that our network achieves outstanding performance over the existing algorithms in terms of PSNR, SSIM and visual quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Convolutional neural networks

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QRFPose: Query-Based 3D Pose Estimation using Radio Signals 16

QRFPose: Query-Based 3D Pose Estimation using Radio Signals

引用

16th International conference on Wireless communications and Signal processing, WCSP 2024

作者： Wan, Hong Song, Ruiyuan Xie, Chunyang Lu, Zhi Chen, Qi Wu, Zhi Zhang, Dongheng Hu, Yang Chen, Yan School of Cyber Science and Technology University of Science and Technology of China Hefei China Qianyuan National Laboratory Hangzhou China School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9798350390643

The major challenge for radio-frequency-based (RF) multi-person pose estimation is to solve the problem of the global information-sparsity and local information-dense in RF signals. Unlike the visual image that the human body is located in a determined region, the majority of information regarding the human body in RF signals is concentrated in localized areas, with a small portion dispersed across global regions. However, the existing works directly follow the divide-and-conquer strategy to detect candidate regions where people may exist, and then zoom into each local region to generate human skeletons, while the information beyond local information-dense regions is ignored resulting in performance degradation. To resolve the problem, we propose a novel query-based multi-person pose estimation framework, QRFPose, to view body joints as learnable query embeddings to adaptively focus on features related to target skeleton joints. Moreover, QRFPose is carefully designed to take the geometric prior of horizontal and vertical RF signals into consideration. By leveraging the projective deformable attention rather than the naive global attention mechanism, QRFPose works more efficiently. The experimental results show that QRFPose plays favorably against state-of-the-art approaches in terms of both accuracy and efficiency. © 2024 IEEE.

关键词： image segmentation

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Mapping engineering communications based on radiovision and augmented reality technologies 20

Mapping engineering communications based on radiovision and ...

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20th International conference on Ground Penetrating Radar, GPR, 2024

作者： Khmelev, Vitaly Tseplyaev, Ilya Shipilov, Sergey Fedyanin, Ivan Dimov, Maxim Satarov, Rail Romanov, Dmitry Radiophysics Faculty Tomsk State University Tomsk634050 Russia Limited Liability Company Radiovision Russia Research and Production Association Terrazond Moscow Russia

The article presents a mathematical apparatus of radio wave tomosynthesis for obtaining a radar image using an ultra-wideband multi-channel antenna array. Also presented are the results of processing radar data and the resulting three-dimensional radio tomogram of inhomogeneities in a homogeneous layer. Using the example of restoring the shape of test objects, the possibility of isolating individual layers at the required depth is shown. By combining radio wave and video information, the possibility of implementing augmented reality is shown. This combination provides a more visual interpretation of GPR data in construction and repair tasks. © Published under licence by IOP Publishing Ltd.

关键词： Geological surveys

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Perceptual Quality Assessment for Video Frame Interpolation

Perceptual Quality Assessment for Video Frame Interpolation

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Han, Jinliang Min, Xiongkuo Gao, Yixuan Jia, Jun Sun, Lei Cao, Zuowei Luo, Yonglin Zhai, Guangtao Shanghai Jiao Tong University Institute of Image Communication and Network Engineering China Tencent China

ISBN: (纸本)9798350359855

The quality of frames is significant for both research and application of video frame interpolation (VFI). In recent VFI studies, the methods of full-reference image quality assessment have generally been used to evaluate the quality of VFI frames. However, high frame rate reference videos, necessities for the full-reference methods, are difficult to obtain in most applications of VFI. To evaluate the quality of VFI frames without reference videos, a no-reference perceptual quality assessment method is proposed in this paper. This method is more compatible with VFI application and the evaluation scores from it are consistent with human subjective opinions. A new quality assessment dataset for VFI was constructed through subjective experiments firstly, to assess the opinion scores of interpolated frames. The dataset was created from triplets of frames extracted from high-quality videos using 9 state-of-The-Art VFI algorithms. The proposed method evaluates the perceptual coherence of frames incorporating the original pair of VFI inputs. Specifically, the method applies a triplet network architecture, including three parallel feature pipelines, to extract the deep perceptual features of the interpolated frame as well as the original pair of frames. Coherence similarities of the two-way parallel features are jointly calculated and optimized as a perceptual metric. In the experiments, both full-reference and no-reference quality assessment methods were tested on the new quality dataset. The results show that the proposed method achieves the best performance among all compared quality assessment methods on the dataset. © 2023 IEEE.

关键词： Network architecture

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1st International conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

1st International Conference on Advanced Computing, Machine ...

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1st International conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

ISBN: (纸本)9783031472237

The proceedings contain 46 papers. The special focus in this conference is on Advanced Computing, Machine Learning, Robotics and Internet Technologies. The topics include: Galactic Simulation: visual Perception of Anisotropic Dark Matter;Protocol Anomaly Detection in IIoT;Agricultural Informatics & ICT: The Foundation, Issues, Challenges and Possible Solutions—A Policy Work;a Guava Leaf Disease Identification Application;text to image Generation Using Attentional Generative Adversarial Network;attention-CoviNet: A Deep-Learning Approach to Classify Covid-19 Using Chest X-Rays;a Deep Learning Framework for Violence Detection in Videos Using Transfer Learning;multi-focus image Fusion Methods: A Review;cache Memory and On-Chip Cache Architecture: A Survey;authenticating Smartphone Users Continuously Even if the Smartphone is in the User’s Pocket;Comparative Analysis of Machine Learning Algorithms for COVID-19 Detection and Prediction;machine Learning Classifiers Explanations with Prototype Counterfactual;a Systematic Study of Super-Resolution Generative Adversarial Networks: Review;Stance Detection in Manipuri Editorial Article Using CRF;deep Learning Based Software Vulnerability Detection in Code Snippets and Tag Questions Using Convolutional Neural Networks;a Comprehensive Study of the Performances of Imbalanced Data Learning Methods with Different Optimization Techniques;Smart Parking System Using Arduino and IR Sensor;quMaDe: Quick Foreground Mask and Monocular Depth Data Generation;fine-Grained Air Quality with Deep Air Learning;enhancing Melanoma Skin Cancer Detection with Machine Learning and image processing Techniques;image processing Technique and SVM for Epizootic Ulcerative Syndrome Fish image Classification.

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SRFAMap: A Method for Mapping Integrated Gradients of a CNN Trained with Statistical Radiomic Features to Medical image Saliency Maps 2nd

SRFAMap: A Method for Mapping Integrated Gradients of a CNN ...

引用

2nd World conference on Explainable Artificial Intelligence (xAI)

作者： Davydko, Oleksandr Pavlov, Vladimir Biecek, Przemyslaw Longo, Luca Technol Univ Dublin Sch Comp Sci Artificial Intelligence & Cognit Load Res Lab Dublin Ireland Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst Kiev Ukraine Warsaw Univ Technol Warsaw Poland

ISBN: (纸本)9783031638022;9783031638039

Many explainable AI methods for generating medical image saliency maps exist, but most are devoted to working on trained neural network-based models. At the same time, many medical image classification neural networks use as input radiomic features derived from images. The mathematics for radiomic feature computations are not always represented by a differentiable function, which makes it impossible to apply existing saliency map methods to obtain input image pixel attributions because they heavily rely on gradient calculation possibility. For this reason, a novel method (SRFAMap) is introduced to map the statistical radiomic feature attributions derived by applying the Integrated Gradients methods, often used in explainable AI, to image saliency maps. In detail, integrated gradients are used to compute radiomic feature attributions of chest X-ray scans for a ResNet-50 convolutional network model trained to distinguish healthy lungs from tuberculosis lesions. These are subsequently mapped to saliency maps over the original scans to facilitate their interpretation for diagnostics. Findings show that, in most cases, SRFAMap can generate saliency maps with an acceptable level of faithfulness. The increase-in-confidence metric reached at least 34%, while the Average Drop reached 38% at most. Finally, the percentage of the statistically significant target class score increases reached at least 71% on 20 random folds for the grey-level co-occurrence matrix and higher for other methods when only pixels under positive saliency map values were revealed on the blurred image. The main contribution is a method of mapping integrated gradients to saliency maps to facilitate the visual interpretation of the relevant statistical radiomic features of X-ray scans of lungs responsible for discriminating types of lesions.

关键词： Explainable artificial intelligence Radiomics Texture analysis Medical image processing Saliency map

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SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing image Dehazing Using Satellite-to-Ground NDVI Knowledge

SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remot...

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Liu, Zepeng Yang, Zhicheng Zhu, Mingye Wong, Andy Wei, Yibing Han, Mei Yu, Jun Lai, Jui-Hsin University of Science and Technology of China China Ping An Technology China PAII Inc. United States University of Wisconsin - Madison United States

ISBN: (纸本)9798350313154

image dehazing is a meaningful low-level computer vision task and can be applied to a variety of contexts. In our industrial deployment scenario based on remote sensing (RS) images, the quality of image dehazing directly affects the grade of our crop identification and growth monitoring products. However, the widely used peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) provide ambiguous visual interpretation. In this paper, we design a new objective metric for RS image dehazing evaluation. Our proposed metric leverages a ground-based phenology observation resource to calculate the vegetation index error between RS and ground images at a hazy date. Extensive experiments validate that our metric appropriately evaluates different dehazing models and is in line with human visual perception. © 2023 IEEE.

关键词： Remote sensing

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RGFormer: Residual Gated Transformer for image Captioning 23rd

RGFormer: Residual Gated Transformer for Image Captioning

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23rd Asia Simulation conference, AsiaSim 2024

作者： Jin, Zehui Chen, Kai Fang, Guoyu Tang, Dunbing Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789819772247

image captioning is a cross-modal task that combines computer vision and natural language processing. The model is required to generate an appropriate caption for the given image. To address this challenge, we proposed a Residual Gated Transformer, RGFormer, as an enhancement of Transformer architecture. The model based on CLIP and RGFormer, CRM, is then proposed for image captioning. CRM utilizes the encoder of CLIP to extract image features as a prefix to the caption. The prefix is projected into language space using RGFormer, a lightweight mapping network, and then fed into GPT-2 to generate captions. CLIP was trained on an extensive dataset comprising image-text pairs, which contains rich visual and semantic information and is exceptionally well-suited for vision-language tasks. The core idea of CRM is to reduce the disparity between visual and textual representations by using RGFormer to accomplish the cross-modal task. CRM could generate meaningful captions for diverse and large-scale datasets in a short training time without additional annotations or pre-training. Quantitative evaluation experiments show that CRM achieves results comparable to some advanced models on the COCO Caption dataset more efficiently. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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