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Mixed Residual Convolutions with Vision Transformer in Hyperspectral image Classification 22

Mixed Residual Convolutions with Vision Transformer in Hyper...

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22nd ieee International conference on Communication Technology, ICCT 2022

作者： Cao, Ying Wang, Yang Yin, Zhijian Yang, Zhen School of Communications and Electronics Jiangxi Science and Technology Normal University Nanchang China

ISBN: (纸本)9781665470674

In recent years, deep learning methods represented by convolutional neural networks (CNN) have gradually become a research focus in the field of hyperspectral image classification (HSI). Although the proposed CNN-based methods have the advantages of spatial feature extraction, they are difficult to handle the spectral feature. We propose a mixed residual convolutions with Vision Transformer model (MRViT), which uses ViT Transformer (ViT) to overcome this limitation. First, using principal component analysis (PCA) and a channel shift strategy to construct a module to process HSI data. Then, a mixed residual convolution is constructed to extract spectral-spatial features. Finally, the important feature information is enhanced by the ViT model. In this paper, four datasets are used for experimental analysis, which confirms that the performance of MRViT model is superior to other HSI classification models. The research content of this paper enriches related research in HSI classification, and also provides certain reference value for subsequent research. © 2022 ieee.

关键词： Principal component analysis

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Two-Stage Robust Reversible image Watermarking Based on Deep Neural Network 8th

Two-Stage Robust Reversible Image Watermarking Based on Deep...

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8th International conference on Artificial Intelligence and Security , ICAIS 2022

作者： Huang, Jie Li, Fengyong Zhang, Lei Qin, Chuan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Computer Science and Technology Shanghai University of Electric Power Shanghai200090 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China Computer Science Department University of Victoria VictoriaBC Canada

ISBN: (纸本)9783031067631

This paper proposes a two-stage lossless robust watermarking scheme based on deep neural network. In the first stage, the encoder, noise layer and decoder network are combined to design an end-to-end network framework, which is used to train the input watermark information and cover image. In this framework, the encoder outputs a robust watermarked image whose visual quality is similar to the cover image, and the decoder can extract the embedded watermark information to evaluate robustness. In the second stage, by using reversible information hiding algorithm with extended prediction error, the auxiliary information that can recover the cover image is embedded into the robust watermarked image to ensure reversibility. If the watermarked image does not undergo attack processing, the hidden watermark information can not only be extracted, but also the cover image can be completely recovered. Even if the watermarked image does undergo attack processing, the embedded watermark information can also be extracted correctly because of the strong robustness in the first stage. Experimental results demonstrate the robustness of proposed scheme in terms of JPEG compression, gaussian filtering noise, rotation and shearing operations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Improving 3D Pose Estimation For Sign Language

Improving 3D Pose Estimation For Sign Language

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Acoustics, Speech, and Signal processing Workshops (ICASSPW), ieee International conference on

作者： Maksym Ivashechkin Oscar Mendez Richard Bowden University of Surrey Centre for Vision Speech and Signal Processing (CVSSP) Stag Hill University Campus Guildford UK

This work addresses 3D human pose reconstruction in single images. We present a method that combines Forward Kinematics (FK) with neural networks to ensure a fast and valid prediction of 3D pose. Pose is represented as a hierarchical tree/graph with nodes corresponding to human joints that model their physical limits. Given a 2D detection of keypoints in the image, we lift the skeleton to 3D using neural networks to predict both the joint rotations and bone lengths. These predictions are then combined with skeletal constraints using an FK layer implemented as a network layer in PyTorch. The result is a fast and accurate approach to the estimation of 3D skeletal pose. Through quantitative and qualitative evaluation, we demonstrate the method is significantly more accurate than MediaPipe in terms of both per joint positional error and visual appearance. Furthermore, we demonstrate generalization over different datasets and sign languages. The implementation in PyTorch runs at between 100-200 milliseconds per image (including CNN detection) using CPU only.

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NSCT decomposition and VGG-Net based multimodal Infrared and Visible image Fusion

NSCT decomposition and VGG-Net based multimodal Infrared and...

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Integrated Circuits and Communication Systems (ICICACS), ieee International conference on

作者： G. Prema S. Arivazhagan ECE Department Mepco Schlenk Engineering College Sivakasi India

A novel technique for infrared and visible image fusion is proposed, combining the Non-subsampled Contourlet Transform (NSCT) with VGG-Net by effectively preserving edge details, contrast, and brightness. The image fusion process initiates with the NSCT decomposition of original images yielding low frequency (LF) and band-pass frequency (BF) sub-band coefficients. An energy-based fusion rule merges LF sub-band coefficients. The BF sub-band coefficients undergo processing in the VGG-19 Net to extract deep features. Using l1-norm and a block-based average operator, various candidates of fused BF sub-bands are generated. A maximum selection strategy determines the combined BF sub-band coefficients. The final step reconstructs the amalgamated image through inverse NSCT, ensuring an effective fusion. The proposed method outcomes are better and its objective and visual evaluation is done with five conventional methods and metrics such as Qabf, Entropy, FMI_Pixel, MS_SSIM, MI, SSIM are used for performance evaluation.

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ST360IQ: No-Reference Omnidirectional image Quality Assessment With Spherical Vision Transformers

ST360IQ: No-Reference Omnidirectional Image Quality Assessme...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Nafiseh Jabbari Tofighi Mohamed Hedi Elfkir Nevrez Imamoglu Cagri Ozcinar Erkut Erdem Aykut Erdem KUIS AI Center Koç University Hacettepe University AIST Japan MSK.AI

Omnidirectional images, aka 360° images, can deliver immersive and interactive visual experiences. As their popularity has increased dramatically in recent years, evaluating the quality of 360° images has become a problem of interest since it provides insights for capturing, transmitting, and consuming this new media. However, directly adapting quality assessment methods proposed for standard natural images for omnidirectional data poses certain challenges. These models need to deal with very high-resolution data and implicit distortions due to the spherical form of the images. In this study, we present a method for no-reference 360° image quality assessment. Our proposed ST360IQ model extracts tangent viewports from the salient parts of the input omnidirectional image and employs a vision-transformers based module processing saliency selective patches/tokens that estimates a quality score from each viewport. Then, it aggregates these scores to give a final quality score. Our experiments on two benchmark datasets, namely OIQA and CVIQ datasets, demonstrate that as compared to the state-of-the-art, our approach predicts the quality of an omnidirectional image correlated with the human-perceived image quality. The code has been available on https://***/Nafiseh-Tofighi/ST360IQ

关键词： image quality Geometry visualization Codes Media Transformers Data models

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification 8

Point-Level and Set-Level Deep Representation Learning for C...

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8th ieee International conference on Computer and communications, ICCC 2022

作者： Hu, Jihui Ye, Pengfei Li, Danyang Dong, Lingyun Chen, Xiaopan Zhu, Xiaoke School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China

ISBN: (纸本)9781665450515

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach. © 2022 ieee.

关键词： Infrared imaging

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Dynamic Template Update Mechanism for visual Object Tracking

Dynamic Template Update Mechanism for Visual Object Tracking

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： YongQi Hu ZhongWang Cai ZiJun Nie ZiZheng Yu Ying Cao Zhijian Yin Zhen Yang School of Communications and Electronics Jiangxi Science and Technology Normal University Nanchang China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

Given the requirements for robust target classification and accurate target state estimation in visual tracking, SiamFC++ proposes a set of practical guidelines for designing high-performance general-purpose trackers by considering the special nature of visual tracking problems. Inspired by dynamic modules, We propose an empirical method for integrating a dynamic module into the image input, which is concatenated with the template module after feature maps are extracted by the backbone network. Since the position and shape of the object can change significantly within a video sequence, the added dynamic module can better focus on the target region of the feature map to obtain better similarity maps. Extensive experiments and comparisons demonstrate that our simple and effective method achieves reliable results on the benchmarks of LaSOT, TrackingNet, and GOT-10K and provides a significant speed advantage in real-time.

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Enabling Collaborative and Green Generative AI Inference in Edge Networks

Enabling Collaborative and Green Generative AI Inference in ...

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ieee conference on Global communications (GLOBECOM)

作者： Meng Tian Zhicheng Liu Chao Qiu Xiaofei Wang Dusit Niyato Victor C. M. Leung College of Intelligence and Computing Tianjin University Tianjin China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm’s ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content.

关键词： Energy consumption Adaptation models Generative AI Computational modeling image edge detection Green products Collaboration Inference algorithms Energy efficiency Internet of Things

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An efficient algorithm for cancelable biometric recognition based on noise magnification 2

An efficient algorithm for cancelable biometric recognition ...

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2nd ieee International conference on Electronic Engineering, ICEEM 2021

作者： Farouk Al-Libady, Abd El-Rahman Ashiba, Huda El Banby, Ghada M. El-Fishawy, Adel S. Dessouky, Moawad I. Abd El-Samie, Fathi E. El-Rabaie, El-Sayed M. Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Egypt Zagazige University Bilbis Higher Institute of Engineering Department of Electronics and Electrical Communications Engineering Egypt Menoufia University Faculty of Electronic Engineering Department of Industrial Electronics and Control Engineering Egypt Menoufia University Faculty of Electronic Engineering Communications and Electronics Department Menouf Egypt Manoufia University: Menoufia University Faculty of Electronic Engineering Communications and Electronics Department Egypt

ISBN: (纸本)9781665418423

More recently, biometric systems have spread for modern security applications. Unfortunately, these systems have experienced several attempts of hacking. If biometric databases are compromised and stolen, biometrics in these databases will be lost forever. Consequently, there is an immediate need to introduce new upgradable biometric systems. The concept behind cancelable biometrics is to convert biometric data to alternative templates, which cannot be easily used by the impostor or intruder, and can be eliminated if breached. In this paper, the inverse filter is utilized in a cancelable face recognition system. In this system, masked biometric images are generated by blurring, noise addition and then inverse filtering. It is well-known in the image processing theory that inverse filtering leads to noise enhancement, which is an undesired effect in image restoration. In contrary, this effect will be desired in cancelable biometric systems. If the noise is magnified with an appropriate extent, it can mask the original biometrics leading to cancelable templates. This is the theory behind the proposed system. The proposed system is applied on the Olivetti and Oracle (ORL) dataset. Simulation results using evaluation metrics such as non-invertibility, unlinkability, visual inspection, False Positive Rate (FPR), False Negative Rate (FNR), Equal Error Rate (EER), Decidability, correlation coefficient, Area under the Receiver Operating Characteristic (AROC) curve demonstrate that the proposed system is resistant to intruders and hackers. Hence, it is efficient for several security applications. © 2021 ieee.

关键词： Biometrics

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U-Net-based medical image segmentation algorithm 13

U-Net-based medical image segmentation algorithm

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13th International conference on Wireless communications and Signal processing, WCSP 2021

作者： Yan, Weidan Chen, Can Zhang, Dengyin School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9781665407854

Before the deep learning algorithm, there are many traditional image segmentation methods. However, with the rapid development of artificial intelligence technology, the requirements for the accuracy and efficiency of image segmentation are getting higher and higher, so the image segmentation algorithm based on deep learning arises as the times require. But it is worth noting that these algorithms are natural image processing, and medical image format diversification, the difference of pixel value range, the presence of noise and artifacts, and so on, using the general image segmentation algorithms cannot meet medical demand scenarios medical image segmentation. On the basis of U-Net model, this paper improves its sub-modules, and proposes four sub-module structures: Path direct connection, Dropout direct connection, Conv direct connection, and Constant scale. Dataset uses Skin lesions of melanoma, and accuracy, sensitivity, specificity, precision, F-Measure, IOU, Dice coefficient and comprehensive score were selected as model performance evaluation indexes. After model training, validation and testing, the conclusion was drawn: In the segmentation task of melanoma skin disease image, the performance of Constant scale model was the best among all experimental models. © 2021 ieee.

关键词： image segmentation

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