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A Robust Multilinear Mixing Model with l2,1 norm for Unmixing Hyperspectral images

A Robust Multilinear Mixing Model with l2,1 norm for Unmixin...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Li, Minglei Zhu, Fei Guo, Alan J. X. Tianjin Univ Ctr Appl Math Tianjin Peoples R China

ISBN: (纸本)9781728180687

The unmixing of hyperspectral data is a hot topic in the field of r emote s ensing. H owever, in p resence o f various types of noise, especially the noisy channels, the performance of unmixing approaches is seriously deteriorated. To enhance the robustness of the unmixing method is a subject worth studying. This paper presents a robust unmixing method based on the recently- proposed multilinear mixing model, where the l(2,1) norm is adopted in the loss function to suppress the influence of noise. The sparseness of abundance is also considered to improve the parameter estimation. The resulting optimization problem is solved by the alternating direction multiplier method (ADMM). Experiments on both synthetic and real images demonstrate the performance of the proposed unmixing strategy.

关键词： hyperspectral image nonlinear unmixing alternating direction method of multipliers robust l(2,1) norm

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Deep Blind Video Quality Assessment for User Generated Videos

Deep Blind Video Quality Assessment for User Generated Video...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Tang, Jiapeng Dong, Yu Xie, Rong Gu, Xiao Song, Li Li, Lin Zhou, Bing Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China MIGU Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728180687

As short video industry grows up, quality assessment of user generated videos has become a hot issue. Existing no reference video quality assessment methods are not suitable for this type of application scenario since they are aimed at synthetic videos. In this paper, we propose a novel deep blind quality assessment model for user generated videos according to content variety and temporal memory effect. Content-aware features of frames are extracted through deep neural network, and a patch-based method is adopted to obtain frame quality score. Moreover, we propose a temporal memory-based pooling model considering temporal memory effect to predict video quality. Experimental results conducted on KoNViD-1k and LIVE-VQC databases demonstrate that the performance of our proposed method outperforms other state-of-the-art ones, and the comparative analysis proves the efficiency of o ur t emporal p ooling model.

关键词： video quality assessment user generated videos no reference

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Wearable visually Assistive Device for Blind People to Appreciate Real-world Scene and Screen image

Wearable Visually Assistive Device for Blind People to Appre...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Ai, Jin Hu, Menghan Li, Guodong Zhai, Guangtao Zhang, Jian Li, Qingli Sun, Wendell Q. Univ Shanghai Sci & Technol Sch Med Instrument & Food Engn Shanghai Peoples R China East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Minist Educ Key Lab Articial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728180687

Due to the loss of vision, the appreciation of the real-world scene and the images displayed on the screen becomes almost impossible for blind people. In an effort to meet the needs of the blind community, we develop a wearable visually assistive device to help them perceive images. With the help of various multimedia information processing technologies, the proposed device can first acquire image information through a depth camera, then implement an image-to-text transformation using image caption technology, and finally the obtained text sequence is fed back to the user via voice. In this way, blind people are able to perceive the outside world, thus creating an unprecedented experience for them. The main technical specifications of the system are: distance perception range is 0.1m to 10m;RGB field of view is 69.4 degrees x42.5 degrees x77 degrees;depth field of view is 91.2 degrees x65.5 degrees x 100.6 degrees;maximum weight is 3.05kg. Two demo videos of the proposed navigation system which are respectively recorded for real- world scene and screen image are available at: https://***/10.6084/***.12520499. v1

关键词： blindness assistive system vision disorder

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Mining Larger Class Activation Map with Common Attribute Labels

Mining Larger Class Activation Map with Common Attribute Lab...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Zhang, Runtong Meng, Fanman Li, Hongliang Wu, Qingbo Ngan, King N. Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781728180687

Class Activation Map (CAM) is the visualization of target regions generated from classification networks. However, classification network trained by class-level labels only has high responses to a few features of objects and thus the network cannot discriminate the whole target. We think that original labels used in classification tasks are not enough to describe all features of the objects. If we annotate more detailed labels like class-agnostic attribute labels for each image, the network may be able to mine larger CAM. Motivated by this idea, we propose and design common attribute labels, which are lower-level labels summarized from original image-level categories to describe more details of the target. Moreover, it should be emphasized that our proposed labels have good generalization on unknown categories since attributes (such as head, body, etc.) in some categories (such as dog, cat, etc.) are common and class-agnostic. That is why we call our proposed labels as common attribute labels, which are lower-level and more general compared with traditional labels. We finish the annotation work based on the PASCAL VOC2012 dataset and design a new architecture to successfully classify these common attribute labels. Then after fusing features of attribute labels into original categories, our network can mine larger CAMs of objects. Our method achieves better CAM results in visual and higher evaluation scores compared with traditional methods.

关键词： image classification class activation map common attribute label

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Machine Vision Aided Adaptive Beamforming Decision for IRS-Assisted Wireless Networks

Machine Vision Aided Adaptive Beamforming Decision for IRS-A...

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European conference on Networks and communications (EuCNC)

作者： M. Munawar M. Guenach I. Moerman IMEC Leuven IDLab Ghent University

ISBN: (数字)9798350344998

ISBN: (纸本)9798350345001

This study leverages machine vision to assist communication in wireless networks, with a specific focus on intelligent reflecting surface (IRS)-assisted wireless networks. Instead of depending on traditional schemes such as alternating optimization or semidefinite relaxation to maximize signal strength in an IRS-assisted network, which are computationally expensive and impractical, we use visual data to make low-complexity beamforming decisions for users. Our approach involves employing a ceiling camera with a fish-eye view, covering a wide communication area. The user within the network is initially detected using the YOLOv2 object detection method. Subsequently, we propose closed-form analytical expressions to determine the distances between the access point (AP), user, and IRS. Accounting for the non-uniform nature of the fish-eye image, we introduce a novel method to determine non-uniform pixel weightages using trigonometric techniques. Based on the calculated distances, we make beamforming decisions depending on the user’s proximity to the AP or IRS. The proposed method significantly reduces computational complexity, making it nearly independent of the number of reflecting elements at the IRS. Simulation results indicate that the proposed approach exhibits extremely lower computational costs compared not only to conventional schemes such as alternating optimization and semidefinite relaxation-based convex solvers but also to low-complexity heuristic schemes.

关键词： Degradation visualization Array signal processing Wireless networks Machine vision Simulation Object detection

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Focal Modulation Based End-to-End Multi-Label Classification for Chest X-Ray image Classification

Focal Modulation Based End-to-End Multi-Label Classification...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Şaban Öztürk Tolga Çukur Elektrik ve Elektronik Mühendisligi Bölümü Bilkent Üniversitesi Ankara Türkiye Elektrik ve Elektronik Mühendisligi Bölümü Amasya Üniversitesi Amasya Türkiye Ulusal Manyetik Rezonans Araştırma Merkezi (UMRAM) Bilkent Üniversitesi Ankara Türkiye

Chest X-ray imaging is of critical importance in order to effectively diagnose chest diseases, which are increasing today due to various environmental and hereditary factors. Although chest X-ray is the most commonly used device for detecting pathological abnormalities, it can be quite challenging for specialists due to misleading locations and sizes of pathological abnormalities, visual similarities, and complex backgrounds. Traditional deep learning (DL) architectures fall short due to relatively small areas of pathological abnormalities and similarities between diseased and healthy areas. In addition, DL structures with standard classification approaches are not ideal for dealing with problems involving multiple diseases. In order to overcome the aforementioned problems, firstly, background-independent feature maps were created using a conventional convolutional neural network (CNN). Then, the relationships between objects in the feature maps are made suitable for multi-label classification tasks using the focal modulation network (FMA), an innovative attention module that is more effective than the self-attention approach. Experiments using a Chest x-ray dataset containing both single and multiple labels for a total of 14 different diseases show that the proposed approach can provide superior performance for multi-label datasets.

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Infrared image Noise Reduction and Edge Enhancement Algorithm Based on Local Intuitionistic Fuzzy Entropy

Infrared Image Noise Reduction and Edge Enhancement Algorith...

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Networking, communications and Information Technology (NetCIT), International conference on

作者： Yihan Niu Lanzhou Resources&Environment Voc-Tech University Lanzhou Gansu China

Aiming at the deficiency that the traditional P-M diffusion model can't distinguish the flat area and the detailed area with similar gradient changes, a new diffusion model is proposed. In image engineering, the actual image is flawed. On the one hand, due to the imperfection of image acquisition equipment, transmission equipment and receiving equipment, the image will be disturbed by various noises in the process of acquisition, transmission and storage, and the image will become blurred, resulting in incomplete image information. On the other hand, due to the limitation of various image processing algorithms, some negative effects will be brought. Firstly, the optimal threshold of infrared image is obtained by using the fuzzy maximum straightness criterion. On this basis, three spatial domain enhancement algorithms are proposed, including nonlinear stretching algorithm, improved plateau histogram equalization algorithm and double closed-value adaptive piecewise linear transformation algorithm for the target and background area. Experiments show that their processing effects have their own advantages. Then, with the help of wavelet theory, this paper proposes an infrared image contrast enhancement algorithm based on fuzzy logic and wavelet transform in the frequency domain. The algorithm can not only significantly improve the contrast of the image, but also have a better noise reduction effect. The experimental results show that, compared with the traditional model, the new model keeps the details more completely, removes the noise more cleanly, and has excellent visual and quantitative effects.

关键词： Wavelet transforms Fuzzy logic visualization Histograms Wavelet domain image processing image edge detection

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Copy-Move image Forgery Detection Using Discrete Wavelet Transform 3rd

Copy-Move Image Forgery Detection Using Discrete Wavelet Tra...

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3rd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2020

作者： Mahale, Vivek Yannawar, Pravin Gaikwad, Ashok Institute of Management Studies and Information Technology Aurangabad Maharashtra431001 India Vison and Intelligent System Lab Department of CS and IT Dr. Babasaheb Ambedkar Marathwada University Aurangabad Maharashtra India

ISBN: (纸本)9789811605062

To expressing visual information Digital images play a significant role. On social media this information in form of digital image is continually increasing. Now a day easily digital images are misrepresented by peoples because availability of software using for image processing. In this paper we represent novel approaches which identify Copy-Move forgery in image using DWT. First we read image and convert this image into grayscale image. Then apply wavelet transform. Extract overlapped high contrast blocks of the coarsest level. Then lexicographically sorting and calculate phase correlation between rows. Extract blocks from reference and match region. Then draw the duplicate region. For evaluation of our system we calculate TPR, FPR, Accuracy and AUC. Our system accuracy 94.58%. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Discrete wavelet transforms

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Security of images Using DNA Encryption and Lorenz System in Cloud Storage 7th

Security of Images Using DNA Encryption and Lorenz System in...

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7th International conference on Soft Computing and Signal processing, ICSCSP 2024

作者： Gupta, Raghav Gullapalli, Manasa Suseela, G. Department of Networking and Communications School of Computing Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Networking and Communications Faculty of Engineering and Technology School of Computing Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9789819799251

images are the most valuable yet susceptible type of data since they may be used to track user activity, exploited by hackers, and utilized for fraud and identity theft. Storing images in local storage, on a device’s disk, within a web browser’s local storage, or on a server’s file system can introduce several vulnerabilities and risks, particularly regarding security and data integrity. Using image encryption as a security solution provides a reliable means of protecting sensitive visual data from unauthorized access and potential security threats. The convenience of cloud-based systems, however, comes with the responsibility of protecting data against cyber threats. The widespread usage of cloud storage has led to concerns about the security of images stored there. Addressing the challenges associated with securing images in the cloud, this project brings a novel approach that blends cryptographic techniques, such as DNA encryption, with the dynamism of chaotic systems like the Lorenz system. Such integrative methodologies aim to meet current security demands for image storage and transmission in the digital age. This dual-layer encryption approach ensures that images encrypted and stored in cloud storage are safeguarded with a security measure that is currently unparalleled, making unauthorized access and decryption practically impossible without the specific, uniquely generated decryption key. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensitive data

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Fully Neural Network Mode Based Intra Prediction of Variable Block Size

Fully Neural Network Mode Based Intra Prediction of Variable...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Sun, Heming Yu, Lu Katto, Jiro Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama Japan Zhejiang Univ Inst Informat & Commun Engn Hangzhou Peoples R China Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan

ISBN: (纸本)9781728180687

Intra prediction is an essential component in the image coding. This paper gives an intra prediction framework completely based on neural network modes (NM). Each NM can be regarded as a regression from the neighboring reference blocks to the current coding block. (1) For variable block size, we utilize different network structures. For small blocks 4x4 and 8x8, fully connected networks are used, while for large blocks 16x16 and 32x32, convolutional neural networks are exploited. (2) For each prediction mode, we develop a specific pre-trained network to boost the regression accuracy. When integrating into HEVC test model, we can save 3.55%, 3.03% and 3.27% BD-rate for Y, U, V components compared with the anchor. As far as we know, this is the first work to explore a fully NM based framework for intra prediction, and we reach a better coding gain with a lower complexity compared with the previous work.

关键词： Intra prediction image compression deep learning fully connected layer convolutional neural network

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