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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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Cuckoo Search Optimized Histogram Equalization for Low Contrast image Enhancement

Cuckoo Search Optimized Histogram Equalization for Low Contr...

引用

International conference on parallel distributed and Grid Computing (PDGC)

作者： Niveditta Thakur Nafis Uddin Khan Sunil Datt Sharma Jaypee University of Information Technology Solan India

ISBN: (纸本)9781665454025

image enhancement is a process to improve the visual standard of image so as to extract spatial features of image. Histogram Equalization is method by which image can be improved for better perception and interpretation by different automated image processing system and human beings. Although, for low-contrast images (with lower dynamic range) traditional histogram equalization deteriorate quality of the output image by introducing washed-out form caused by too much brightness change. This paper presents histogram equalization based image enhancement method which is optimized using cuckoo search algorithm. Foremost purpose of the proposed method is to control over and under enhancement by calculating clip limits of the image histogram before performing histogram equalization. This method uses cuckoo search algorithm to automatically control the clip limit according to the image nature using improved fitness function. Effectiveness of the proposed method in comparison to other methods is analyzed using well known image quality metrics.

关键词： image quality Histograms Visualization Heuristic algorithms Feature extraction Noise measurement Indexes

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PB-UAP: Hybride Universal Adversarial Attack for image Segmentation

PB-UAP: Hybride Universal Adversarial Attack for Image Segme...

引用

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

作者： Yufei Song Ziqi Zhou Minghui Li Xianlong Wang Hangtao Zhang Menghao Deng Wei Wan Shengshan Hu Leo Yu Zhang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, i.e., adversarial attacks on deep neural networks. Existing works primarily focus on image classification tasks, aiming to alter the model’s predicted labels. Due to the output complexity and deeper network architectures, research on adversarial examples for segmentation models is still limited, particularly for universal adversarial perturbations. In this paper, we propose a novel universal adversarial attack method designed for segmentation models, which includes dual feature separation and low-frequency scattering modules. The two modules guide the training of adversarial examples in the pixel and frequency space, respectively. Experiments demonstrate that our method achieves high attack success rates surpassing the state-of-the-art methods, and exhibits strong transferability across different models.

关键词： Training image segmentation Perturbation methods Semantics Scattering Signal processing Predictive models Network architecture Robustness Speech processing

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Lightweight Edge Detection Model with parallel Pixel Difference

Lightweight Edge Detection Model with Parallel Pixel Differe...

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Wenhao Zheng Ling Yao Bo Liu Mingwu Ren Zhiyuan Ma School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Beijing Shoufa Highway Maintenance And Construction Co Ltd Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Although existing learning-based edge detection methods have made significant progress, they typically require large network parameters and substantial computational resources, which limits their practical applicability. Instead of focusing on larger deep models to improve detection performance, we propose a lightweight deep edge detection method that reduces computational costs while achieving comparable detection performance. This approach is built on a residual framework network architecture. Specifically, we introduce an efficient parallel Pixel Difference Convolution Block (PPDB) as the core building block of our method to reduce model complexity. Additionally, to better leverage features from different scales, we incorporate a lightweight dynamic upsampling technique and develop a Refined Feature Fusion Block (RFFB) to achieve high-quality results. Experimental results on benchmark datasets show that our method requires about 200K fewer parameters and FLOPs compared to state-of-the-art methods such as LDC, while maintaining competitive performance.

关键词： Deep learning Computer vision Convolution image edge detection Computational modeling Focusing Network architecture Feature extraction Computational efficiency Complexity theory

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Tamper-forgery image Detection in Security Digital image: A Review

Tamper-forgery Image Detection in Security Digital Image: A ...

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Blockchain and distributed Systems Security (ICBDS), IEEE International conference on

作者： Nilesh N. Thorat Prashant Dhotre Nilesh Kulal Aarti P Pimpalkar Vijaya S. Patil Sachin M. Kolekar Computer Science & Engineering Department MIT School of Computing MIT Art Design and Technology University Pune Information Technology Department MIT School of Computing MIT Art Design and Technology University Pune

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Concerns with image security exist in every sector that uses digital photographs. Historically, public safety and forensics have depended on images from the crime scene, biometric photos, suspect photos, and other sources. With the development of digital imaging, there has been a significant increase in the use of digital images in this field. Although the use of digital image processing has led to the creation of several new forensic methods of inquiry, it has also simplified picture modification. Digital picture validity is an issue since several snipping and image alteration programs are freely available to the public. It serves as solid proof in many different types of crimes and is utilized as documentation for many purposes. Creating and modifying photos has become easier and more accessible with the development of image manipulation and editing tools. The two most common methods of photo counterfeiting are copy-and-move forgery and image splicing. An image can be spliced, or combined from two photos, to hide or show an error scenario. A section of a picture can be copied and pasted into another image. The many kinds of digital image frauds are examined in this study along with software that detects them. An analysis was done on the methods currently in use for spotting phony photos.

关键词： Reviews Digital images Forensics Splicing Training data Solids Forgery Software Fraud Security

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Enriching image Security in Healthcare Based on Cryptography and Deep Learning Techniques

Enriching Image Security in Healthcare Based on Cryptography...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Rishav Gusain Ashwani Kumar Avinash Kumar Sharma Shitharth Selvarajan Department of Computer Science & Engineering School of Engineering & Technology Sharda University India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering School of Engineering & Technology Sharda University Greater Noida India School of Built Environment Engineering and Computing Leeds Beckett University U.K

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Securing electronic health records in the internet of medical things is a key interest in health-care due to the sector's different surroundings. As technology evolves, preserving the privacy, reliability, and accessibility of healthcare data becomes extremely challenging. Cryptographic techniques provide a possible solution for safeguarding confidential information about medical pictures while it is being transferred and stored. On the other hand, deep leaning has the potential to totally change cryptography by providing strong encryption, quality improvements, and detection potential for healthcare picture security. To increase the privacy of healthcare image information, this research analysed the fusion of deep leaning and cryptography methods. It gives a study of the current situation of deep learning-based image detection of anomalies approaches in working contexts, such as network typologies, supervision levels, along with evaluation norms. This study provides direction to future research techniques to overcome these problems, also the possibility and challenges of medical picture cryptography and picture detection of abnormality. This work bridges the gap between deep learning and encryption, paving the way for better privacy, integrity, and availability of key image data.

关键词： Deep learning image segmentation Data privacy Privacy Neural networks Internet of Medical Things Encryption Reliability Electronic medical records Biomedical imaging

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SHALLOW MULTI-SCALE NETWORK FOR STYLIZED SUPER-RESOLUTION

SHALLOW MULTI-SCALE NETWORK FOR STYLIZED SUPER-RESOLUTION

引用

IEEE International conference on image processing (ICIP)

作者： Durand, Thibault Rabin, Julien Tschumperle, David Normandie Univ GREYC CNRS ENSICAENUNICAEN F-14000 Caen France

ISBN: (纸本)9781665441155

image Super Resolution (SR) has come a long way since the early age of image processing. Deep learning methods nowadays give outstanding results, yet very few are actually used in digital illustration and photo retouching software due to large memory storage and GPU computational requirements, but also due to the actual lack of control provided to the user over the final result. This paper introduces a two-step framework for stylized SR using a multi-scale network built with independent parallel branches. The approach aims at: i. designing a shallow network based on image processing techniques making it usable on light hardware architecture (low memory cost, no GPU);ii. providing a versatile, controllable and customizable network to stylize SR results in a plug-and-play manner. We show that the proposed method offers significant advantages over state-of-the-art reference-based approaches regarding these aspects.

关键词： image Super Resolution Style Transfer Shallow Neural Network Texture Synthesis Interactive Computation

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Guided Spatio-Temporal Learning Method for 4K Video Super-Resolution 5

Guided Spatio-Temporal Learning Method for 4K Video Super-Re...

引用

5th ACM International conference on Multimedia in Asia, MMAsia 2023

作者： Jiang, Qin Wang, Qinglin Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology China

ISBN: (纸本)9798400702051

4K Video Super-Resolution (VSR) presents a challenging task in video processing, as most existing VSR models have high computational complexity, limiting their application to high-resolution videos, particularly for 4K resolution videos. To address this issue, we propose a novel Guided Spatio-Temporal Video Super-Resolution network (GST-VSR) designed to perform 4K VSR on a single GPU. The proposed method comprises two key components: the Spatio-Temporal Alignment Network (STAN) and the Super-resolution Reconstruction Network (SRN), which work together to enhance the quality of the output frames. The STAN is responsible for extracting highly relevant features in frames and aligning the reference frame with the neighboring frames at the feature level to maintain temporal consistency. The SRN fuses high-quality features into the final high-resolution frames. Unlike existing methods, our proposed approach does not require explicit optical flow estimation, making it more efficient and less computationally demanding. To facilitate the training and testing of the compared models, we have established a new dataset, Pixabay-Set, consisting of 145 videos suitable for the 4K VSR task. Experimental results on the test dataset show that the proposed method achieves competitive performance compared to state-of-the-art models. In summary, our proposed GST-VSR network provides an effective solution to the challenging task of 4K VSR. © 2023 Copyright held by the owner/author(s).

关键词： Statistical tests

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Sparse Matrix Reordering Method Selection with parallel Computing and Deep Learning

Sparse Matrix Reordering Method Selection with Parallel Comp...

引用

International Joint conference on Neural Networks (IJCNN)

作者： Rui Xia Jihu Guo Huajian Zhang Shun Yang Qinglin Wang Jie Liu College of Computer Science and Techonology National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Sparse matrix reordering is an important step in Cholesky decomposition. By reordering the rows and columns of the matrix, the time of computation and storage cost can be greatly reduced. With the proposal of various reordering algorithms, the selection of suitable reordering methods for various matrices has become an important research topic. In this paper, we propose a method to predict the optimal reordering method by visualizing sparse matrices in chunks in a parallel manner and feeding them into a deep convolutional neural network. The results show that the theoretical performance can reach 95% of the optimal performance, the prediction accuracy of the method can reach up to 85%, the parallel framework achieves an average speedup ratio of 11.35 times over the serial framework, and the performance is greatly improved compared with the traversal selection method on large sparse matrices.

关键词： Deep learning Visualization Accuracy Costs Neural networks parallel processing Prediction algorithms

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On the Evaluation of Two methods Applied to the Morphometry of Linear Dunes 21st

On the Evaluation of Two Methods Applied to the Morphometry ...

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21st International conference on image Analysis and processing (ICIAP)

作者： Schein, Tatiana Tais Emmendorfer, Leonardo R. NobreMendes, Fabiano Rodriguez, Barbara D. A. Almeida, Luis Pedro de Oliveira, Vinicius Menezes Univ Fed Rio Grande Programa Posgrad Modelagem Computac Rio Grande City Brazil Univ Fed Rio Grande Ctr Ciencias Computac BR-96203900 Rio Grande City Brazil Univ Fed Rio Grande Inst Matemat Estat & Fis BR-96203900 Rio Grande City Brazil CoLAB ATLANTIC Edificio LACSEstr Malveira Serra 920 P-2750834 Cascais Portugal

ISBN: (纸本)9783031064333;9783031064326

The evolution of dunes is a relevant topic in many areas, including the study of the marine environment. In particular, the widespread linear dunes are characterized by roughly parallel ridges, which extend for long distances. The identification of the main morphometric characteristics of linear dunes is a relevant topic, with wide applicability. This work evaluates two approaches for linear dune morphometry. The methods are built over the Radon transform, the Discrete Fourier Transform, and autocorrelation. The evaluation is performed over a set of publicly available dune images. Results show that the performance of the methods varies over the morphometric characteristics considered.

关键词： Dune morphology Morphometry Discrete Fourier Transform Radon transform Autocorrelation

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An Improved Ant Colony Algorithm Accelerated by GPU in Track Correlation

An Improved Ant Colony Algorithm Accelerated by GPU in Track...

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

作者： Xu Chen Xianzhao Shen Qingchen Zhou Mingming Xu East China Research Institute of Electronic Engineering Hefei City Anhui China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

In the distributed information fusion system, the problem of track correlation can be transformed into the problem of multi-dimension assignment. However, it's a NP-Hard problem. In view of the low convergence-rate and the more iterations for the existing methods, a kind of improved ant colony parallel solution algorithm with the advantages of small-scale searches, fast convergence speed, less iteration has been put forward. Firstly, the multi-dimension assignment model of track correlation should be established based on grey theory. Secondly, the search area is narrowed down to get higher searching efficiency through updating the search listings based on the neighbourhood of the maximum grey relational coefficient, and the pheromones are updated using the wolves assignment principle for reference in order to avoid trapping in local optimum. At last, use the parallel strategy of GPU acceleration to realize the improved algorithm. The simulation results show that the improved algorithm can obtain the correct association rate of more than 90% by ten iterations or less in one association period; In addition, the parallel solving strategy based on GPU acceleration can improve the solving efficiency, and the acceleration result is more obvious with the number of targets increasing.

关键词： Correlation coefficient Correlation Target tracking NP-hard problem Simulation Signal processing algorithms Graphics processing units Signal processing Search problems Convergence

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