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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VII"

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image Encryption Algorithm Based on Combination Chaotic System and DNA Coding 4

Image Encryption Algorithm Based on Combination Chaotic Syst...

引用

4th International conference on Natural Language processing, ICNLP 2022

作者： Mao, Panpan Zhang, Xuefeng Jiang, Wen Xi'an University of Posts and Telecommunications School of Cyberspace Security Xi'an710121 China National Internet Emergency Center Beijing100029 China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

In order to increase the chaotic parameter range, improve the initial sensitivity of the chaotic system and the security of the image encryption algorithm, This paper proposed a new image encryption based on combination chaotic system and DNA coding. Two one-dimensional chaotic maps are nested to obtain a combined chaotic system, and the chaotic sequences generated by the combined chaotic system is amplified and modulated to obtain the DNA coding rule sequences and the scrambling sequences, and the uniformity of the coding rules are improved through rule replacement operation. Using DNA encoding, DNA exclusive-or(XOR),and DNA decoding operations to achieve plain image diffusion, bit-level permutation is performed on the diffused image through the permutation sequences to obtain encrypted image. Theoretical and experimental simulation analysis shows that the chaotic interval of the combined chaotic system is increased and the initial value sensitivity is improved. The encryption algorithm based on the combined chaotic system and DNA coding can effectively resist statistical attacks, differential attacks and exhaustive attacks, the security of the encryption algorithm is also significantly improved compared to other algorithms. © 2022 IEEE.

关键词： Chaotic systems

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Edge Computing Based Miniature Maps Using Embedded Webserver For Prediction of Malignancy 6

Edge Computing Based Miniature Maps Using Embedded Webserver...

引用

6th International conference on Devices, Circuits and systems, ICDCS 2022

作者： Johny, Anil Madhusoodanan, K.N. Cyriac, Sanju Cochin University of Science and Technology Department of Instrumentation Cochin682022 India Cusat Department of Instrumentation Cochin682022 India Rajagiri Hospital Department of Medical Oncology Kerala 683112 India

ISBN: (纸本)9781665480949

Cancer detection from histopathology images is based on extraction of spatial information from whole slide images(WSI) using image processing tools. Machine learning algorithms and state-of-the art convolution neural networks based prediction of malignancy requires cloud services which are often prone to network latency. We put forward a novel technique to classify histopathology images by creating a miniature probability maps to locate the presence or absence of cancer which can be used in any portable device connected to the local network. The shortcomings of loading the high resolution images in mobile devices is resolved as the extraction of patches from WSI and prediction is performed concurrently to minimize computational overhead. The miniature map indicating the presence or absence of cancer in pathology images can be viewed in mobile screen for the uploaded WSI from same device. A custom trained model is used to perform to reduce the processing time thereby faster predictions in end-devices deployed near the medical practitioner as assisting tool in disease diagnosis. © 2022 IEEE.

关键词： Performance evaluation image resolution Histopathology Computational modeling Neural networks Memory management Predictive models

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Controlling a Swarm of Unmanned Aerial Vehicles Using Full-Body k-Nearest Neighbor Based Action Classifier

Controlling a Swarm of Unmanned Aerial Vehicles Using Full-B...

引用

International conference on Unmanned Aircraft systems (ICUAS)

作者： Chaudhary, Akash Nascimento, Tiago Saska, Martin Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 Czech Republic Univ Fed Paraiba Joao Pessoa Paraiba Brazil

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405935

The intuitive control of robot swarms becomes crucial when humans are working in close proximity with the swarm in unknown environments. In such operations, it is necessary to maintain the autonomy of the swarm while giving the human operator enough means to influence the decision-making process of the robots. This paper presents a human-swarm interaction approach using full-body action recognition to control an autonomous flock of unmanned aerial vehicles. We estimate the full-body pose of the human operator and use a k-nearest neighbor algorithm to classify the action made by the humans. Finally, the swarm uses the identified action to decide its goal direction. We demonstrate the practicality of our approach with a multi-stage experimental setup to evaluate the prediction accuracy and robustness of the system.

关键词： image processing Decision making Lighting Autonomous aerial vehicles Prediction algorithms Robustness Real-time systems

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Real-Time Dynamic image Stitching Using GNN-Based Feature Matching

Real-Time Dynamic Image Stitching Using GNN-Based Feature Ma...

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Mechatronics and Robotics Engineering (ICMRE), International conference on

作者： Young Hoon Suh Eun Ho Kim Yeong Gwang Choi JaeWook Jeon Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Especially in static situations, image stitching has been routinely used to seamlessly merge many photographs into a panorama. Using it in dynamic and real-time environments, however, presents a number of difficulties, such as managing shifting objects, changing surroundings, and processing limitations. An image stitching look at that works well in dynamic real-time situations is presented in this paper. The method makes use of GNN-based feature matching, dynamic object removal, and real-time synthesis techniques. Comparative studies suggest that, in comparison to traditional methods like SIFT and ORB, the suggested approach meets real-time processing requirements while achieving greater registration precision, lower homography RMSE, and consistent panoramic outputs. The findings imply that the algorithm might be used in fields where precision and efficiency are required in dynamic circumstances, like parking management systems, sports broadcasting, and autonomous driving.

关键词： Accuracy Smoothing methods Heuristic algorithms Surveillance Real-time systems Robustness image stitching Robots Optimization Sports

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An Adaptive image Windowing Method for Real-Time Object Detection on Board

An Adaptive Image Windowing Method for Real-Time Object Dete...

引用

International conference on Electronics Technology (ICET)

作者： Long Ling Zhijun Lu Manli Shi Jie Wang Yuqing Li Minghe Hu China Academy of Space Technology Beijing Institute of Space Mechanics & Electricity Beijing China

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

In order to meet the real-time detection and processing requirements of on-board targets in the field of remote sensing image processing, this paper carries out relevant research from the perspective of software optimization and proposes an onboard adaptive image windowing processing method based on differential evolution algorithm to solve the problem of shortage of on-board hardware computing resources. Based on the adaptive differential evolution algorithm, combined with the engineering application scenario, the algorithm innovatively designs an individual objective function to evaluate the “advantages and disadvantages“ of the algorithm evolution results. By coordinating all the target positions to be tracked in the image plane, the algorithm optimizes the number of windows, and considers the application boundary of the algorithm, which can effectively reduce the amount of data to be processed and greatly improve the timeliness of real-time detection on the board without losing the calculation accuracy.

关键词： Target tracking image processing Software algorithms Object detection Linear programming Real-time systems Hardware

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Multimodal Sign Language and Big Data Integration Research

Multimodal Sign Language and Big Data Integration Research

引用

Communication, image and Signal processing (CCISP), International conference on

作者： Fengqian Song Huiyu Yang Yangyang Zhang Internet Academy Anhui University Hefei China Daguan Data Co. Ltd. Shanghai China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

Sign language is the primary means of communication for the deaf and mute community. The regional and national differences in sign language have led to barriers between different sign language systems, making communication between deaf and mute individuals from different regions challenging. This paper proposes a vision-tactile fusion approach to recognize sign language. The scheme applies a flexible sensor data glove to monitor the angle changes of finger joints, as well as visual image processing technology, to integrate tactile and visual data for sign language recognition. The integration is performed through three methods: weighted average, weighted multiplication, and attention weights, with accuracy rates of 95.55%, 97.87%, and 98.83%, respectively.

关键词： Sign language Visualization image recognition Accuracy Sociology Signal processing algorithms Signal processing Feature extraction Pattern recognition Sensors

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5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

5th International Conference on Advances in Distributed Comp...

引用

5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

ISBN: (纸本)9789819718405

The proceedings contain 35 papers. The special focus in this conference is on Advances in Distributed Computing and Machine Learning. The topics include: Computer-Aided Bundle Branch Block Detection Using Symbolic Features of ECG Signal;block-Chain and Cloud-Based Tender Allocation System;reliability Assessment of IoT-Enabled systems Using Fault Trees and Bayesian Networks;resnet-50 Integrated with Attention Mechanism for Remote Sensing Classification;revolutionizing Data Annotation with Convergence of Deep Learning and Active Learning to Enhance Credibility on Twitter Datasets;statistical and Deep-Learning Approaches for Individual Carbon Footprint Calculation in India;intelligent Healthcare System Using Emerging Technologies: A Comprehensive Survey;Efficient Energy Management by Using SJF Scheduling in Wireless Sensor Network;cost Effective and Energy Efficient Drip Irrigation System for IoT Enabled Smart Agriculture;facial Detection and Recognition in Drone imagery Using FaceNet;valluvan: processing Name Board images to Enhance Communication for Native Tamil Speakers;Optimized VM Migration for Energy and Cost Reduction Using TSO Algorithm in Cloud Computing;A Study on the Mental Health Among Indian Population in the Post COVID-19 Pandemic Using Computational Intelligence;Cloud-Based Anomaly Detection for Broken Rail Track Using LSTM Autoencoders and Cross-modal Audio Analysis;Face Recognition Using CNN for Monitoring and Surveillance of Neurological Disorder Patients;a Novel Approach to Breast Cancer Histopathological image Classification Using Cross-colour Space Feature Fusion and Quantum–Classical Stack Ensemble Method;comparative Analysis of Deep Learning-Based Hybrid algorithms for Liver Disease Prediction;preface;performance Improvements of Covert Timing Channel Detection in the Era of Artificial Intelligence;sign Detection Using an N-Gram Language Model and MobileNet.

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Standardising Wound image Acquisition Through Edge AI 1

引用

9th International conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2023

作者： Vasconcelos, Maria João M. Sampaio, Ana Filipa Cardoso, Nuno Liberal, Marcos Alves, Paulo Marques, Raquel Salgado, Pedro Fraunhofer Portugal AICOS Porto Portugal Porto Portugal Centre for Interdisciplinary Research in Health Instituto de Ciências da Saúde UCP Porto Portugal F3M Information Systems S.A. Braga Portugal

ISBN: (数字)9783031627538

ISBN: (纸本)9783031627521

The high prevalence of chronic wounds, along with their impact on the patient’s life quality and healthcare systems, make them a relevant public health issue and have motivated the rise of digital health solutions to support wound monitoring. This paper presents a new framework that leverages deep learning models to automate wound image acquisition in real-time while guaranteeing focus and inclusion of an adequate periwound area. Considering an adhesive marker as a metric reference, a RetinaNet detection model is responsible for locating the wound and marker regions, further analysed by a post-processing module that validates if both structures are present and verifies if a periwound radius between 4 to 8 centimetres is included. The initial validation of this pipeline demonstrated that the developed algorithms exhibit a robust detection performance for varying acquisition conditions (translated into mAP@.75IOU values of 0.39 and 0.95 for wound and marker detection) and the deployability of the framework in an easily usable mobile application, without causing any performance hindrances. The proposed solution was then tested in a real environment by integrating the whole framework into a mobile application available in Android and iOS. During a two-month pilot study, healthcare professionals tested this application in their clinical practice. According to their feedback, the usage of the mobile application improved image quality and standardisation as the main advantages of the application, confirming the potential of the presented framework to streamline the image acquisition flow and make wound monitoring more reproducible. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Object detection

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image processing for automatic road inventory 15

Image processing for automatic road inventory

引用

15th International Scientific and Technical conference: Applied Mechanics and systems Dynamics, AMSD 2021

作者： Belim, S.V. Khiryanov, E.V. Kvashnina, P.A. Ostrinskaya, L.I. Omsk State Technical University 11 Mira ave Omsk644050 Russia Siberian State Automobile and Highway University Omsk Russia

Road image analysis is an important task for automatic road inventory. The determination geometric dimensions for the road and the identification road objects are subprocess of constructing a road digital image. In this article, two algorithms for solving different subtasks of automatic road image inventory are proposed. The first algorithm identifies road signs. A convolutional artificial neural network is used in this algorithm. The training set for the neural network is prepared. A computer experiment to determine the recognition effectiveness of road signs has been conducted. The second algorithm defines the edges of the pavement. The algorithm consists five stages. The edges of the road are modeled as straight lines. The result allows you to automatically determine the width of the road. © Published under licence by IOP Publishing Ltd.

关键词： Neural networks

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Identifying Adversarial Attacks in Crowdsourcing via Dense Subgraph Detection

Identifying Adversarial Attacks in Crowdsourcing via Dense S...

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

作者： Abdullah Karaaslanli Panagiotis A. Traganitis Aritra Konar Dept. of Electrical & Computer Engineering Michigan State University East Lansing MI USA Dept. of Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Crowdsourcing is becoming increasingly important for contemporary applications in machine learning and artificial intelligence. However, crowdsourcing systems may be susceptible to adversarial attacks where a subset of annotators deliberately provide erroneous responses. This paper introduces a novel algorithm for identifying adversarial attacks in crowdsourcing systems by recasting the problem as dense subgraph detection in bipartite graphs. In particular, we represent crowdsourced data as a weighted bipartite graph between workers and data points which are connected with edges whose weights are computed from annotators’ responses. The constructed bipartite graph is then analyzed with a sequential peeling algorithm to detect the dense subgraph that includes adversarial attacks. Compared to previous methods where only adversaries are detected, our proposed method can simultaneously identify adversarial annotators as well as affected data points. Preliminary results on real datasets showcase the potential of this novel approach.

关键词： Crowdsourcing Machine learning algorithms image edge detection Signal processing algorithms Machine learning Signal processing Acoustics Bipartite graph Speech processing

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