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

共 15618 条记录，以下是721-730 订阅

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Blood Vessel Detection in Fundus images Using Symbolic Approach 14

Blood Vessel Detection in Fundus Images Using Symbolic Appro...

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14th International conference on Intelligent systems: Theories and Applications, SITA 2023

作者： Lamti, Sanae Sekhsoukh, Rachid Mabrouki, Fatima Zahra Kerzazi, Noureddine Harrar, Youssef Oujidi, Wissale University Mohammed v Ensias School of Engineering Rabat Morocco Mohammed Vi University Hospital Center Morocco

ISBN: (纸本)9798350308211

The diagnosis of retinal diseases using the vasculature of Fundus images has long been a focus of both ophthalmologists and medical research. Using computer-aided techniques to provide segmentation of blood vessels helps ophthalmologists in their clinical application routine. However, segmenting the vessels from retinal images is still a challenging task because of the variability in retinal vascular thickness, inhomogeneity of image intensity, and noise textures(both pathologies or bad pixels) that might hinder the quality of images. This work introduces a new technique of image processing based on Hessian filter to extract the blood vessel structure with a three-stage approach to filter Thicker, Thinner, and Neo Vessels. The proposed method is evaluated on three publicly available datasets namely Drive, Stare, and ChaseDB respectively achieving an average accuracy of.93%,.94%,.95 % and then applied on our proper dataset of 350 images classified into 7 classes according to specific challenges. Manual validation by a team of four ophthalmologists provides useful insights into each class of images. Finally, we aim at providing an annotated database of fundus images which will serve as a training dataset for Deep Learning techniques. © 2023 IEEE.

关键词： Blood vessels

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An image Region Hierarchical Encryption Approach for Edge Intelligence Perception 24

An Image Region Hierarchical Encryption Approach for Edge In...

引用

6th International conference on Telecommunications and Communication Engineering

作者： Hou, Chengyu Li, Wenzao Xiao, Linsong Yao, Sai Chengdu Univ Informat Technol Chengdu Peoples R China

ISBN: (纸本)9798400709630

In the context. of smart cities, edge-aware machine are widely used. These systems involve scenarios where large volumes of image data are stored locally. They also involve scenarios where image data is uploaded to edge clouds, posing significant privacy risks. 'therefore, it is necessary to encrypt images containing sensitive information. However, edge computational devices typically have limited computational ability. To address the need t'or privacy protection, this paper proposes a partial image encryption algorithm based on object detection. First, our approach uses an object detection model to identify private areas in images (such as license plates) and applies a specific encryption strategy to license plate areas. At the same time, the computational burden on edge devices is reduced. Additionally, we introduce a chaotic mapping algorithm based on image segmentation and compare its performance with traditional chaotic mapping algorithms. Experimental results show that the improved algorithm performs better in encrypting sensitive areas while also exhibiting superior performance in gray value histogram analysis and scatter plot analysis.

关键词： machine vision encryption and decryption, image segmentation chaotic mapping

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Attention based image Caption Generation (ABICG) using Encoder-Decoder Architecture 5

Attention based Image Caption Generation (ABICG) using Encod...

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5th International conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Kulkarni, Uday Tomar, Kushagra Kalmat, Mayuri Bandi, Rakshita Jadhav, Pranav Meena, S.M. Kle Technological University Dept. of Cse Hubballi India

ISBN: (纸本)9781665474672

The image captioning is utilized to develop the explanations of the sentences describing the series of scenes captured in the image or picture forms. The practice of using image captioning is vast although it is a tedious task for the machine to learn what a human is capable of. The model must be built in a way such that when it reads the scene, it recognizes and reproduce to-the-point captions or descriptions. The generated descriptions must be semantically and syntactically accurate. Hence, availability of Artificial Intelligence (AI) and Machine Learning algorithms viz. Natural Language processing (NLP), Deep Learning (DL) etc. makes the task easier. Although majority of the existing machine-generated captions are valid, they do not focus on the crucial parts of the images, which results in lesser clarity of the captions. In the proposed paper, anew introduction to attention mechanism called Bahdanau's along with EncoderDecoder architecture is being used so as to reflect the image captions that are more accurate and detailed. It uses a pretrained Convolutional Neural Network (CNN) called InceptionV3 architecture to gather the features of images and then a Recurrent Neural Network (RNN) called Gated Recurrent Unit (GRU) architecture in order to develop captions. This model is trained on Flickr8k dataset and the captions generated are 10% more accurate than the present state of art. © 2023 IEEE.

关键词： Recurrent neural networks

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image Impulse Noise Removal Using a Hybrid System Based on Self Organizing Map Neural Networks And Median Filters 11

Image Impulse Noise Removal Using a Hybrid System Based on S...

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11th IEEE International conference on systems and Control, ICSC 2023

作者： Mohammed Hamid, Mabroukah Fathi Hammad, Fatimah Hmad, Nadia College of Computer Technology Department of Software Engineering and Development Tripoli Libya College of Sciences and Natural Resources Aljafara University Aljafara Libya

ISBN: (纸本)9798350304886

Digital image restoration has become important for many image applications. Therefore, image Noise removal is an essential issue in an image processing fields. In this paper, we presented a hybrid system, based on Self Organizing Maps neural networks (SOM NN) and Median filter (MF), to eliminate Random Impulsive Noises (RIN) from grayscaled digital images. In our system we applied two main processes (features extraction process, detection process): in the features extraction process, feature vectors of two or three features, (central pixel value, standard deviation value) or (pixel value, standard deviation, Mean Difference value) respectively, were extracted. In the detection process, the self-organizing maps (SOM NN) are used as an impulse noise detector. This SOM NN module is trained using competitive learning algorithms. Then the detected corrupted pixel is modified using Median filter (MF) algorithm;otherwise, it is left unchanged. Finally, the results of this study are compared with the previous traditional and state-of-the-art methods results that applied on the same database. Our results are significantly outperforms other traditional methods and were comparable with the state-of-the-art methods results in terms of reconstruction quality and that are comparable to the FL using the MSE and PSNR measurements. © 2023 IEEE.

关键词： Impulse noise

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A Novel visual Communication Scenario Based on image processing algorithms

A Novel Visual Communication Scenario Based on Image Process...

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Jinhua Yang Li Hou Xijing University Shaanxi Xian China

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

In order to comply with the trend of intelligent visual communication, this study proposed an innovative visual communication scenario based on image processing algorithms. The framework aims to optimize traditional key technologies such as the image generation, editing, style transfer and image compression. First, as the foundation of visual communication, this study proposes a generative adversarial network model based on text semantic information for image generation and editing. The model achieves stable image generation and efficient editing from a theoretical level through paired training of text and image pairs. Secondly, for image style transfer, this study designed an improved VGG19 convolutional neural network. At the same time, the adaptive instance normalization technology was combined to optimize the effect of style transfer. Finally, in terms of image compression, the study proposed an improved generative adversarial network (REviSED-GAN) model. This model can dynamically adjust the compression error based on structured information to improve image compression efficiency. Through comparative tests, the proposed image style transfer and image compression algorithms have shown excellent performance in terms of structural similarity, image quality and compression ratio.

关键词： Training Adaptation models image coding Machine learning algorithms visual communication image synthesis Heuristic algorithms Semantics Generative adversarial networks Neural style transfer

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Graph Neural Network Performance Analysis in the Cell-Free Massive MIMO Wireless systems 21

Graph Neural Network Performance Analysis in the Cell-Free M...

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21st International Joint conference on Computer Science and Software Engineering (JCSSE)

作者： Rahman, Md Habibur Sejan, Mohammad Abrar Shakil Aziz, Md Abdul Tabassum, Rana Song, Hyoung-Kyu Sejong Univ Dept Informat & Commun Engn Seoul South Korea Sejong Univ Dept Convergence Engn Intelligent Drone Seoul South Korea

ISBN: (纸本)9798350381771;9798350381764

To address challenging issues in wireless communications, researchers have developed deep learning (DL)-based techniques, yielding encouraging outcomes. However, commonly adopted neural network (NN) architectures, such as convolutional (CNNs) and multi-layer perceptrons, are derived from DL for image processing applications and are not specifically tailored to tackle wireless network challenges. Consequently, they often exhibit poor generalization and scalability issues in large-scale networks and unknown network environments. Graph neural networks (GNNs) have gained recent popularity as a solution to these challenges due to their ability to effectively utilize domain knowledge and graph topology in wireless communications. GNN-based techniques demonstrate nearly optimal performance in massive networks and show good generalization across various system configurations. To leverage GNNs in wireless systems, we have designed a cell-free massive MIMO (CF-mMIMO) system, presented as a GNN structure. The proposed CF-mMIMO system is employed to analyze the effectiveness of different GNN models and various optimization algorithms. The system exhibits effective performance against optimal benchmarks and provides valuable insights for the next-generation research community.

关键词： cell-free massive MIMO graph neural networks optimization algorithms

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Eye Movement analysis And Prediction Using image processing

Eye Movement analysis And Prediction Using Image Processing

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2024 International conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Kalaiselvi, V.K.G. Doss, D. Information Technology Sri Sairam Engineering College Chennai India

ISBN: (纸本)9798350352689

The predominant function of most facial analysis systems revolves around facial alignment and eye tracking, crucial for locating key facial landmarks in images or videos. While developers have access to various models and techniques within popular computer vision libraries, the real-time demands and challenges posed by extreme head positions, occlusions, and inadequate lighting often limit their *** this article, we propose a range of software optimization techniques and training strategies centered on data augmentation. These strategies aim to enhance a diverse array of models integral to real-time face alignment algorithms. We advocate for a broader set of evaluation criteria, enabling innovative assessments that mitigate common issues encountered in real-time monitoring *** experimental findings showcase that models developed using our suggested methodologies exhibit smoother tracking, enhanced speed, reduced size, heightened accuracy, and greater resilience in challenging conditions. © 2024 IEEE.

关键词： Eye movements

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Enhancing Security in SMS by Combining NLP Models Using Ensemble Learning for Spam Detection with image Steganography Integration 2

Enhancing Security in SMS by Combining NLP Models Using Ense...

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2nd International conference on Edge Computing and Applications, ICECAA 2023

作者： Kumar, Aditya Fancy, C. Srm Institute of Science and Technology Department of Networking and Communications Chennai Kattankulathur India

ISBN: (纸本)9798350347579

Spam SMS messages are a prevalent problem in today's world and have become a source of annoyance for users. This research study proposes a novel approach to detect SMS spam using Natural Language processing (NLP) and creating multiple models and apply ensemble learning on them. The proposed method includes pre-processing the data with NLP techniques such as stopwords removal, stemming, and lemmatizing, extracting relevant features from the text data, and transforming it into numerical representations. The performance of the proposed method was evaluated on a real-world dataset and compared to traditional machine learning algorithms such as Naïve Bayes, SVM. The multiple classifiers are trained on the transformed data, and an Ensemble Learning algorithm is applied to combine their predictions to obtain a more accurate result. The resulting model KNR gave higher accuracy than traditional models. The custom model was integrated into a custom image Steganography tool which can hide textual data into an image using 1 bit Lease Significant Bit (LSB) technique and while decrypting a hidden message, the custom model would detect if the textual data is spam or not. The purpose of the image Steganography tool is to provide users with a secure method of communication. For example, sender can hide sensitive data they wish to send to the receiver by hiding it inside an image and the receiver decrypting it to get the original message. If a malicious actor finds out about this communication method and tried to send spam links or messages by hiding it in an image and sending it to its victim, the custom spam detection model will detect it. © 2023 IEEE.

关键词： Learning systems

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Performance Analysis of Improved Sub-band Sparse Adaptive Algorithm for Acoustic Channel Identification

Performance Analysis of Improved Sub-band Sparse Adaptive Al...

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2024 International conference on Advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Alilouche, Ali Bendoumia, Redha Guessoum, Abderrezak University of Blida 1 Signal Processing and Image Laboratory BP N° 270 road of Soumaa Blida Algeria University of Blida 1 Detection Information and Communications Laboratory BP N° 270 road of Soumaa Blida Algeria

ISBN: (纸本)9798350353754

In this paper, we present the performance analysis of new Sub-band Improved Proportionate NLMS algorithm to improve the convergence rate of dispersive acoustical channel identification. Although the existing proportionate algorithms lately implemented had a good convergence speed in sparse systems, however they suffer from slow convergence speed in case of dispersive channels. This proposed algorithm based on Improved Proportionate NLMS incorporated into sub-band adaptive filtering structure denoted by S-IPNLMS shows good results in the latter case. In this approach, sub-band adaptive filtering is utilized to address challenges encountered in long adaptive filters, such as their slow convergence rate. Simulation results show that the sub-band IPNLMS performs better than the sub-band NLMS, PNLMS when the acoustical impulse response is dispersive. © 2024 IEEE.

关键词： Adaptive filtering

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Automatic Supervision of Online Assessments Using System Process Information and Random Photography 45th

Automatic Supervision of Online Assessments Using System Pro...

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45th Annual conference of the South-African-Institute-of-Computer-Scientists-and-Information-Technologists (SAICSIT) on Human- Machine-Digital-Convergence

作者： Sekokotoana, Malia Mhlongo, Siyabonga Ade-Ibijola, Abejide Univ Johannesburg Dept Appl Informat Syst Johannesburg South Africa Univ Johannesburg Johannesburg Business Sch JBS Innovat Lab Res Grp Data Artificial Intelligence & Innovat Di Johannesburg South Africa

ISBN: (纸本)9783031648809;9783031648816

Recently, online teaching and learning have seen a notable uptrend in adoption, subsequently increasing interest in conducting online assessments. The limitation of remote online assessments lies in the challenge of supervising the individual being assessed. For this reason, many consider human supervision a superior method for maintaining the integrity of assessments. This paper introduces algorithm-driven techniques for the automated supervision of online assessment-takers by analysing system processes on their devices and conducting random photographic monitoring. These techniques, along with their associated algorithms, have been encapsulated into a proof of concept tool. The approach aims to deter assessment-takers from accessing unauthorised files on their devices during assessments and to instil a sense of being monitored. The system is built around two primary components: one that monitors process activity and another that analyses images captured through the assessment-taker's device webcam. Data collected through these methods are further analysed using facial recognition and additional algorithms to detect behaviours potentially indicative of cheating during the assessment. Initial testing of the proposed tool achieved a 96.3% accuracy rate in image analysis for identifying cheating behaviour. Moreover, university lecturers' evaluations strongly support the tool's potential to deter cheating, its effectiveness in detection, and its role in maintaining the integrity of online assessments. Future research is recommended to address the challenges identified with the proof of concept tool, with the objective of enhancing both the accuracy and the overall effectiveness of the proposed techniques.

关键词： Online proctoring Online assessments Proctoring systems Facial recognition image processing Process monitoring

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