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

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VisNow-Medical—A Visual Analysis Platform for Medical Data processing 10th

VisNow-Medical—A Visual Analysis Platform for Medical Data ...

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10th KES International conference on Innovation in Medicine and Healthcare, KES-InMed 2022

作者： Regulski, Piotr Szopinski, Kazimierz Department of Dental and Maxillofacial Radiology Faculty of Medicine and Dentistry Medical University of Warsaw Binieckiego 6 St Warsaw Poland

ISBN: (纸本)9789811934391

The VisNow Medical platform is a set of integrated algorithms for visual analysis of medical data and is an extension of the VisNow platform used for image processing and visualization. VisNow Medical platform emphasizes on the ease of development of case-specific, multi-platform medical applications, with the possibility of processing one-, two-, and three-dimensional data in terms of filtering, denoising, segmentation, mapping, classification, quantitative and qualitative visual processing. VisNow Medical facilitates the research and development process in medical field. This research presents several applications of the platform for magnetic resonance, computed tomography and image processing applications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medical applications

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CyberSaver - A Machine Learning Approach to Detection of Cyber Bullying 16

CyberSaver - A Machine Learning Approach to Detection of Cyb...

引用

16th International conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Jia, Hii Lee Hameed, Vazeerudeen Abdul School, Muhammad Ehsan Rana Asia Pacific Univ Sch Comp Technol Pk Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781665426787

In this modern era of extensive use of online resources there has been reports of numerous cases of cyberbullying. Although awareness through medical health support systems such as counselling and psychological assistance is available, a system to combat threats is needed to handle the increasing rate of cyber bullying. This paper presents a model that can be used to detect and report cyberbullying with the use of machine learning techniques. A careful selection of the machine learning algorithms has been identified that could enable better accurate detection. The model was transformed into a prototype in python to evaluate the effectiveness of the model in detecting cyber bullying. The proposed model primarily focusses on test based and image-based threats as they are more common than other forms of cyber bullying.

关键词： Convolutional Neural Network Cyber Bullying Machine Learning Support Vector Machine Text processing image processing

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Compression and Denoising of Medical images Using Autoencoders 1

Compression and Denoising of Medical Images Using Autoencode...

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1st International conference on Recent Trends in Microelectronics, Automation, Computing and Communications systems, ICMACC 2022

作者： Senapati, Ranjan K. Badri, Rohit Kota, Anudeep Merugu, Nithin Sadhul, Suraj Vnr Vjiet Bachupally Dept.of Ece Nizampet Hyderabad India

ISBN: (纸本)9781665496049

In medical image analysis image compression and denoising is an important processing steps for remote analytics. A number of algorithms are proposed in the literature with varying degrees of denoising performances. In this paper, a 3-layer autoencoder model to compress and denoise grayscale medical images is proposed. Gaussian noise with a noise factor of 0.5 is added and passes through 3 convolution and 3 max pool layers. The reverse process is made for image denoising. The proposed model learns to denoise data with a high PSNR and a high compression ratio after training. Most of the denoised images show over 30 dB PSNR with a 4:1 compression ratio. images can be combined to increase sample size and overall denoising performance. © 2022 IEEE.

关键词： image denoising

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Multi-modality Medical image Fusion Employing Various Contrast Enhancement Techniques 2nd

Multi-modality Medical Image Fusion Employing Various Contra...

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Emergent Converging Technologies and Biomedical systems Select Proceedings of the 2nd International conference, ETBS 2022

作者： Jain, Shruti Pal, Bandana Sood, Rittwik Jaypee University of Information Technology Himachal Pradesh Solan Waknaghat India Indira Gandhi Delhi Technical University for Women Delhi India NIT Hamirpur Himachal Pradesh Hamirpur India

ISBN: (纸本)9789819922703

Clinical imaging has a major role in healthcare applications. The blur and the noise of the picture are eliminated, which improves the contrast and provides information about the image. But to increase the precision of the image, it is first improved. The tasks of improving medical pictures are to intensify an area of concern. Because of such issues, image processing techniques like enhancement and fusion algorithms aid human visuals in making unbiased judgments of brain images using CT and MRI. The main aim of this research paper is to exploit the benefits of spatial enhancement methods, namely binarization, median filter, contrast stretching, and contrast limited adaptive histogram equalization (CLAHE). This helps in attaining greater quality images. The enhanced images are followed by the fusion of images using principal component analysis, discrete wavelet transform, and Laplacian re-decomposition (LRU) fusion methodology. The comparative analysis is done based on qualitative and quantitative parameters and found that the results using CLAHE and LRU outperform other techniques. Later, the model is validated on the available datasets and the results are in consensus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Median filters

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Key Technologies on High-capacity Satellite imagery Compression 11

Key Technologies on High-capacity Satellite Imagery Compress...

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2023 IEEE 11th International conference on Information, Communication and Networks, ICICN 2023

作者： Fang, Huoneng Han, Pengfei Xiao, Huachao Xi'An Institute of Space Radio Technology Information Processing Technology Research Lab Xi'an China Xi'an China

ISBN: (纸本)9798350314014

With the continuous progress of science and technology, the accuracy of satellite remote sensing, detection and reconnaissance technologies is getting higher and higher, and the amount of data generated by them is also getting larger and larger. The amount of image data generated by high-resolution satellite imaging systems often reaches hundreds of Gb/s, which, coupled with the limited transmission bandwidth and storage space constraints, puts great pressure on data caching and transmission. The upper limit of transmission rate provided by traditional satellite data transmission methods has been difficult to meet the demand of inter-satellite and satellite-ground high-speed communication. To address this, a high-capacity satellite image data compression algorithm is proposed. Firstly, for the problem of redundancy of handover satellite image data, image fusion or stitching technology is used to extract valuable information from different images and synthesize high-quality images to reduce data redundancy. Secondly, to address the problem of repeated transmission of re-entry satellite data, multi-temporal data is analyzed to determine the change process of re-entry satellite image features and segment the change region in the image. In the re-entry process, only the information of the change region needs to be transmitted, avoiding multiple transmissions of duplicate information. Finally, to solve the problem of low compression efficiency of existing on-satellite compression algorithms, a new 'image-information-image' data compression strategy is explored to achieve high compression ratio and high fidelity of satellite image compression. In order to achieve high capacity compression and efficient transmission of satellite images with limited bandwidth and storage space, and to lay the foundation for fast and efficient intelligent services for all-day, all-weather observation of the Earth. © 2023 IEEE.

关键词： image compression

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A New hybrid deep learning coder for Alzheimer classification

A New hybrid deep learning coder for Alzheimer classificatio...

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International conference on image and Signal processing and their Applications (ISPA)

作者： Zoulikha Bebboukha Athmane Zitouni Department of Electrical Engineering Faculty of Science and Technology University of Mohammed Khider Biskra Algeria Vision and Communication Systems Laboratory (VCS) Biskra

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Deep learning algorithms offer distinct advantages over other machine learning methods, enabling the exploitation of advanced techniques to analyze brain MRI scans. In a recent research study, a combination of ResNet-50 and DenseNet-201 convolutional neural network models was utilized to extract crucial features from brain MRI images, which were then fed into a compact classification model. The proposed model demonstrated remarkable results in accurately classifying the stages of Alzheimer’s disease. It outperformed all other approaches using the same dataset and achieved an outstanding accuracy of 99.9 % in categorizing the four cases, affirming the effectiveness and promising potential of this proposed model in the precise diagnosis and classification of Alzheimer’s disease.

关键词： Deep learning Machine learning algorithms Databases Magnetic resonance imaging Signal processing algorithms Signal processing Feature extraction

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Proximity-Based Reward systems for Multi-Agent Reinforcement Learning

Proximity-Based Reward Systems for Multi-Agent Reinforcement...

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image processing and Robotics (ICIP), International conference on

作者： Marc-André Blais Moulay A. Akhloufi Dept. of Computer Science Université de Moncton Perception Robotics and Intelligent Machines Lab (PRIME) Moncton NB Canada

ISBN: (数字)9798350374766

ISBN: (纸本)9798350374773

Unmanned vehicles, such as drones, have surged in popularity in recent years. Swarms of these vehicles offer new opportunities in applications such as agriculture, weather monitoring and natural events management. However, efficiently controlling a large swarm of unmanned vehicles poses a significant challenge. Intelligent solutions, particularly reinforcement learning, have been proposed to address this challenge. We introduce a proximity-based reward system for multi-agent reinforcement learning to handle the issue of reward sparsity. Our goal is to develop an approach for controlling a swarm towards a common objective while maintaining robust swarm cohesion. In this paper, we compare various distance-based functions to build a comprehensive reward system. Specifically, we explore the Euclidean, Manhattan, Chebyshev and Minkowski distances in our experiments. We evaluate the impact of these proximity-based reward systems on four reinforcement learning algorithms. We conduct a comparison of our reward systems using various metrics during validation and test episodes. Our goal is to highlight the importance of comparing different algorithms and distance functions in the development of multiagent reinforcement learning systems.

关键词： Measurement image processing Reinforcement learning Chebyshev approximation Control systems Agriculture Task analysis

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Optimizing Convolutional image processing on RISC-V Using Branch Prediction on FPGA

Optimizing Convolutional Image Processing on RISC-V Using Br...

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IEEE Region 10 International conference TENCON

作者： Mekala Bindu Bhargavi Sujay Krishnan Subramanian Sri Parameswaran Soumya J Department of EEE BITS Pilani-Hyderabad Campus Hyderabad India Department of Electrical and Computer Engineering University of Sydney Australia

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

This paper explores the enhancement of convolutional image processing on a RISC-V based architecture through the implementation of branch prediction (BP) techniques. Con-volution, a critical operation in image processing, is essential for tasks such as edge detection, blurring, and feature extraction. Efficient execution of convolution operations, particularly in a RISC-V architecture, requires effective BP to minimize delays caused by conditional operations. This study presents the development of a 5-stage pipelined 32-bit RISC-V processor, integrated with a 2-bit saturating counter branch predictor using a Branch History Table (BHT). The efficiency of the RISC-V processor was evaluated by synthesizing and implementing it using Xilinx Vivado on the Kintex- 7 KC705 Evaluation Platform and testing with a convolution algorithm written in assembly language. Experimental results demonstrate that optimizing BP significantly improves the execution efficiency of convolution operations, making it a promising approach for advanced image processing applications.

关键词： Machine learning algorithms Convolution image processing image edge detection Prediction algorithms Throughput Real-time systems History Assembly Testing

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Parallel Dual Broad Learning Surrogate Assisted Semi-supervised Kernel Multi-objective Evolutionary Rough Fuzzy Clustering Algorithm for image Segmentation

Parallel Dual Broad Learning Surrogate Assisted Semi-supervi...

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Natural Language processing (ICNLP), International conference on

作者： Weilin Du Feng Zhao School of Communications and Information Engineering (School of Artificial Intelligence) Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

To improve the segmentation performance of multi-objective evolutionary clustering algorithms, this paper proposes a parallel dual broad learning surrogate assisted semi-supervised kernel multi-objective evolutionary rough fuzzy clustering algorithm (PDBLS-SKMRFC) for image segmentation. First, the algorithm constructs a parallel dual broad learning surrogate assisted multi-objective evolutionary framework. It uses two broad learning systems as classification and regression surrogate models to evaluate the population in parallel manner. In the framework, a multi-population division strategy guided by dual broad learning system, a dominant individual crossover strategy, a sub-population mutation strategy, and a hierarchical environment selection strategy are designed to obtain more excellent offspring populations. Then, a semi-supervised kernel rough fuzzy intra-class compactness function is constructed, which uses a few labels and pseudo-labels as supervision information to improve the image segmentation performance, and evaluates the clustering quality together with the kernel separability function. Finally, a semi-supervised kernel rough fuzzy clustering validity index is designed to select the optimal solution from the final non-dominated solution set for image segmentation. Experimental results on color images show the effectiveness of the proposed algorithm.

关键词： Learning systems image segmentation Clustering algorithms Color Linear programming Natural language processing Classification algorithms Indexes Kernel Standards

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A Multilevel Framework for Accelerating uSARA in Radio-Interferometric Imaging

A Multilevel Framework for Accelerating uSARA in Radio-Inter...

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European Signal processing conference (EUSIPCO)

作者： Guillaume Lauga Audrey Repetti Elisa Riccietti Nelly Pustelnik Paulo Gonçalves Yves Wiaux Univ Lyon Inria EnsL UCBL CNRS LIP UMR 5668 Department of Actuarial Mathematics & Statistics Heriot-Watt University Edinburgh United Kingdom Institute of Sensors Signals and Systems Heriot-Watt University Edinburgh United Kingdom Ens de Lyon CNRS Laboratoire de Physique

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper presents a multilevel algorithm specifically designed for radio-interferometric imaging in astronomy. The proposed algorithm is used to solve the uSARA (unconstrained Sparsity Averaging Reweighting Analysis [2]–[4]) formulation of this image restoration problem. Multilevel algorithms rely on a hierarchy of approximations of the objective function to accelerate its optimization. In contrast to the usual multilevel approaches where this hierarchy is derived in the parameter space, here we construct the hierarchy of approximations in the observation space. The proposed approach is compared to a reweighted forward-backward procedure, which is the backbone iteration scheme for solving the uSARA problem.

关键词： Imaging Signal processing algorithms Europe Signal processing Approximation algorithms Linear programming Data models image restoration Optimization Astronomy

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