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

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A New hybrid deep learning coder for Alzheimer classification

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

引用

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...

引用

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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Circular Synthetic Aperture Radar for Near-field Terahertz Imaging 5

Circular Synthetic Aperture Radar for Near-field Terahertz I...

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5th International conference on Multimedia, Signal processing and Communication Technologies (IMPACT)

作者： Gezimati, Mavis Singh, Ghanshyam Univ Johannesburg Ctr Smart Informat & Commun Syst Dept Elect & Elect Engn Sci Auckland Pk Kingsway CampusPOB 524 ZA-2006 Johannesburg South Africa

ISBN: (纸本)9781665476478

The potential of synthetic aperture radar (SAR) systems to yield high resolution images has led to its exploration in new applications. The conventional microwave radar imaging suffer limited range resolution in the sub-millimeter range and optical imaging methods are constrained by the diffraction limit. In the Terahertz (THz) spectrum, SAR systems are capable of operating in all weather, all time and penetrating through clouds, smoke, dust etc. to achieve better resolution images beyond the diffraction limit. In this paper, the different aspects of Multimedia computing are investigated in THz circular SAR as a new mode of radar imaging for near-field applications for example indoor environment. We have explored a new toolbox that enables the rapid development of near-field THz imaging SAR systems and generation of large near-field THz imaging scenarios that could be used for data driven applications. A circular SAR imaging system is presented for 2D (two-dimensional) imaging of targets in THz-SAR and the radar images are reconstructed using the Back Propagation and Polar Formatting algorithms. The SAR performance evaluation metrics are reported and scope for future work is discussed.

关键词： Synthetic aperture radar (SAR) THz imaging Circular SAR Near-field Radar image reconstruction High resolution imaging Reconstruction algorithms MIMO FMCW Multimedia

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Research on Fruit Picking Positioning Based on Binocular Vision

Research on Fruit Picking Positioning Based on Binocular Vis...

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Signal processing and Intelligent Computing (SPIC), International conference on

作者： Lijuan Qin Xiaohan Wang Yubin Zhu Ting Wang School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Based on YOLOv5 object detection algorithm, fruit images are extracted from complex backgrounds in response to occlusion of branches and leaves, fruit overlap, and light effects caused by environment. Method of adding center coordinate value of image based on prediction box of fruit image determines centroid coordinate of target image. Then, by optimizing the focus camera and using Zhang Youzheng's calibration method, the image is calibrated using Matlab calibration box. Obtained image is stereo corrected using local stereo matching algorithm to obtain three-dimensional coordinates of centroid of target fruit, which are then converted into physical coordinates for positioning, After actual measurement, a positioning result with an error of ± 20mm was obtained, confirming its applicability for practical positioning operations.

关键词： YOLO Visualization Signal processing algorithms Visual systems Signal processing Cameras Prediction algorithms Calibration Standards Matlab

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Construction Site Fence Recognition Method Based on Multi-Scale Attention Fusion ENet Segmentation Network 35

Construction Site Fence Recognition Method Based on Multi-Sc...

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35th International conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Zhang, Xing Tang, Tinglong Wu, Yirong Quan, Tingwei Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China Wuhan National Laboratory for Optelectronics Huazhong University of Science and Technology Wuhan China

In this paper, we propose a fence recognition method based on the ENet (Efficient neural Network) segmentation network to address the problems of traditional segmentation networks, which have poor performance in recognizing fences with a large range of scale variations and hollow structures. Firstly, a multi-scale attention fusion ENet segmentation network is designed, which is trained using the fence with obvious color features. Then, a morphological algorithm is used to process the predicted image to restore the fence segmentation results. The designed multi-scale attention fusion segmentation network performs better on fence datasets than traditional methods. In addition, the activation function Leaky_Relu6 further enhances the stability and generalization ability of the network. The experiments are conducted on 540 fence images from different construction sites, and the computed IoU is 90%. The processing speed is about 28 frames per second. The experimental results show that our proposed network outperforms traditional segmentation algorithms in fence recognition performance, and achieves robustness in different construction scenarios while meeting the requirements of both accuracy and speed. © 2023 Knowledge systems Institute Graduate School. All rights reserved.

关键词： image segmentation

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Advanced Hybrid algorithms for High-Efficiency image Compression in Satellite and Drone systems

Advanced Hybrid Algorithms for High-Efficiency Image Compres...

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Recent Progresses in Science, Engineering and Technology (ICRPSET), International conference on

作者： Priyankar Biswas Ualiur Rahman Rahat Ashiquzzaman Lemon Ishita Mazumder Department of Electrical and Electronic Engineering Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh

ISBN: (数字)9798331509477

ISBN: (纸本)9798331509484

In satellite and drone imaging, a huge amount of data is generated; therefore, image compression should be done efficiently. This hybrid algorithm document deals with Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Singular Value Decomposition (SVD) based approaches for effective image compression with quality preservation. The proposed approach will be suitable for satellite and drone images since it reduces the cost of storage and transmission. The simulations demonstrate that, for the same amount of distortion, the algorithm outperforms state-of-the-art algorithms by as much as above 48% in compression ratio or up to ten times in processing speed. This is enabled by the effective combination of strengths between lossy and lossless techniques in solving some unique challenges about satellite and drone systems, including limited resources and real-time processing. Therefore, this may be an effective means of boosting image compression in remote sensing.

关键词： image coding Satellites Transforms Discrete wavelet transforms Satellite images Discrete cosine transforms Time complexity Remote sensing Drones Singular value decomposition

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Review of various image Mining Techniques 6

Review of various Image Mining Techniques

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6th International conference on Computing Methodologies and Communication, ICCMC 2022

作者： Kavitha, C. Balaganesh, D. Sukumar, M. Mathan, J. Madurai Kamaraj University College Dept of Computer Science Tamil Nadu Madurai India Lincoln University College Faculty of Computer Science and Multimedia Malaysia

ISBN: (纸本)9781665410281

One of the easiest ways of communication is possible through interpreting wide range of images on various applications. It is the non-structural data that does not have clear semantics. image mining techniques implemented on the images are used to extract features/train the real time systems. This can be achieved using combination of various algorithms of image processing, machine learning and artificial intelligence. This paper discusses about those image mining techniques and the challenges faced by various researchers. © 2022 IEEE.

关键词： Real time systems

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