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

共 13664 条记录，以下是1411-1420 订阅

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A Novel image Segmentation Method Based on Random Walk Combined with Robust Sampling Prior

A Novel Image Segmentation Method Based on Random Walk Combi...

引用

International conference on Business Intelligence and Information Technology, BIIT 2023

作者： Yao, Guilin Li, Huimin Yang, Huixin Li, Shizhou Harbin University of Commerce Harbin150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin150028 China

ISBN: (纸本)9789819739790

In the image segmentation technique, how to accurately solve the boundary of foreground objects and background is the key problem, and the typical unsupervised interactive binary segmentation algorithms in this include Grab Cut, random walker and K-means clustering, etc. Among them, the random walker algorithm is a classical algorithm of segmentation based on graph theory, which has the advantages of strong interpretation and high efficiency and is suitable for segmentation fields with low boundary requirements. However, because the random walk algorithm only uses the neighborhood information of unknown pixels and does not make full use of the sample prior information at longer distances, the results of this method are segmented more coarsely. This paper proposes a random walker image segmentation method based on robust sampling. The method selects the sample points that are far from the unknown pixels for robust sampling and selects the best foreground and background sample pairs for the calculation of the foreground prior probability. After that, the computed prior probability values are fused with the random walker algorithm as a way to improve the accuracy of the segmentation results. The experimental results show that the algorithm of the addition of prior information can obtain better segmentation results than the original random walker segmentation. The experiments in this paper compare the mean square error values of some typical images to further validate the effectiveness of this method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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Real-Time Pedestrian Detection in Hazy Weather for Surveillance systems

Real-Time Pedestrian Detection in Hazy Weather for Surveilla...

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

作者： Bouafia Yassine Guezouli Larbi Hicham Lakhlef Computer Science Department LaSTIC Laboratory University of Batna2 Algeria Batna Algeria Higher National School of Renewable Energy Environment and Sustainable Development LEREESI Laboratory Batna Algeria Sorbonne University University of Technology of Compiègne Compiègne France

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Real-time pedestrian detection is an expanding research topic that is crucial in various vision-based applications. Effective detection of pedestrians in various environments and situations will significantly improve human safety. However, the performance of current pedestrian detection algorithms faces challenges in scenarios with reduced visibility and blurred appearance, such as hazy weather conditions. This study addresses this issue by seeking an optimal balance between accuracy and speed for deploying deep learning-based pedestrian detection in real-time intelligent surveillance systems. For this aim, we used the transfer learning technique to fine-tune small versions of state-of-the-art YOLO models (YOLONAS-small, YOLOv8-small, YOLOv6-small, and YOLOv5-small) to perform pedestrian detection in hazy weather effectively. Experiment results demonstrate the effectiveness of our methods in accurately detecting pedestrians under hazy conditions by achieving high average precision and F-Score. Moreover, they all operate in real time, with impressive processing speeds ranging from 52.41 to 186.22 FPS.

关键词： YOLO Training Pedestrians Surveillance Transfer learning Signal processing Real-time systems

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Maritime image Stabilization: A Comprehensive Review of Techniques and Challenges

Maritime Image Stabilization: A Comprehensive Review of Tech...

引用

Electronic Technology and Information Science (ICETIS), International conference on

作者： Enping Wei Yong Chai Tan Vin Cent Tai Yanan Hao Xiaodong Zhang Tian Zhang Centre for Modelling and Simulation Faculty of Engineering Built Environment and Information Technology SEGI University Kuala Lumpur Malaysia

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

image stabilization plays a crucial role in providing accurate and reliable visual information for machine vision applications. In maritime applications, such as unmanned ship navigation, where six degrees of freedom (DOF) motion and harsh maritime conditions prevail, the efficacy of image stabilization technology is vital for robust image processing algorithms. This paper offers a comprehensive review of image stabilization techniques tailored for maritime environments, developed over the past two decades. We analyzed a total of 39 research articles on the subject, sourced from Web-of-Science, SCOPUS, and the Engineering Index databases, discussing potential research directions to address the limitations of current image stabilization methods, with special consideration for the unique requirements of ship-borne cameras. It provides an up-to-date overview of the techniques, limitations, and algorithms of ship-borne cameras for maritime applications, identifying current knowledge gaps and areas requiring further research. This review aims to guide the development of new technologies and methods to improve the performance of image stabilization systems in maritime contexts.

关键词： Vibrations Visualization Reviews Target recognition Machine vision Stability criteria Object detection

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Digital Modulation Identification in Spatial Modulation MIMO systems

Digital Modulation Identification in Spatial Modulation MIMO...

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

作者： Dalil Beknadj Hakim Tayakout Mohamed Tounsi Fatma Zohra Bouchibane Elhocine Boutellaa LIMed Laboratory University of Bejaia Bejaia Algeria Center for Development of Advanced Technologies (CDTA) Algiers Algeria LAMOS Laboratory University of Bejaia Bejaia Algeria Institute of Electrical Engineering and Electronics University Mhamed Bougara Boumerdes Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Cognitive radio (CR) is a game-changing technology that allows devices to adjust transmission and/or reception parameters in real time, improving wireless communication efficiency. Digital modulation identification (DMI) plays a crucial role in in CR, wireless communications, and spectrum management, providing adaptive and efficient signal processing. In this paper, we introduce a DMI approach designed for spatial modulation (SM) within the context of wireless multiple input multiple output (MIMO) technologies. In this study, we use the higher order statistics (HOS) as feature-based digital modulation identification (FB-DMI) to assess the performance of frequently utilized classifiers namely, k-nearest neighbor (KNN), and decision tree (DT). To train these classifiers, several configurations of the SM-MIMO network were evaluated, using the zero forcing (ZF) equalizer. Experiments show that KNN outperforms DT in identifying modulation schemes across all SNR levels.

关键词： Resistance Digital modulation Signal processing algorithms Interference Robustness Real-time systems Object recognition

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Experimental Results of Local Backprojection for Monostatic THz SAR Imaging

Experimental Results of Local Backprojection for Monostatic ...

引用

International Workshop on Mobile Terahertz systems (IWMTS)

作者： Yevhen ivanenko Aman Batra Viet Thuy Vu Mats I. Pettersson Thomas Kaiser Blekinge Institute of Technology Karlskrona Sweden University of Duisburg-Essen Duisburg Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

The use of THz frequencies has enabled the opportunity to perform synthetic-aperture radar (SAR) imaging at the sub-mm level. It is of great interest for applications where high-resolution remote sensing in short range is required. However, with the increase of the operating frequencies from microwave to THz, the SAR image formation algorithms work with a larger amount of data and become more sensitive to phase errors that can be caused by insufficient signal sampling rate or physical factors that cause platform deviations. This motivates the use of fast image formation capable to handle phase errors. In this paper, we present the experimental results on the performance of the local backprojection (LBP) algorithm for processing THz SAR signals. The LBP algorithm has been tested with the real data in the frequency range 0.325–0.5 THz. The results demonstrate the efficiency of the LBP algorithm for the SAR scene reconstruction and highlight the necessity of the use of two times higher signal upsampling to achieve reconstruction accuracy similar to the global backprojection algorithm.

关键词： Terahertz wave imaging Radar remote sensing Accuracy Heuristic algorithms Signal sampling Approximation algorithms Radar polarimetry Reliability image reconstruction Remote sensing

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Efficient Person Detection on Single Board Computers: A Comparative Analysis of algorithms

Efficient Person Detection on Single Board Computers: A Comp...

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International conference on Advanced Computing and Communication systems (ICACCS)

作者： Shrajan Jain Abhishek Kumar Pooja Goswami Raghav Khajuria Prateek Dayal Somnath Banerjee CRL – Bharat Electronics Limited Ghaziabad India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This research paper conducts an exhaustive technical evaluation and comparative analysis of person detection algorithms optimized explicitly for deployment on single-board computers (SBCs). The burgeoning demand for edge computing solutions, particularly on constrained platforms like SBCs, necessitates the optimization of object detection algorithms, specifically targeting person detection. This study meticulously examines and contrasts the performance of varied person detection algorithms tailored to SBCs, meticulously scrutinizing metrics encompassing accuracy, computational efficiency, CPU utilization, and latency. By conducting rigorous experimentation and in-depth analysis, this research dissects the intricate trade-offs inherent in algorithmic intricacy, computational efficacy, and the real-time inference capabilities crucial for edge computing scenarios. The research outcomes yield profound technical insights essential for strategic algorithm selection, aiding in the identification of the most adept person detection algorithm primed for deployment on SBCs across diverse applications, spanning surveillance systems to edge-driven robotics. Ultimately, this comprehensive comparative analysis serves as a robust technical blueprint, offering vital guidance for refining person detection algorithms on resource-constrained edge devices, thereby propelling advancements in edge computing and bolstering real-time object detection capabilities.

关键词： Measurement YOLO Accuracy Computational modeling image edge detection Real-time systems Inference algorithms Central processing Unit Detection algorithms Edge computing

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Surface Drift Debris Segmentation and Visualization through YCbCr Colour Space Thresholding using Low Power FPGA

Surface Drift Debris Segmentation and Visualization through ...

引用

International Symposium on VLSI Design and Test (VDAT)

作者： Sarath Kumar K Akash Iyer Swastik Raj Behera K Sivasankaran School of Electronics Engineering VIT University Vellore India

ISBN: (数字)9798350380101

ISBN: (纸本)9798350380118

The accumulation of oceanic debris is a matter of increasing concern in today’s world. Thus, it is crucial to develop an autonomous solution that can segment debris in real-time and at high speeds while maintaining cost-effectiveness, low power consumption, and scalability. FPGAs are well-known to be low-power, high-speed alternatives to CPUs and GPUs that can also be reprogrammed at the user’s convenience, allowing for improvements to be made to the system easily. However, segmentation algorithms typically utilize complex machine learning or image processing algorithms that are difficult to implement effectively on hardware. In this work, an algorithm using thresholding in the YCbCr colour space is proposed and its accuracy is evaluated using Entropy, True Positive Rate, and True Negative Rate. The segmented regions in all the test images are found to have an entropy greater than 7 while the average True Positive Rate is 91.14%. The average True Negative Rate of the segmented regions is found to be 93.8%. The algorithm was deployed on a Cyclone iv-E FPGA wherein live camera feed was processed on the FPGA and the output was visualized on a VGA monitor. The overall performance was satisfactory, although some glitches and jitters were observed, likely due to post-processing steps required for integrating the camera feed with the VGA monitor. The algorithm proved to be robust in segmenting ocean debris under good lighting conditions. However, accuracy decreased when the ocean colour significantly deviated from its expected blue or when the image was not an aerial view.

关键词： image segmentation Visualization Thresholding (Imaging) Machine learning algorithms Accuracy image color analysis Real-time systems Entropy Feeds Field programmable gate arrays

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image to Audio Conversion to Aid Visually Impaired People by CNN 4

Image to Audio Conversion to Aid Visually Impaired People by...

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4th International conference on Electronics and Sustainable Communication systems, ICESC 2023

作者： Sivaganesan, D. Venkateshwaran, M. Dhinesh, S.P. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Sri Krishna Arts and Science College Department of Computer Science Coimbatore India

ISBN: (纸本)9798350300093

This study suggests an innovative method for helping people who are blind or visually handicapped by turning visuals into sounds. In the proposed system, audio descriptions are produced in real-time together with significant features that are extracted from photos using deep learning algorithms. The proposed work is developed to be user-friendly, which includes a simple interface that aids blind individuals to easily capture and process images using a mobile device. A user research was undertaken to assess the efficiency of the suggested method, and the results were encouraging in terms of precision and usability. This initiative offers a promising technique to give people who are blind or visually impaired an alternate means of perceiving and interacting with their environment, therefore improving their quality of life. The suggested picture to audio converter system aims to overcome the drawbacks of current assistive devices that rely on braille or textual descriptions. Blind people can more easily interpret visual information that is necessary for daily life, such as recognising items, interpreting signs, or navigate unfamiliar situations, through offering audio descriptions of images. The system makes use of recent deep learning developments that have significantly improved picture identification as natural language processing. As a result, the suggested technique has the ability to offer audio descriptions that are more precise and comprehensive than current methods. This technology has the potential to be implemented into a variety of products, from cellphones to intelligent glasses, and could significantly improve the lives of people who are blind or visually impaired. © 2023 IEEE.

关键词： Convolutional neural networks

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Identification of Early Diabetic Retinopathy Through Outlier Based Skimpy Regularization Fuzzy Clustering with Meta-Cognitive Neural Network

Identification of Early Diabetic Retinopathy Through Outlier...

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Intelligent algorithms for Computational Intelligence systems (IACIS), International conference on

作者： Savariappan L S. Thiyagarajan Department of CSE St. Joseph University Dimapur Nagaland

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Background: Traditional screening methods face limitations in terms of efficiency and accuracy, prompting the explorationTraditional screening methods for diabetic retinopathy often struggle with efficiency and accuracy. To address these challenges, we have explored advanced technologies like outlier-based Skimpy Regularization Fuzzy Clustering and the Meta- Cognitive Neural Network (McNN). By integrating artificial intelligence and sophisticated image processing techniques, our intelligent screening system aims to significantly enhance both the speed and precision of diabetic retinopathy diagnosis, enabling timely intervention for at-risk individuals. Methods: Our analysis compared various classifiers, including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and the proposed McNN. Findings: The McNN classifier outperformed the others, demonstrating exceptional performance with a specificity of 93.90%, sensitivity of 99.06%, and an overall accuracy of 97.89%. Notably, the McNN achieved a Positive Predictive Value (PPV) of 100%, indicating flawless performance in predicting positive cases. These results underscore the potential of advanced AI technologies to revolutionize diabetic retinopathy screening by improving diagnostic accuracy and efficiency. results: Early detection is crucial in mitigating the impact of diabetic retinopathy, ultimately reducing the risk of irreversible vision loss in diabetic patients and alleviating the burden on global healthcare systems. The significance of the McNN classifier's performance, reinforcing the importance of advanced AI in early diabetic retinopathy detection and intervention.

关键词： Diabetic retinopathy Accuracy Sensitivity Telemedicine Scalability image processing Medical services Convolutional neural networks Artificial intelligence Monitoring

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Innovative image processing Techniques for Crack Detection and Analysis

Innovative Image Processing Techniques for Crack Detection a...

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International conference on Computer and Communications Technologies (ICCCT)

作者： G.Adiline Macriga Santhiyashri. R Vidhula. M. A Department of Information Technology Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The integrity and safety of infrastructure are critical, yet traditional methods for crack detection are often plagued by inefficiencies and inaccuracies. In addition to being labor-intensive and time-consuming, manual inspections are also prone to human mistake, which increases the possibility that tiny or hidden cracks will go unnoticed and jeopardize structural safety. This project focuses on creating an automated crack detection system that employs convolutional neural networks (CNNs) and sophisticated image processing algorithms in order to overcome these difficulties. The proposed system aims to significantly improve the detection and analysis of cracks in structural materials. By enhancing image preprocessing to improve crack visibility and minimize noise, the system ensures more accurate identification and segmentation of cracks. This automated approach's efficacy is compared with other established techniques, indicating its potential to offer a more dependable option for ongoing structural health monitoring and maintenance. This innovation not only streamlines the inspection process but also contributes to the longevity and safety of critical infrastructure.

关键词： Technological innovation Roads Noise Inspection Streaming media Real-time systems Safety Convolutional neural networks Monitoring Morphological operations

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