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

共 14657 条记录，以下是431-440 订阅

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Vehicle image Anomaly Detection Based on Improved K-Means Algorithm and High-Resolution Camera 2

Vehicle Image Anomaly Detection Based on Improved K-Means Al...

引用

2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Yang, Xiaoqin Alias, Syazwina Wang, Hui FoEBEIT SEGi University Kota Damansara Selangor Malaysia College of Computer and Communication Engineering Nanjing Tech University Pujiang Institute Jiangsu Nanjing210000 China

ISBN: (纸本)9798331505745

This study proposes a vehicle image anomaly detection framework based on improved K-means algorithm and high-resolution camera, which provides a new idea for road safety construction from the image processing level. The main innovation of the study contains 4 aspects: 1. A new vehicle target extraction algorithm based on deep learning is proposed. The algorithm uses the YOLOv5s network structure and combines the SimAM attention mechanism to optimize the model while improving the detection accuracy of vehicle targets. 2. A visual-based traffic flow clustering analysis is performed. This analysis achieved effective clustering of traffic flow through deep HOG feature extraction and parallel K-means clustering algorithm. 3. An optimized license plate detection and recognition algorithm based on YOLOv5 is proposed. The algorithm improves the accuracy of license plate recognition by adding the key point detectors. 4. The traditional K-means algorithm is optimized. The optimization idea is mainly to combine quantum computing and space-time theory. This optimization improves the convergence speed and clustering effect of the algorithm. The results of comparative experiments show that the improved K-means algorithm converges faster in traffic flow clustering analysis, and its license plate recognition accuracy is significantly better than traditional algorithms (such as k-means and FCM). © 2025 IEEE.

关键词： K-means clustering

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Research on image Encryption Algorithm Based on One-dimensional Logistic and Two-dimensional Hénon Chaotic System

Research on Image Encryption Algorithm Based on One-dimensio...

引用

2022 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2022

作者： Wu, Xionghui Dong, Rui Yang, Kuo Zhang, Wenli PLA Beijing100043 China Taiyuan University of Technology Taiyuan030024 China

ISBN: (纸本)9781510663350

Aiming at the problems of high leakage risk and serious information loss that may exist in the encryption and decryption of digital images, an image encryption and decryption algorithm based on chaos theory is proposed. The algorithm introduces both one-dimensional Logistic mapping and two-dimensional Hénon mapping. In the encryption process, the digital image is dislocation encrypted by Logistic mapping and diffusion encrypted by Hénon mapping, and finally the cipher image is obtained. Compared with other similar algorithms, this algorithm has the advantages of low algorithm complexity and high operation efficiency. The simulation experiment is carried out on the MATLAB platform, and the results show that the algorithm can meet the test standard in the field of digital image encryption. © 2023 SPIE.

关键词： Chaotic systems

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Application of Particle image Velocimetry Method Based on Full Convolution Neural Network in Turbulence Field 16

Application of Particle Image Velocimetry Method Based on Fu...

引用

16th IEEE International conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Xie, Zhicheng Guo, Shuqiang School of Computing Northeast Electric Power University Jilin China

ISBN: (纸本)9798331505264

Exploring fluid flow phenomena has always been a hot topic in fluid mechanics research. Particle image Velocimetry (PIV) is a non-contact technique applied in fluid mechanics laboratories for measuring the velocity field of fluids. The core of PIV lies in extracting displacement vector information from particle image pairs to ultimately generate a two-dimensional velocity field. Traditional methods typically estimate the two-dimensional velocity field using cross-correlation algorithms and optical flow techniques. In recent years, with the rapid development of artificial intelligence technologies and their high compatibility with PIV in image processing, there is significant potential and research value in designing deep learning techniques for accurately estimating fluid velocity vector fields in particle image velocimetry. Compared to traditional methods, the PIV technique proposed in this paper, based on fully convolutional neural networks, not only maintains the accuracy of velocity field estimation but also significantly enhances resolution and computational efficiency. Experiments demonstrate that the proposed method exhibits higher accuracy and stronger generalization capabilities. © 2024 IEEE.

关键词： Optical flows

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Cross-Domain Feature Extraction Using CycleGAN for Large FoV Thermal image Creation 1

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8th International conference on Computer Vision and image processing (CVIP)

作者： Rathore, Sudeep Upadhyay, Avinash Sharma, Manoj Yadav, Ajay Chand, G. Shyam Singhal, Amit Mukherjee, Prerana Lall, Brejesh Bennett Univ Greater Noida UP India Netaji Subhas Univ Technol New Delhi India Jawaharlal Nehru Univ New Delhi India Indian Inst Technol Delhi New Delhi India

ISBN: (数字)9783031585357

ISBN: (纸本)9783031585340;9783031585357

Thermal images are extensively utilized in defense operations due to their ability to capture thermal radiation. However, the limited Field of View (FoV) in thermal imaging systems often results in insufficient and fragmented large Field of View (FoV) simulation data, thereby compromising the effectiveness of defense applications using large Field of View (FoV) cameras. To address this challenge, we propose an innovative approach employing image stitching techniques to create comprehensive and detailed views. Traditional image feature extractors such as Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) are optimized for Red, Green, and Blue (RGB) images. Hence, their performance on thermal images is often sub-optimal as the thermal images contain low-resolution, reduced contrast and dynamic range, non-uniform thermal emissions, and the absence of color information, making identifying and matching robust features and descriptors difficult. To enhance feature extraction, we adopt the Cycle Generative Adversarial Network (CycleGAN) technique to convert thermal images into their RGB counterparts prior to feature extraction. This enables us to leverage the robustness of RGB-optimized feature extractors and amalgamate them with the features obtained from the original thermal images, resulting in a richer and more comprehensive feature set. The performance of this proposed network has been demonstrated and validated on various datasets, showing promising results for large Field of View (FoV) thermal image creation.

关键词： Thermal image stitching Panorama Feature Matching image Blending CycleGAN Deeplearning

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Modelling and Simulation Framework for ATR Design Evaluation 19

Modelling and Simulation Framework for ATR Design Evaluation

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conference on Electro-Optical and Infrared systems - Technology and Applications XIX

作者： Hickman, Duncan L. Koh, Eunjin Lee, Jaekyu Yoo, Hyunjung Baek, Jeonghyun Tektonex Ltd Argyll Rd Kilcreggan Argyll & Bute England Agcy Def Dev 160 Bugyuseong Dero 488beon Gil Daejeon South Korea

ISBN: (纸本)9781510655461

Automatic Target Recognition (ATR) and target tracking are fundamental functions in many military systems and so have a significant impact on a sensor system's performance. In response to the demand for increased capability, ATR designs have evolved from relatively simple filters to increasingly complex algorithms, using techniques such as artificial intelligence. Assessing the performance of image processing algorithms is a significant challenge, particularly with their increased design complexity. Increasing the complexity of a processing design tends to result in greater sensitivity to variations in scene conditions, rendering the system performance more nuanced with respect to the image content. There is a need to develop modelling and simulation design tools that better reflect the impact of image processing on the overall system performance when subjected to a wide variation in input scene and sensor platform characteristics. In this paper, the development and design of the System Performance Model (SPM) is described which provides the modelling and simulation of different image processing algorithms such as ATR. The approach taken within the SPM is to use real imagery and convert this to imagery that would be generated by the modelled camera. This process, which is described in the paper, is critical to the design of the SPM and underpins its effectiveness and accuracy. Example results are given that illustrate the design of the SPM's image conversion process.

关键词： Infrared Sensor Modelling image Simulation image Flow Models Statistical Models Infrared Camera Performance ATR Trials imagery image Restoration image Conversion image Metrics

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Analysis of the Noise in ECG Recordings on Different Devices 62

Analysis of the Noise in ECG Recordings on Different Devices

引用

62nd Annual conference of the Society-of-Instrument-and-Control-Engineers (SICE)

作者： Felix-Chavez, Andres Ikeura, Ryojun Hayakawa, Soichiro Mie Univ Grad Sch Engn Mie Japan

ISBN: (纸本)9784907764807

Cardiovascular diseases are one of the leading causes of mortality. Advances in technology have improved the monitoring of patients. However, these devices are still sensitive to noise from movement, therefore it is important to understand how the movement affects electrocardiographic recordings. In this research, three devices were tested. The signal-to-noise ratio (SNR) was used to determine the quality of the ECG recordings from each device. The Vitalgram system was the only device to present a positive average SNR value for EMA while the subjects were running. In Apandi's research the performance of several heartbeat detection algorithms was tested. Using Apandi's research data and the SNR values from this research, the performance of these algorithms on recordings from these devices was estimated.

关键词： Medical and Welfare systems Signal an/or image processing Electrocardiogram Noise Wavelet

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Efficiency Evaluation of Noise Based Camera Measurements algorithms

Efficiency Evaluation of Noise Based Camera Measurements Alg...

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2023 systems of Signals Generating and processing in the Field of on Board Communications, SOSG 2023

作者： Egorov, Dmitry Egorova, Anastasia Potashnikov, Aleksey Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798350397727

Nowadays digital cameras are being used in many practical applications. Therefore, problem of parameter estimation and quality evaluation becoming more and more significant. Recently computational photography trend become more notable and many devices started to use advanced algorithms to mask out weak characteristics of hardware. This makes traditional deterministic approaches of measurement less relevant. But random methods used these days have limitations too. This article compares measurement approaches and highlights limitation of each case and brings recommendation for measuring process creating foundation for future measuring method development. © 2023 IEEE.

关键词： image resolution

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Effectiveness of image processing and Different Machine Learning Models in Classifying Local Soybean Varieties in the Philippines 12

Effectiveness of Image Processing and Different Machine Lear...

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12th IEEE conference on systems, Process and Control, ICSPC 2024

作者： Dioses, Isaac Angelo M. Salum, Romero Dante Nayga, Desiray Asuncion, Mark Joseph Dioses, Jesusimo L. Castillo, Wendell M. MAPUA University School of Information Technology Makati Campus Metro Manila Makati Philippines Isabela State University College of Computing Studies Information and Communication Technology San Fabian Echague Isabela Philippines

ISBN: (纸本)9798350391398

Deceptive providers offering inferior products threaten the integrity of soybean exportation and product quality. To uphold fair trade standards and protect consumer rights, it is essential to classify and trace different soybean varieties accurately. Implementing stringent legislation and reliable technology for crop origin identification is crucial in maintaining order in the soybean trading market. This study uses artificial intelligence, specifically machine learning, image processing, and computer vision, to develop a standard model for soybean classification. Researchers collected two bags of soybeans from CVRC Cagayan at Ilagan Isabela, resulting in a dataset of 1562 soybean images, with 810 images of PSB SY2 and 752 images of CL1 Soy. The study employed Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) algorithms to create models for classifying and predicting soybean varieties. The SVM model achieved an accuracy of 88.2%, while the KNN model, using the FINE KNN preset, outperformed with an accuracy of 94.4%. Although the KNN algorithm exhibited slower training speed and time, it demonstrated superior accuracy over the SVM model, as evidenced by its confusion matrix. Feature selection using the MRMR algorithm identified perimeter and equivalent diameter as the most significant features for soybean classification, while minor axis length was deemed the least important. To improve the precision and dependability of soybean variety classification and safeguard customer interests, this research emphasizes the potential of machine learning approaches in conjunction with image processing and morphology feature extraction. © 2024 IEEE.

关键词： Support vector machines

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Evolution of real-time processing of visual information over four decades: a retrospective as outlook to the future of real-time imaging

Evolution of real-time processing of visual information over...

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Real-time processing of image, Depth and Video Information 2023

作者： Carlsohn, Matthias F. Engineering and Consultancy Dr. Carlsohn for Computer Vision and Image Communication Bremen Germany

ISBN: (数字)9781510662636

ISBN: (纸本)9781510662629

The expectations and demands for real-time performance for processing of visual information usually overloaded contemporary computer systems at all times. Increasing hardware performance enabled the use of more complex analysis and vice versa. For a long time, this led to a mutual fertilization of electronics and computer programs or architectures and algorithms with coherent data structures. Appropriate tool sets marked corner stones and boosted some significant developments. However, up to now one got always the impression that the requirements were always heading the possible performance and the open question becoming more serious is: where will it end? This paper provides a short rush through four decades of real-time image processing with a conclusion for an outlook into future opportunities and current developments providing guidelines to host corresponding research results by this conference in the coming years. New concepts, programming paradigms and production technologies are paving the way for future image processing solutions providing real-time capabilities. © 2023 SPIE.

关键词： Parallel architectures

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EgpuIP: An Embedded GPU Accelerated Library for image processing 24

EgpuIP: An Embedded GPU Accelerated Library for Image Proces...

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24th IEEE International conference on High Performance Computing and Communications, 8th IEEE International conference on Data Science and systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Luhan Jia, Haipeng Zhang, Yunquan Li, Kun Wei, Cunyang Institute of Computing Technology Chinese Academy of Sciences State Key Lab of Processors Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Microsoft Research China

ISBN: (纸本)9798350319934

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. The image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm;2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm;3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm;4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to 19×88×, and 3× speedup respectively. © 2022 IEEE.

关键词： Graphics processing unit

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