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

共 15616 条记录，以下是461-470 订阅

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Matrix Inversion Accelerated MCU Circuit for image Recognition Pertinent Computation

Matrix Inversion Accelerated MCU Circuit for Image Recogniti...

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2023 International Technical conference on Circuits/systems, Computers, and Communications, ITC-CSCC 2023

作者： Wang, Weisheng Jin, Yu Yuan, Quan Sun, Heming Beijing University of Chemical Technology Beijing China Waseda Research Institute for Science and Engineering Tokyo Japan

ISBN: (纸本)9798350326413

As the most complex computation in the fundamental matrix manipulation, the operation of matrix inversion limits the processing capability in the field of the image recognition and the pertinent application specified circuit. The intensive computation of matrix inversion is mitigated by efficient algorithms such as the Sherman-Morrison formula and accelerated floating-point multiplication and addition, while still suffering from division operations. Here, we present a high-performance floating-point divider circuit, which is capsuled as an instructor in to a general processor Cortex-M3 MCU to efficiently improve the matrix inversion speed in image recognition. The reconstructed MCU circuits are fully implemented on the Xilinx Spartan6 FPGA platform. Compared to the Xilinx floating point IP, the proposed architecture intensively reduces the division operation time by 82%. © 2023 IEEE.

关键词： image recognition

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Deep Learning Applications in Microscopy and Holography for near-field Signal processing 13

Deep Learning Applications in Microscopy and Holography for ...

引用

13th IEEE International conference on Communication systems and Network Technologies, CSNT 2024

作者： Mohseni, Milad Thilagham, K.T. Aravinda, K.A. Kumar, B Santhosh Nagpal, Amandeep Geetha, B.T. Islamic Azad University Department of Computer and Engineering Tabriz Branch Tabriz Iran Government College of Engineering. Salem Metallurgical Engineering Tamilnadu India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Saveetha School of Engineering Simats Department of Ece Tamilnadu India

ISBN: (纸本)9798350305463

Focusing on near-field signal processing, we provide a novel and comprehensive strategy for improving picture quality, obtaining super-resolution, and performing quantitative analysis in the disciplines of microscopy and holography. Three deep learning algorithms - Enhanced imageNet (EINet), HoloReconGAN, and QuantSegNet - are combined in this method to maximize their potential. In order to improve images, super-resolve them, and analyze them quantitatively, many algorithms have been developed. In order to better comprehend each method, mathematical equations are provided to describe the main steps involved. With the use of convolutional neural networks, noise is reduced and finer features are brought into focus with the help of the Enhanced imageNet (EINet) method. To accomplish super-resolution and 3D reconstruction from holographic data, HoloReconGAN combines generative adversarial networks (GANs) with variational autoencoders (VAEs). Label-free segmentation and quantitative analysis of structures in microscopy and holography pictures are the focus of QuantSegNet, a semantic segmentation network. Our suggested technique has been shown to outperform six established methods over a broad variety of assessment criteria. image quality, noise suppression, feature recognition, computational efficiency, and resilience are just few of the areas where it shines. It's also faster, uses less memory, is more accurate, easier to use, cheaper, and faster. Applications in several scientific fields might be facilitated, and the area of microscopy and holography as a whole could see significant advancements as a result. © 2024 IEEE.

关键词： image enhancement

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Research on Radar Imaging Method Based on Tensor Neural Network 16

Research on Radar Imaging Method Based on Tensor Neural Netw...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Qiu, Tian Liu, Nan Yang, Minglei National Key Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9781510689251

LFM signals, as the most commonly used form of signals in radar systems, are of significant research value. At this stage, compressed sensing (CS) has significant potential to improve the image quality of radar imaging. However, the existing CS theories have limited ability to improve the imaging capability of LFM signals, and always suffer from low imaging efficiency, parameters need to be manually adjusted, and poor suppression of side flaps. In this paper, we propose a neural network imaging algorithm based on tensor compressed perception, called 2D-ISTA-Net. this algorithm accomplishes distance-azimuth 2D radar imaging by combining a model-based iterative threshold shrinkage (ISTA) algorithm and a data-driven deep learning model. First, the sparse recovery model of the LFM signal is obtained analytically, and then, the 2D-ISTA optimization algorithm is unfolded into a deep network structure in which all parameters are optimized based on data-driven auto-learning instead of manual tuning. Subsequently, the network learning is trained on the scattering point images in the training set to obtain a 2D-ISTA-Net network with autonomous parameter tuning capability. Furthermore, we compared this algorithm with some existing CS algorithms. The data show that 2D-ISTA-Net significantly improves the computing power and has a lower side flap level compared with the traditional CS algorithm. Experiments show that 2D-ISTA-Net has good reconstruction performance. © 2025 SPIE.

关键词： Radar imaging

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image Based Security System in cloud resource Scheduling 5

Image Based Security System in cloud resource Scheduling

引用

5th International conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Chakraborty, Koushik Dora Pravina, C.T. Arya, Nancy Sharma, Sunil Rakesh, Shanu Kuttan Singh, Arvind Kunar Adamas University Kolkata India Department of Mathematics Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi India Department of Computer Science and Engineering Shree Guru Gobind Singh Tricentenary University Gurugram India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Department of Computer Science and Engineering Chouksey Engineering College Bilaspur Chhattisgarh India Department of Computer Science and Engineering S.V.N.I.E.T Barabanki India

ISBN: (纸本)9798400709418

The popularity of cloud computing in scheduling work processes, particularly logical work processes, is growing. The cloud computing environment may encounter significant issues with execution time and cost during resource allocation, resulting in disruptions in customer support quality. Text Passwords are right now a totally utilized insistence part, however graphical passwords and biometrics are likewise utilized. However, we really want to improve both comfort and security in any system to make it extremely secure. image-based security systems leverage visual information and image processing techniques to enhance security measures. These systems use images or video streams from cameras to monitor, detect, and respond to security threats. Most likely, the framework employs our algorithm to improve performance. It utilizes parts of both Graphical and Text Secret phrase plans. Advanced image processing algorithms analyze captured images for various security-related purposes, such as object detection, facial recognition, and anomaly detection. In contrast to the most well-known scheduling algorithms, such as multi-objective scheduling (MOS) and cost-conscious scheduling, the various scheduling algorithms in this proposed hybrid algorithm mannequin are used to make cloud services secure and highly available to end users. Identifying and verifying individuals based on facial features. This is used for access control, identity verification, and tracking individuals of interest. Analyzing images to detect unauthorized persons or objects in restricted areas, triggering alerts or alarms. In this investigation, we attempt to demonstrate the execution of various algorithm systems for the most well-known three static booking calculations. All the calculations are working out the all-out execution cost and timing of various calculations. © 2023 ACM.

关键词： Security systems

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Implementation of an Efficient image Inpainting Algorithm using Optimization Techniques 6th

Implementation of an Efficient Image Inpainting Algorithm us...

引用

6th International conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Revathi, K. Janardhana Rao, B. Odugu, Venkata Krishna Gade, Harish Babu Sphoorthy Engineering College Hyderabad India CVR College of Engineering Hyderabad India

ISBN: (纸本)9789819984503

To repair the demolished images and remove the particular unnecessary objects in the image, optimized image inpainting techniques are required. In this work, a novel exemplar-based image inpainting technique is suggested. In this technique, the patch priority is computed using the regulation factor and coefficients. Two optimization techniques as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques are employed to attain the optimal values of regularization factor and coefficients. The best exemplar patch selection is carried out by calculating the sum of the absolute difference between the patches. Performance measures including peak-signal-to-noise ratio (PSNR), mean square error (MSE), and Structural Similarity Index (SSIM) are tested using the suggested image inpainting process on images based on datasets. These results are compared with the available inpainting methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic algorithms

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image Reconstruction Leveraging Time-Event Feature Fusion for Event Cameras 7

Image Reconstruction Leveraging Time-Event Feature Fusion fo...

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7th International conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Jian Wu, Sheng Liu, Wenwei Feng, Hui Hu, Bo School of Information Science and Technology Fudan University Shanghai200433 China Shanghai Institute of Intelligent Electronics & Systems Shanghai200433 China

ISBN: (纸本)9798350350890

The event cameras generate asynchronous event sequences, rendering most existing image-based algorithms in-applicable for direct use and processing. Therefore, the development of a simulator that utilizes event streams captured by an event camera as input and reconstructs image sequences as output is crucial for subsequent tasks such as high-speed moving object tracking, object recognition and detection, and SLAM. Despite the extensive research and attention garnered by deep learning-based image sequence generation, there remains a problem of local representation information loss during the reconstruction process from event streams to image sequences. Existing solutions, whether based on Stacking Based on Time (SBT) or Stacking Based on Events (SBE), exhibit significant limitations when using a single stacking approach. To address the insufficiency in feature representation capability, this paper proposes a visual image conversion method based on multi-view feature fusion, effectively transforming event streams into traditional image sequences. Extensive experiments and dataset validations, utilizing multiple evaluation metrics including both global and local assessments, demonstrate the advantages of our proposed method. © 2024 IEEE.

关键词： image reconstruction

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Deep Learning-Based Noise Reduction Techniques in Electronic Signal processing 9

Deep Learning-Based Noise Reduction Techniques in Electronic...

引用

9th International conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Johari, Lalit Ramya, P. Suganya, M. Praveena, Hirald Dwaraka Lathamageshwari, P.S. Rajendiran, M. Iftm University Department of Computer Applications Uttar Pradesh Moradabad244102 India Bannari Amman Institute of Technology Department of Electronics And Communication Engineering Tamil Nadu Sathyamangalam638401 India Annapoorana Engineering College Department of Electronics And Communication Engineering Tamil Nadu Salem636308 India Department of Electronics And Communication Engineering Andhra Pradesh Tirupati517102 India Tamil Nadu Kavaraipettai601206 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

This paper explores the operation of deep literacy- grounded noise reduction ways in electronic signal processing. Noise hindrance poses a significant challenge in colorful signal processing operations, impacting the delicacy and trustability of data analysis and interpretation. Traditional noise reduction styles frequently calculate on heuristic approaches or signal processing algorithms, which may have limited effectiveness, especially in complex and dynamic surroundings. In discrepancy, deep literacy algorithms, similar as convolutional neural networks(CNNs) and intermittent neural networks(RNNs), offer promising results for noise reduction by learning complex patterns and connections directly from data. This paper determines the state- of- the- art deep literacy- grounded noise reduction ways, including denoising autoencoders, generative inimical networks(GANs), and intermittent neural network- grounded models. Case studies and experimental results demonstrate the effectiveness and performance of these ways in colorful signal processing tasks, similar as audio denoising, image restoration, and biomedical signal processing. The integration of deep literacy- grounded noise reduction styles into electronic signal processing systems shows significant eventuality for perfecting signal quality and enhancing the delicacy of data analysis in different operations. © 2024 IEEE.

关键词： Recurrent neural networks

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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...

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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...

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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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Analysis of the Noise in ECG Recordings on Different Devices 62

Analysis of the Noise in ECG Recordings on Different Devices

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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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