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

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image processing Algorithm Design for Low-Light EBCMOS Devices Based on FPGA

Image Processing Algorithm Design for Low-Light EBCMOS Devic...

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Electronic Information Engineering and Computer Communication (EIECC), International conference on

作者： Yi Cao Na Wei xiangping Zhu Jun Ma State Key Laboratory of Transient Optics and Photonics Institute of Optics and Precision Mechanics University of Chinese Academy of Sciences Xi'an China Xi'an Institute of Atomic Precision Manufacturing Technology Co. Ltd. Xi'an China

In this study, a hardware platform based on the xilinx 7-series Field-Programmable Gate Array (FPGA) is employed. The design of a Static Storage Control Module and a Median Filtering Module is accomplished through programming in the Verilog hardware description language using Vivado software. The functionality of these modules is rigorously verified through logical validation using Modelsim software. The algorithm is applied to process low-light images captured by a custom EBCMOS image sensor. Comparative analysis with software testing platforms reveals a substantial improvement in image processing speed when utilizing the FPGA-based hardware resources. This improvement allows the processing of 1 pixel per clock cycle. As a result, this work lays the foundation for the design of lightweight, integrated data acquisition systems for the nextgeneration night vision helmet systems.

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Research and analysis of image enhancement algorithm in the classification of rock thin section images 3

Research and analysis of image enhancement algorithm in the ...

引用

3rd International conference on Intelligent Control, Measurement and Signal processing and Intelligent Oil Field, ICMSP 2021

作者： Huaizhou, Yang Danyang, Xu School of Computer Science Xi'An Shiyou University Xi'an China

ISBN: (纸本)9781665437158

The structure of rock flakes is complex and difficult to be classified accurately. The proposed method to solve the problem is to use an image enhancement algorithm to enhance the rock slice image. In the study, the neural network ResNet50, which has a significant effect on fine-grained classification, was used to construct the rock cast thin section image classifier, and three image enhancement algorithms, CutOut, MixUp, and CutMix, were used to enhance the rock thin section image. The rock slice images used in the data set are from Ordos, and are divided into five categories according to the size of the rock. The experimental result obtained was that the CutOut algorithm performs well on the data set, and the accuracy of the classifier was as high as 93.39%, which is 1.3% higher than the result of only using ResNet50 for classification. The experimental results show the effectiveness of the image enhancement algorithm in the classification of rock slice images. © 2021 IEEE.

关键词： image classification

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Qiang Liu Mahdi Soleymani Hessam Mahdavifar National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements.

关键词： image coding Signal processing algorithms Signal processing Acoustics Sensors Decoding Sparse matrices Speech processing Compressed sensing

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image Enhancement Algorithm based on Local Contrast for Convolutional Neural Network-Based Infrared Target Recognition: ∗Note: Subtitles are not captured in Xplore and should not be used 24

Image Enhancement Algorithm based on Local Contrast for Conv...

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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： Nian, BingKun Ma, LiYuan Zhang, Yan Zhang, Yi Shi, HuaJun The 32nd Institute China Electricity Science and Technology China College of Science National University of Defense Technology China College of Electronic Science National University of Defense Technology China

ISBN: (纸本)9798350319934

In the task of infrared weak and small target recognition, in order to improve the image quality and solve the problem of poor learning ability of convolutional neural network (CNN) due to the imbalance of positive and negative samples, this study proposes a three-stage image enhancement algorithm named adaptive filter for quality enhancement based on local contrast (AFQELC). Firstly, AFQELC analyzes the statistical characteristics of the image and constructs a local contrast adaptive filter (LCAF) to enhance the detailed information of the image, which promotes the deep learning model to learn low-level semantic information. Secondly, principal component analysis (PCA) fusion combines information from the original image and the image enhanced by LCAF to reduce noise. Finally, the gradient component of the original image is extracted to further revise the processing result, which promotes the deep learning model to learn advanced semantic information. The proposed non-data-driven algorithm has a clear and interpretable process, which is superior to other traditional and neural network based image enhancement algorithms. The experiment results show that the quality of images enhanced by AFQELC is improved significantly. In addition, AFQELC can improve the recognition and positioning accuracy of the CNN-based algorithm for infrared target recognition by alleviating the imbalance of positive and negative samples. © 2022 IEEE.

关键词： image enhancement

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Target Estimation with Angle-Only Observations of Distributed Passive Radar Networks

Target Estimation with Angle-Only Observations of Distribute...

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Signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Fabing Dou Linhai Wang Shenghua Zhou Mengchen Zhang Yidi Chen National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Academy of Space Information Systems Xi’an China China Academy of Launch Vehicle Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Passive radars that rely on angle-only measurements cannot obtain the distance between radars and targets. To obtain position estimates of targets, passive radars can be connected to a central processor through communication links. Therefore, fusion algorithms are required to combine observations from distributed passive radars. For the scenario where all radars and targets are moving, most existed fusion algorithms use filtering methods to estimate the velocity and position of moving targets. In fact, most filtering algorithms have some limitations which will limit the algorithm’s effectiveness to a large extent. In this paper, two fusion algorithms are proposed for the central processor. The first algorithm, known as the gross least squares (GLS), employs all observations from passive radars to implement moving target estimation, and thus needs significant computation at the central processor. The second algorithm, termed as the recursive least squares (RLS), iteratively optimizes the velocity and position of moving target. Exploiting the continuity of target motion, current target position and velocity estimates are used as initial values in the next moment. With good initial values, the number of iterations decreases and thus the computational cost is reduced at the central processor. Numerical results show that both algorithms benefit from more observations. The GLS algorithm provides more accurate position and velocity estimates of the target but requires substantial computation cost. The RLS algorithm may suffer a slight performance loss but offers a lower computation cost. The results demonstrate the trade-off between accuracy and computational cost, providing useful insights for practical applications.

关键词： Passive radar Costs Accuracy Radar measurements Filtering Estimation Filtering algorithms Data processing Computational efficiency

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A Secure Storage System for Financial Data based on Fuzzy Recognition algorithms

A Secure Storage System for Financial Data based on Fuzzy Re...

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

作者： xi Yu Ferdinand Lapus Timbang College of Finance and Economics Guangdong University of Science and Technology Dongguan China ChanRobles Professional Review Inc Manila Philippine

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

This electronic document is a "live" template and already defines the coThe purpose of this paper is to develop a secure storage system for financial data based on fuzzy recognition algorithm to improve the security, accuracy and reliability of financial data and its applications. The system adopts the fuzzy C-means clustering algorithm to carry out intelligent analysis and processing of financial data. This paper begins to carry out specific research on the part of algorithm selection and algorithm implementation, and elaborates on the whole process, so as to explain the algorithm clearly and provide support for its subsequent application. The system test results show that the data encryption method and strict access control mechanism are used to ensure the security of data and the system has excellent performance in terms of data storage performance, encryption and decryption speed, API response time and system availability, throughput and error rate, etc., which can effectively meet the needs of safe, reliable and efficient financial data storage and management.

关键词： Cloud computing Data security Buffer storage Clustering algorithms Memory Data processing Encryption Reliability Time factors Secure storage

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Integrated Platform of Underwater Acoustic Signal Simulation and Classification

Integrated Platform of Underwater Acoustic Signal Simulation...

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International conference on Advanced Computer Theory and Engineering, ICACTE

作者： Haoliang Zhu Tiantian Guo Limin Yu dept. School of Advanced Technology Xi’ an Jiaotong Liverpool University Suzhou China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

With the increase in marine activities, there is a growing need for efficient and accurate underwater acoustic signal processing techniques. In this study, MATLAB-based graphical user interfaces (GUIs) including Receiver Signal Simulation GUI, Database System GUI and Signal Classification GUI are developed for simulating, sorting, classifying and visualizing underwater acoustic signals with the aim of improving the efficiency and accuracy of acoustic signal processing. By converting one-dimensional acoustic signals into two-dimensional time-frequency spectrogram, this study applies image classification techniques in underwater acoustic signal processing using convolutional neural networks (CNNs), deep neural networks (DNNs), and random forests (RFs) for signal classification. Experimental results show that these machine learning models are able to effectively classify acoustic signals in a low signal-to-noise ratio environment, with CNN and RF showing particular robustness. In addition, the GUI provides a user-friendly platform that greatly simplifies the complex process of analyzing acoustic data. This study not only demonstrates effectiveness of the algorithms in classifying underwater acoustic signals, but also provides new tools and perspectives for marine science research and related application areas. The future work will explore the integration of more machine learning algorithms and extend the functionality of the GUI to support a wider range of acoustic signal processing applications.

关键词： Radio frequency Machine learning algorithms Accuracy Pattern classification Signal processing algorithms Convolutional neural networks Underwater acoustics Graphical user interfaces Signal to noise ratio Sorting

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image Compression Algorithm Based on Region of Interest Extraction for Unmanned Aerial Vehicles Communication

Image Compression Algorithm Based on Region of Interest Extr...

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IEEE Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Yanxia Liang Tong Jia xin Liu Huanhuan Zhang Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’an China College of Information Engineering Xi’an Eurasia University Xi’an China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

The flexibility and maneuverability of unmanned aerial vehicles (UAVs) have led to their widespread use, but the challenge of limited storage space and bandwidth in emergency scenarios necessitates efficient image compression techniques. In this research, we propose an image compression algorithm tailored for UAV communication, termed ROIE-UC (Region of Interest Extraction for UAV Communication). Firstly, image pixels are clustered into super pixel blocks using the Simple Linear Iterative Clustering (SLIC). Secondly, these super pixels are grouped into regions of interest (ROI) using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The image is then segmented into ROI and non-ROI areas based on these clusters. Lossless compression is applied to the ROI, while lossy compression with a high ratio is used for non-ROI regions. At the receiving end, the image is decompressed and reconstructed. Experimental results show that the ROIE-UC achieves a Peak Signal-to-Noise Ratio (PSNR) of 46.37 dB and a Structural Similarity Index (SSIM) of 99.94% for the ROI part, with compression ratios of 27, 25, and 14 for the whole image. Compared to the JPEG, PSNR improved by up to 28.52% and SSIM by 1.51%, with a compression ratio increase from 14.52 to 27.56. The ROIE-UC also provides up to a 49.44% increase in PSNR over other methods. These results demonstrate that ROIE-UC is an efficient and effective solution for image processing and data compression in UAV applications.

关键词： image segmentation Visualization image coding PSNR Wireless networks Bandwidth Autonomous aerial vehicles Real-time systems image reconstruction Videos

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An improved algorithm for regional cattle population counting under local optimal methods

An improved algorithm for regional cattle population countin...

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Intelligent Control, Measurement and Signal processing and Intelligent Oil Field (ICMSP), International conference on

作者： Bingxuan Li Jiandong Fang Yudong Zhao College of Information Engineering Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Perceptive Technology and Intelligent Systems Hohhot China Inner Mongolia Autonomous Region Intelligent Agriculture and Animal Husbandry Perceptual Technology Collaborative Innovation Center Hohhot China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

As a movable collateral, live cattle need to be accurately counted and recorded on a regular basis, such as their breeds, numbers and other important information. Aiming at the problems that the existing cattle number detection algorithms have leakage detection phenomenon for dense and small targets and have many parameters and large computation volume, which are not suitable to be deployed at the end of embedded devices, a CBG-YOLOv5s cattle number detection algorithm is proposed. The experimental results show that the improved model reduces the number of parameters and computation by 37.32% and 43.04%, respectively, compared with the original model YOLOv5s, and still maintains good detection accuracy. Finally, the lightened model was successfully deployed to Nvidia Jetson Nano mobile and verified by actually running on the development board. The model in this paper is able to ensure the accuracy of cattle identification, which can provide a reference for the accuracy of detecting the number of cattle in complex farming environments and under severe occlusion conditions.

关键词： YOLO Accuracy Target tracking Computational modeling Signal processing algorithms Cows Signal processing Real-time systems Object recognition Detection algorithms

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Deep Learning-Based Improved Automatic Building Extraction from Open-Source High Resolution Unmanned Aerial Vehicle (UAV) imagery 1

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2nd International conference on Unmanned Aerial systems in Geomatics (UASG)

作者： Maniyar, Chintan B. Kumar, Minakshi Indian Inst Remote Sensing Photogrammetry & Remote Sensing Dept Dehra Dun 248001 Uttarakhand India

ISBN: (数字)9783031193095

ISBN: (纸本)9783031193118;9783031193095;9783031193088

Automatically extracting buildings from remotely sensed imagery has always been a challenging task, given the spectral homogeneity of buildings with non-building features as well as the complex structural diversity within the image. Traditional machine learning (ML) based methods deeply rely on a huge number of samples and are best suited for medium-resolution images. Unmanned aerial vehicle (UAV) imagery offers the distinct advantage of very high spatial resolution, which is helpful in improving building extraction by characterizing patterns and structures. However, with increased finer details, the number of images also increases many folds in a UAV dataset, which require robust processing algorithms. Deep learning algorithms, specifically Fully Convolutional Networks (FCNs) have greatly improved the results of building extraction from such high resolution remotely sensed imagery, as compared to traditional methods. This study proposes a deep learning-based segmentation approach to extract buildings by transferring the learning of a deep Residual Network (ResNet) to the segmentation-based FCN U-Net. This combined dense architecture of ResNet and U-Net (Res-U-Net) is trained and tested for building extraction on the open source Inria Aerial image Labelling (IAIL) dataset. This dataset contains 360 orthorectified images with a tile size of 1500 m(2) each, at 30 cm spatial resolution with red, green and blue bands;while covering total area of 805 km(2) in select US and Austrian cities. Quantitative assessments show that the proposed methodology outperforms the current deep learning-based building extraction methods. When compared with a singular U-Net model for building extraction for the IAIL dataset, the proposed Res-U-Net model improves the overall accuracy from 92.85% to 96.5%, the mean F1-score from 0.83 to 0.88 and the mean IoU metric from 0.71 to 0.80. Results show that such a combination of two deep learning architectures greatly improves the building extract

关键词： Transfer learning Fully convolutional networks image segmentation Building extraction

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