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

共 16440 条记录，以下是2041-2050 订阅

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Automatic Number Plate Recognition System Using Deep Learning algorithms and image processing for Surveillance

Automatic Number Plate Recognition System Using Deep Learnin...

引用

International conference on Science Technology Engineering and Management (ICONSTEM)

作者： A. Christy Jeba Malar Deepika S Jayasree S Keerthana G Department of Information Technology Sri Krishna College of Technology Coimbatore India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The increase in amount of vehicles in the past few years have made traffic management a difficult job. Technologies play an important role in these systems to regulate the traffic. Number plates are distinguished by their shape, bulk, and color in different countries. Number plates must adhere to rules in India, where a light hue serves as the background and a black color serves as the foreground. This paper recommended a new technique to locate the number plate on a vehicle by using histogram equalization and then identifying the alphabets and digits on the plates using character segmentation using Sobel and Canny edge detection image processing technique.

关键词： Deep learning image segmentation Histograms image color analysis Shape Surveillance image edge detection

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GAN-based image deblurring: A comparison

GAN-based image deblurring: A comparison

引用

Electrical Engineering, Big Data and algorithms (EEBDA), IEEE International conference on

作者： Qi Feng Liyan Jiang Yuyang Ji Ruihan Zhang University of Wisconsin-Madison Madison United States UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai China New York University New York United States Xi’an Jiaotong-Liverpool University Suzhou China

In recent years, image deblurring algorithms based on Generative Adversarial Network (GAN) have been proposed. The motion deblurring has been studied for some time. The multi-scale architecture of convolutional neural networks (CNN) can clearly recover blurred images, but the processing time is long. Generative Adversarial Networks (GAN) performs well in style conversion and can clearly recover blurred images under a non-multi-scale architecture. The authors reviewed the popular papers related to DeblurGAN, compared the algorithm structure of different papers, related data sets and the accuracy of experimental results, which helped GAN based image deblur field to summarize and summarize, and enabled researchers to better promote the development of related fields. The authors review six papers, introduce their research motivation, research methods and experimental results, and show the differences of different deblur algorithms. Some excellent algorithms can work flexibly with a wide backbone, balancing performance and efficiency. They run faster than simple DeblurGAN while maintaining near-state of the art results.

关键词： Machine learning algorithms image synthesis image edge detection Heuristic algorithms Generative adversarial networks Inference algorithms image restoration

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A Survey on Automated Disease Diagnosis and Classification of Herbal Plants Using Digital image processing

A Survey on Automated Disease Diagnosis and Classification o...

引用

International conference on Information systems and Computer Networks (ISCON)

作者： Ankit Khandelwal Aasheesh Shukla Mangal Sain Electronics & Communication Dept. GLA University Mathura India Information Engineering Dept. Dongseo University Busan Republic of Korea

This paper presents a review on automated disease detection processing. The primary issue in herbal plants is the diagnosis and stratification of its diseases. The conventional process is unpredictable and inconsistent, therefore techniques for classifying plants diseases using digital method are necessary as they speed up the identification process. This literature discusses various digital methods for classifying plant diseases. The first step is image processing of herbal plants, which make use of traditional image processing techniques and algorithms to categories diseases in herbal plants based on attributes like texture, color, and shape. The second step is plant categorization based on deep learning and machine learning, such as CNN, KNN, logistic regression etc. Deep learning applications mainly lessen the reliance on designs and preprocessing techniques by joining the entire process. This review provides a basic understanding and knowledge of the current state-of-the-art researches, as well as some dynamic trends for future work.

关键词： Deep learning Plant diseases Machine learning algorithms Shape image color analysis Digital images Neurons

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Malware Combination Detection in Industrial Control Terminal Collaborative Response System Using Multiple algorithms Based on Deep Learning

Malware Combination Detection in Industrial Control Terminal...

引用

Communication, image and Signal processing (CCISP), International conference on

作者： Bo Hu Weiyong Yang Jian Zhou Jianye Yu Zi Wang xiaoming Zhou State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang China NARI Group Corporation/State Grid Electric Power Research Institute Nanjing NARI Information and Communication Technology Co Ltd. Nanjing China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

Because of its relevance and complexity in industry, the industrial control terminal collaborative response system is popular among app developers and users, as well as the main target of malevolent criminals. The rapid spread of malware has caused significant harm to users of the industrial control terminal collaborative response system. In this study, we build a model by combining dynamic and static feature detection, compiling test software output information using Fuzz technology, and training with a method that combines dynamic and static interest-based expert recommendation model (DSIERM) algorithm with Bi-LSTM algorithm. Modeling enables specific analysis and classification of test results. Based on this, the TaintBench suite and framework are introduced to compensate for the lack of relevant content for static taint analysis of applications in industrial control terminal collaborative response systems and to increase detection efficiency.

关键词： Deep learning Analytical models Heuristic algorithms Industrial control Software algorithms Signal processing algorithms Collaboration Static analysis Feature extraction Malware

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UAV image Denoising based on Swin Deformer for Power System Condition Monitoring

UAV Image Denoising based on Swin Deformer for Power System ...

引用

6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Dong Wang Linwei Ye Department of Mechanics and Robotics Systems Xi’an Jiaotong-Liverpool University Suzhou China Department of Information Management and Information System Xi’an Jiaotong-Liverpool University Suzhou China

In order to improve the quality of UAV transmission line inspection pictures and ensure the effectiveness of the defect recognition network, this paper proposes a model based on Swin Deformer to denoise the pictures. The data set of this method is the original data set collected by UAV, and the test set and training set are obtained by classifying the raw data. At the same time, Gaussian noise is added to generate tags to ensure the effect of supervised learning. Finally, the test set is input into the pre-training model, and the results obtained prove that our noise reduction network can greatly improve the image quality and further improve the effect of line maintenance.

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Automatic target detection utilizing an Edge IR vision transformer (EIR-ViT) 33

Automatic target detection utilizing an Edge IR vision trans...

引用

conference on Automatic Target Recognition XXxiII

作者： Adams, Ethan R. Depoian, Arthur C., II Kurz, Aidan G. Bailey, Colleen P. Guturu, Parthasarathy Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510661561;9781510661578

The detection and recognition of targets within imagery and video analysis is vital for military and commercial applications. The development of infrared sensor devices for tactical aviation systems imagery has increased the performance of target detection. Due to the advancements of infrared sensors capabilities, their use for field operations such as visual operations (visops) or reconnaissance missions that take place in a variety of operational environments have become paramount. Many techniques implemented stretch back to 1970, but were limited due to computational power. The AI industry has recently been able to bridge the gap between traditional signal processing tools and machine learning. Current state of the art target detection and recognition algorithms are too bloated to be applied for on ground or aerial mission reconnaissance. Therefore, this paper proposes Edge IR Vision Transformer (EIR-ViT), a novel algorithm for automatic target detection utilizing infrared images that is lightweight and operates on the edge for easier deployability.

关键词： Automatic Target Detection Vision Transformer Infrared Imaging Object Detection FLIR image FLIR dataset Edge Computing

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Design of Neural Network Accelerator Based on In-Memory Computing Theory 4

Design of Neural Network Accelerator Based on In-Memory Comp...

引用

4th International conference on Natural Language processing, ICNLP 2022

作者： Wu, Jin Zhao, Bo Wen, Heng Zhao, Qianchao Xi'an University of Posts and Telecommunication Dept. School of Electronic Engineering Xi'an China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

With the continuous deepening of neural network application research, the amount of data has grown rapidly. Although most of the calculations related to the field of machine learning are still completed under the traditional processor architecture, for embedded systems using neural networks, traditional Processors have struggled to meet the requirements of big data processing. This paper proposes to use the in-memory computing architecture as a new type of processor architecture to solve the problem of additional energy consumption caused by the 'memory wall' problem under the von Neumann architecture, so as to achieve a better acceleration effect on neural network algorithms. This paper is based on three types of memory, including Static Random-Access Memory (SRAM), Resistive Random-Access Memory (ReRAM), and Ferro-electric Field Effect Transistor (FeFET). Related research has been done on in-memory computing neural network accelerator architectures. Mainly use VGG-8, VGG-16, AlexNet and other networks to complete the verification of the in-memory computing architecture based on SRAM, ReRAM, FeFET three kinds of memories on three different data sets, from the acceleration effect of each layer and the use of each layer The performance of the designed accelerator is evaluated by multiple indicators such as the number of Tile units, read and write delay, read and write energy consumption, system occupied area, and energy efficiency ratio. The experimental results show that the neural network accelerator based on in-memory computing designed in this paper has reached a relatively advanced level. © 2022 IEEE.

关键词： Energy efficiency

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Autocalibration for MEMS triaxial accelerometer based on a PID-based search algorithm

Autocalibration for MEMS triaxial accelerometer based on a P...

引用

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

作者： Fei Li Deming Wang Nan Zhang Ruixi Zheng Yuxin Zhu School of Electronic Engineering Xi’an Shiyou University Xi’an China College of Petroleum Engineering Xi’an Shiyou University Xi’an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

The errors can accumulate over time when microelectromechanical system (MEMS) accelerometers are in use, it is necessary to perform autocalibration to remove the systematic error effect. However, little research has been done on simultaneously considering the non-orthogonality and misalignment effect in MEMS triaxial accelerometer calibration. To enhance the calibration accuracy and extend the autocalibration methods, this paper proposed a new autocalibration method for MEMS triaxial accelerometers base on a PID-based search algorithm. Non-orthogonality and misalignment error factors were integrated into the error model and a PID-based search algorithm was developed to identify the optimal error factors, minimizing the difference between the actual accelerometer output and the local gravity. The proposed calibration method was tested through simulations, with results showing that the errors in scale factor, bias, non-orthogonality, and misalignment are within 1.5865×10 -4 , 2.0060×10 -4 , 5.0621×10 -4 , and 6.8104×10 -4 , respectively. The proposed method was also compared with the existing calibration methods, with the maximum errors of the proposed method within 0.41% and those of the others exceeding 0.80%. Simulation and comparison results verified the effectiveness and improvement of the proposed method. The average time of the autocalibration is 13.22 s, offering practical value for MEMS triaxial accelerometers.

关键词： Accelerometers Micromechanical devices Microelectromechanical systems Accuracy Systematics Signal processing algorithms Signal processing Calibration Intelligent control Gravity

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Enhanced DSP Blocks for Full High-Definition Video Bridging Application on FPGAs

Enhanced DSP Blocks for Full High-Definition Video Bridging ...

引用

International conference on Communications, Circuits and systems (ICCCAS)

作者： Panfeng Wang Yici Cai Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350386271

ISBN: (纸本)9798350386288

FPGA chips are widely used in video bridging applications. The DSP (Digital Signal processing) in FPGA can effectively improve the efficiency of video image processing algorithms. The 10 bits of pixel depth is common for Full High-Definition (FHD). However, in the existing commercial FPGAs, the DSP does not naturally support 10x10 multiplication. This brings some waste in area and power consumption. This paper proposes a new architecture of embedded DSP. It increases the maximum number of 10x10 multiplications in a DSP block to 8. It is also compatible with common working modes such as multiplication (18x18, 18x9), and accumulation. Meanwhile, introduce power gate and clock gate units in the DSP block to achieve fine-grained control of the power consumption. Users can dynamically control the power supply through their own logic. The entire circuit was successfully designed and implemented in an industrial 22nm process. The experimental results show that the static power consumption of the DSP block is about 20uW, the dynamic power consumption is about 200uW. And the maximum operating frequency can reach 670MHz. The effective area utilization of our DSP in 10x10 mode is 2.2 and 2.5 times higher than that in Intel Arria 10 series and xilinx UltraScale series FPGAs, respectively.

关键词： Power demand Circuits and systems image processing Signal processing algorithms Digital signal processing Logic gates Logic

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Smart Oral Health Monitor

Smart Oral Health Monitor

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International conference

作者： Spandana Anilkumar Kamkar Srushti Sanjay Vaishnavi P S Karthik S Dept. of Computer Science and Engineering BNM Institute of Technology Affiliated to VTU Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder.

关键词： Deep learning image processing Teeth Cameras Real-time systems Mobile applications Monitoring Diseases

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