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

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A Comparative Study of the Performance of Personnel Detection based on Visible and Infrared images 4

A Comparative Study of the Performance of Personnel Detectio...

引用

4th International conference on Video, Signal and image processing, VSIP 2022

作者： Liu, Futao Li, Zhenling Wan, Yuezhong Cao, Weida Ma, Duanwei Li, Xiaolei Yan, Peng Liaocheng Power Supply Company State Grid Corporation of China China Shandong University School of Control Science and Engineering China Shandong TCCA Information Technology Research Institute China

ISBN: (纸本)9781450397810

Personnel security has always been a hot topic of research especially in power systems. Effective detection of people in power scenes has become a high priority. Currently, algorithms for personnel detection have been developed considerably, and they are mainly used for visible images. Since infrared images imaged by thermal radiation can detect living blood creatures easily, they can be used in personnel detection at night. To investigate the effect of visible and infrared images on the performance of personnel detection algorithms in different environments, this thesis uses two object detection algorithms, YOLOv5 and Faster RCNN, to train personnel detection models for visible daytime, visible nighttime, infrared daytime and infrared nighttime datasets respectively. Comparative experiments are conducted to quantitatively and qualitatively analyze the performance advantages and disadvantages of the personnel detection algorithms for visible images and infrared images during the day and night. The experimental results for both models consistently show that the visible images are similar to the infrared images during daylight hours with a good target detection model, the visible image is slightly better in performance. While the infrared image is significantly better than the visible image at night, and the mAP@0.5 can be improved by 0.2. For small human target detection, the infrared image has a significant advantage over the visible image at night, with an average detection size reduction of 48.0% and a minimum detection size reduction of 64.6%. © 2022 ACM.

关键词： image enhancement

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DOMAIN ADAPTATION IN image DEHAZING: EXPLORING THE USAGE OF imageS FROM VIRTUAL SCENARIOS 16

DOMAIN ADAPTATION IN IMAGE DEHAZING: EXPLORING THE USAGE OF ...

引用

16th International conference on Computer Graphics, Visualization, Computer Vision and image processing, CGVCVIP 2022, 8th International conference on Connected Smart Cities, CSC 2022, 7th International conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information systems, MCCSIS 2022

作者： Sappa, Angel D. Suárez, Patricia L. Velesaca, Henry O. Carpio, Darío Escuela Superior Politécnica del Litoral ESPOL Campus Gustavo Galindo Km. 30.5 P.O. Box 09 Vía Perimetral 01-5863 Ecuador Computer Vision Center Campus UAB Bellaterra Barcelona08193 Spain

ISBN: (纸本)9789898704429

This work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image haze removal problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator;these synthetic images contain different densities of haze, which are used for training the model that is later adapted to any real scenario. The adaptation process requires just a few images to fine-tune the model parameters. The proposed strategy allows overcoming the limitation of training a given model with few images. In other words, the proposed strategy implements the adaptation of a haze removal model trained with synthetic images to real scenarios. It should be noticed that it is quite difficult, if not impossible, to have large sets of pairs of real-world images (with and without haze) to train in a supervised way haze removal algorithms. Experimental results are provided showing the validity of the proposed domain adaptation strategy. © MCCSIS *** rights reserved.

关键词： Deep learning

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FPGA Based image processing: A Comparative Study of Software-Centric and Hardware-Accelerated Approaches

FPGA Based Image Processing: A Comparative Study of Software...

引用

Smart Electronics and Communication systems (ISENSE), International conference on

作者： P N Parameswaran S Janardan Siddharth R Nair Thejaswitha T K Nikhil M Adersh V R Dept of Electronics and Communication College of Engineering Trivandrum Thiruvananthapuram India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

This work compares two different approaches to image processing algorithm implementation in Zynq Zybo and Zedboard Field Programmable Gate Array (FPGA) boards. There are three main phases for the study, namely, Hardware-Accelerated image processing (HAIP), Software-Centric image processing (SCIP), and an in-depth analysis of their corresponding performance metrics. image processing algorithms were implemented in the SCIP phase, exclusively using the Zynq processing System (PS). On the other hand, the HAIP phase comprised creating distinct Intellectual Properties (IPs) for image processing tasks and integrating them with the Zynq PS through Direct Memory Access (DMA) and the Advanced eXtensible Interface (AXI) *** all assessed techniques, HAIP considerably outperformed SCIP in terms of processing time, for different image sizes, across both Zynq Zybo and Zedboard. On the other hand, as SCIP is software-centric, it showed minimal resource utilization and lower power consumption than HAIP, which showed higher resource utilization and power consumption. However, both methods produced processed images that were similar, highlighting the stability and dependability of FPGA-based image processing implementations. A comparative analysis between Zynq Zybo and Zedboard reveals insights into how different FPGA architectures impact the performance and resource utilization of image processing implementations. This work offers insights into the trade-offs between hardware-accelerated and software-centric approaches to image processing on FPGA platforms, guiding future design decisions for practical uses.

关键词： Hands Measurement Power demand image processing Intellectual property Logic gates Stability analysis Robustness Resource management Field programmable gate arrays

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Vision-based real-time molten pool monitoring for Electron Beam Freeform Fabrication

Vision-based real-time molten pool monitoring for Electron B...

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2023 IEEE International conference on Real-Time Computing and Robotics, RCAR 2023

作者： Liao, Zhenyu Liang, Zhiyue Zhang, Haoyu Li, Zixiang Wang, Li Du, Dong Tsinghua University Department of Mechanical Engineering Beijing100084 China Tsinghua University Key Laboratory for Advanced Materials Processing Technology Ministry of Education Beijing100084 China

ISBN: (纸本)9798350327182

In the additive manufacturing process, the molten pool contains abundant information related to the deposition quality and stability. Real-time monitoring and feature extraction of the molten pool is of great significance for closed-loop control of molten pool and guarantee of consistency and stability of the deposition process. In this study, we develop a visual monitoring system of molten pool for electron beam freeform fabrication(EBF3) and propose corresponding digital image processing algorithms to extract molten pool geometric and identify the wire position and metal transfer state of the deposition. The effectiveness of the proposed algorithm is verified by deposition experiments under different beam currents and different metal transfer states, and the transfer function model between the molten pool feature and the energy input is established by step response experiments. The proposed molten pool visual monitoring method and model identification work provide the foundation for the closed-loop control of the molten pool. © 2023 IEEE.

关键词： Deposition

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Copy-Move image Forgery Detection using Deep Learning Approaches

Copy-Move Image Forgery Detection using Deep Learning Approa...

引用

Software, systems and Information Technology (SSITCON), International conference on

作者： Zainab Shawkat Ibrahim Taha Mohammed Hasan Department of Computer Science College of Science University of Diyala Iraq

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The advancement of digital image processing software has reached a stage where it is effortless to manufacture forgeries by using numerous manipulating approaches on authentic photos. Occupations such as law, healthcare, and education are susceptible to manipulated imagery. Therefore, it is crucial to ascertain the authenticity of an image. Single type of image manipulation is identified as ‘copy move,’ when significant item or objects are concealed by duplicating and inserting them into the original image. It is crucial to determine whether an image is genuine. The current techniques employed to identify image forgeries relied on conventional feature extraction algorithms, such as key point and block-based algorithms. These conventional approaches yield a subpar outcome. Deep learning approaches have established superior performance in image processing applications. This paper presents a highly effective method for identifying instances of copy-move forgery in images using a hybrid approach that combines deep learning (DL) such as (VGG19 and ResNet50) for feature extraction and Machine Learning (ML) algorithms including (Random Forests (RF) and Support Vector Machine (SVM)) to classify digital images. The proposed models (VGG19-RF), (VGG19-SVM), (ResNet50-RF), and (ResNet50-SVM) have been compared using two standard datasets. The results indicate that ((VGG19-RF) achieves higher recall, accuracy, precision and F1-score of 97% on MICC-F220 dataset, On the other hand (VGG19-RF) and (ResNet50-RF) achieve higher recall, precision, accuracy and F1-score of 97% on the MICC-F2000 dataset.

关键词： Support vector machines Deep learning Radio frequency Accuracy Digital images Feature extraction Software systems Forgery Standards Residual neural networks

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A N-Dimensional Discrete Chaotic Map and Its Performance

A N-Dimensional Discrete Chaotic Map and Its Performance

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Natural Language processing (ICNLP), International conference on

作者： Xin Gao Xiumei Cai Chengmao Wu School of Automation Xi’an University of Post and Telecommunications Xi’an China School of Electronic Engineering Xi’an University of Post and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In order to solve the problems of small key space and simple chaotic behavior of low-dimensional chaotic systems in discrete domain, an N-dimensional discrete chaotic mapping system is proposed. An N-dimensional discrete chaotic system is obtained by coupling Chebyshev mapping with ICMIC mapping. Taking two-dimensional chaotic mapping as an example, the Lyapunov index, bifurcation graph, correlation and other properties of the discrete chaotic system are analyzed and applied to more classical image encryption algorithms. Experimental simulation results show that the N-dimensional discrete chaotic mapping system has larger key space, better chaotic behavior, and better security performance for image encryption algorithms.

关键词： Performance evaluation Correlation Sensitivity Simulation Information security Bifurcation Natural language processing Encryption Trajectory Indexes

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FPGA-based Improved Canny Edge Detection System

FPGA-based Improved Canny Edge Detection System

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International conference on IEEE International conference on

作者： Zhang Huan Wang Yaxin Wang Shuang Liu Zhihao Chen Panyu Wang Fangjuan School of Electronic Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

In order to address the issues associated with the classic Canny operator, such as high computational load, incomplete edge information capture, and poor real-time performance, an improved multi-directional Canny edge detection method is proposed for using in service robot navigation systems. This method collects image information through the OV5640 camera, preprocesses the image after grayscale processing combined with mixed multiple filtering, and then performs eight directional adaptive threshold edge detection based on the classical Canny operator. After image enhancement, it runs on the FPGA chip. It can be seen from the experimental results that compared with the classic edge detection algorithm, the improved Canny operator has significantly improved the detection effect and can efficiently process the edges of various complex images in real time, providing a feasible solution for real-time image processing in the FPGA field.

关键词： Thresholding (Imaging) Service robots Navigation image edge detection Robot vision systems Noise Filtering algorithms Real-time systems Field programmable gate arrays image enhancement

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image Captioning based on Deep Convolutional Neural Networks and LSTM 2

Image Captioning based on Deep Convolutional Neural Networks...

引用

2nd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2022

作者： Srivastava, Swati Sharma, Himanshu Dixit, Pragati GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665432153

image captioning is a challenging task that needs the knowledge from both computer vision algorithms and language processing techniques. The model must be able to understand an image and then apply language generation techniques to describe an image in a natural language such as English. In this paper, we have presented an image captioning model which uses VGG16 for visual feature extraction and LSTM model to generate sentences corresponding to extracted visual features. We have performed experiments on Flickr8k and Flickr30k datasets. Bilingual Evaluation Understudy (BLEU) metric is used to measure the accuracy of the proposed model. The proposed model can be further extended to wide range of applications related to IOT based applications and smart control systems. © 2022 IEEE.

关键词： Computer vision

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Small-Capacity Liquid Density Detection Device Based on Computer Vision

Small-Capacity Liquid Density Detection Device Based on Comp...

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Advanced algorithms and Control Engineering (ICAACE), International conference on

作者： Renda Xing Bin Li Jie Chen Rui Liu School of Mechatronic Engineering and Automation Shanghai University Shanghai China Metrology and Testing Department Shanghai Institute of Quality Inspection and Technical Research Shanghai China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

To address the challenge of density detection for small-volume unit-labeled quantitative packaged products, a computer vision-based density detection device suitable for small-volume liquids was designed. The device integrates image processing technology with an automatic injection system and micro-volume constant-volume control technology to achieve automatic weighing and volume acquisition of volumetric flasks, thereby enabling automated density measurement of samples. Additionally, a data acquisition and processing software system was developed to minimize human reading and operational errors, improving detection efficiency and measurement accuracy. Experimental results demonstrate that the device achieves a relative expanded uncertainty of $0.00069 \mathrm{g} / \text{cm}^3$ and a measurement repeatability of $0.0005 \text{g/cm}^{3}$ , ensuring high detection precision.

关键词： Uncertainty Accuracy Liquids Software design image processing Measurement uncertainty Software systems Hardware Software measurement Thermal stability

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Efficient and Parallel Separable Dictionary Learning 27

Efficient and Parallel Separable Dictionary Learning

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27th IEEE International conference on Parallel and Distributed systems, ICPADS 2021

作者： Rusu, Cristian Irofti, Paul University Politehnica Bucharest Faculty of Automatic Control and Computers Department of Automatic Control and Computers Bucharest Romania University of Bucharest Faculty of Mathematics and Computer Science Department of Computer Science Bucharest Romania

ISBN: (纸本)9781665408783

Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learns such dictionaries which reaches sparse representations competitive with the previous state of the art dictionary learning algorithms from the literature but at a lower computational cost. We highlight the performance of the proposed method to sparsely represent image and hyperspectral data, and for image denoising. © 2021 IEEE.

关键词： image denoising

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