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

共 12871 条记录，以下是31-40 订阅

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Research on Traditional Art image Reconstruction Method Based on Computer Vision Analysis 3

Research on Traditional Art Image Reconstruction Method Base...

引用

3rd International conference on Artificial Intelligence and Autonomous Robot systems, AIARS 2024

作者： Ruan, Xunmiao Guangdong University of Science &Technology Dongguan Guangdong523083 China

ISBN: (纸本)9798350376173

In the information age, image processing technology has become prevalent across various domains. To enhance image correction, computer vision algorithms can be employed. Traditional methods for structural system identification rely on wired or wireless measurement systems for data gathering, which can be cumbersome and time-consuming due to the need for arranging sensors and data collection equipment. The proposed system involves two primary steps: line drawing extraction and rendering. During the extraction phase, an edge detection technique that integrates low and middle-level visual information is utilized. Based on the results of traditional edge detection, the edges are simplified and connected using computer vision principles. This approach treats image processing as the focal point and aims to establish a scientific model for distorted images. image restoration can be facilitated through sample image filtering and noise reduction techniques. Additionally, this article delves into the principles and associated technologies of computer vision, analyzing patterns of visual interaction. Chinese traditional art imagery, as a cultural medium and symbol, fulfills an artistic communication function while preserving the nation's rich cultural heritage. The image processing techniques grounded in computer vision algorithms offer numerous advantages and superior accuracy. © 2024 IEEE.

关键词： Noise abatement

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System-level design for DPCM image Compression with SoC-FPGA Accelerator

System-level design for DPCM Image Compression with SoC-FPGA...

引用

IEEE Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Safaei, Amin Sabeti, Samira Masoudian, Morteza Lakehead Univ Dept Comp Sci Thunder Bay ON Canada

ISBN: (纸本)9798350371635;9798350371628

The implementation of data compression in embedded platforms and portable systems is growing rapidly due to the demands for image and video processing in daily human life. Among the data compression applications, medical imaging, such as Computed Tomography (CT), and Light Detection And Ranging (LiDAR) have become active research areas, especially when data transmissions must be performed over a communication interface. Because of the complexity of data compression algorithms and the high resolution of input streams, current embedded platforms may not be able to fully exploit data compression algorithms. To address this restriction, recent approaches utilize hardware accelerators to speed up the computation. Among available hardware accelerators, system-on-a-chip field-programmable gate arrays (SoC-FPGAs) have emerged as an important architecture approach in terms of achieving satisfactory computational performances. This study presents a hardware accelerator for the computation of a differential pulse code modulation (DPCM) algorithm implemented and synthesized on a Zynq SoC-FPGA and achieving an acceleration factor of 88x.

关键词： Field programmable gate arrays image processing data compression embedded vision subtraction techniques

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Research on the Construction of image processing Model Based on Computer Vision Algorithm 2

Research on the Construction of Image Processing Model Based...

引用

2nd IEEE International conference on Control, Electronics and Computer Technology, ICCECT 2024

作者： Yi, Wang School of Law Shandong University of Technology Economic Law Shandong Zibo255000 China

ISBN: (纸本)9798350380958

This research focuses on constructing an efficient image processing model, which is rooted in computer vision algorithms, to ameliorate image distortion and optimize visual display systems. The article initially discusses the significance of image distortion correction and its processing flow, encompassing detailed steps ranging from image preprocessing, model construction, parameter estimation, to pixel coordinate transformation. Furthermore, the design approach of computer vision display systems is explored, covering the theory of light field reconstruction, software and hardware design essentials, ensuring high fidelity in image presentation. Through multiple experiments conducted on datasets of distorted images, the effectiveness of the model in correcting distortion and enhancing image quality is empirically demonstrated, with the experimental results strongly supporting the feasibility and superiority of the model. This research holds positive implications for advancements and practical applications in the field of image processing. © 2024 IEEE.

关键词： image enhancement

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Soil Type Identification via Deep Learning and Machine Learning Methods 5th

Soil Type Identification via Deep Learning and Machine Learn...

引用

5th International conference on Communication and Intelligent systems (ICCIS)

作者： Jalapur, Shruti Patil, Nagamma Natl Inst Technol Karnataka Dept Informat Technol Surathkal India

ISBN: (纸本)9789819720811;9789819720828

Soil type identification stands as a crucial concern in numerous countries, to ensure optimal crop yield, farmers need to accurately identify the suitable soil type for specific crops, which plays a significant role in meeting the heightened global food demand. The objective of this survey paper is to present a thorough and up-to-date overview of prevailing methodologies in soil identification, primarily focusing on image analysis, machine learning, and deep learning techniques. The paper initiates by highlighting the significance of soil identification and the limitations inherent in traditional methods. It then delves into the fundamental principles of image processing, deep learning, and spectroscopy, explaining how these techniques can be applied to soil identification. The survey presents an in-depth analysis of various image processing techniques employed for soil identification, including image segmentation, feature extraction, and classification algorithms. Furthermore, it discusses the application of deep learning models for soil classification based on image data.

关键词： Soil identification image processing Deep learning Spectroscopy Classification Convolutional neural networks

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QUANTUM INSPIRED image AUGMENTATION APPLICABLE TO WAVEGUIDES AND OPTICAL image TRANSFER VIA ANDERSON LOCALIZATION 49

QUANTUM INSPIRED IMAGE AUGMENTATION APPLICABLE TO WAVEGUIDES...

引用

49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Palaiodimopoulos, Nikolaos Rey, Vitor Fortes Tschoepe, Matthias Joerg, Christina Lukowicz, Paul Kiefer-Emmanouilidis, Maximilian FORTH Inst Elect Struct & Laser GR-70013 Iraklion Greece DFKI Embedded Intelligence D-67663 Kaiserslautern Germany RPTU Kaiserslautern Landau Dept Phys D-67663 Kaiserslautern Germany RPTU Kaiserslautern Landau Dept Comp Sci D-67663 Kaiserslautern Germany

ISBN: (纸本)9798350344868;9798350344851

We present a quantum inspired image augmentation protocol which is applicable to classical images and, in principle, due to its known quantum formulation applicable to quantum systems and quantum machine learning in the future. The augmentation technique relies on the phenomenon Anderson localization. As we will illustrate by numerical examples the technique changes classical wave properties by interference effects resulting from scatterings at impurities in the material. We explain that the augmentation can be understood as multiplicative noise, which counter-intuitively averages out, by sampling over disorder realizations. Furthermore, we show how the augmentation can be implemented in arrays of disordered waveguides with direct implications for an efficient optical image transfer.

关键词： image Augmentation Quantum Inspired algorithms Optical Communication Disordered systems

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Comparative image processing and Object Detection in Aerial Search and Rescue 1

Comparative Image Processing and Object Detection in Aerial ...

引用

1st IEEE International conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Rawat, Sushil Rawat, Abhay Singh Singh Mewad, Vikas Vaidya, Himadri Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372748

Aerial search and response plays an important role in finding and rescuing persons in need. Unmanned Aerial Vehicle (UAV) -acquired aerial images provide an intensive profile search area and facilitate identification of potential targets. To improve the effectiveness of aircraft Search and Rescue missions, this study compares many object detection algorithms and image processing methods. The investigated object detection methods are Mask R-CNN, SSD, and YOLOv8, and image processing techniques are contrast improvement and image brightness control. The effectiveness of these strategies has been evaluated by using real aerial images that include different targets such as humans, vehicles, and fire. The results show how well the object detection algorithms work to accurately distinguish targets, and how an attractive image processing approach improves the image performance of the best object detection model, as well as how the performance of the best object detection model with image processing can improve the performance of model. This research shows that combining image processing (contrast and brightness) with an optimal object detection algorithm (Mask R-CNN) significantly improves target detection in aerial search and rescue. While only Mask R-CNN performs best, adding image processing increases its accuracy to 0.92, mAP to 0.89, and makes it more reliable in finding people in need. This shows the potential of this joint approach to save people in air rescue missions. © 2024 IEEE.

关键词： Antennas

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image Denoising: A Comparative Study of Convolutional Neural Networks 4th

Image Denoising: A Comparative Study of Convolutional Neural...

引用

4th International conference on Digital Technologies and Applications (ICDTA)

作者： Anzal, Oumaima Guessous, Najib Ouakrim, Youssef Univ Sidi Mohamed Ben Abdellah Dept Math & Comp Sci Lab M2PA Fes 30030 Morocco

ISBN: (纸本)9783031686528;9783031686535

image processing is a vigorous area of study that utilizes various algorithms to manipulate, analyze, and enhance digital images. image denoising is one of the crucial applications of image processing. Still, the occurrence of image noise is inevitable due to various sources, including low light conditions, high ISO settings, and transmission artifacts, necessitating the availability of denoising techniques to significantly improve visual image quality. This is particularly important in fields such as computer vision, medical imaging and remote sensing. Not only does it facilitate image analysis by retaining important details, but it also optimizes the performance of compression algorithms, improves storyteller detection. In this project, we propose an in-depth study of image denoising, focusing on the use of convolutional neural networks (CNNs). The problem of Gaussian noise will be treated by applying different levels of s (low sigma = 15, medium sigma = 25, and high sigma = 50). During this project, a full comparative analysis will be made with the three mainCNNarchitectures: DnCNN, RIDNet, and IRCNN, illustrative of the quantitative and qualitative experimental results obtained by these different approaches. In fact, these approaches have shown impressive performance in image processing tasks, including image denoising, since they used different techniques that can be adopted in CNN, such as regularization methods, batch normalization, and residual learning.

关键词： image Denoising Convolutional Neural Networks (CNNs) DnCNN RIDNET IRCNN Noise level PSNR SSIM

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A Fast image Dehazing Using Encoder–Decoder Deep Neural Network

A Fast Image Dehazing Using Encoder–Decoder Deep Neural Net...

引用

International conference on Advances in Signal processing and Communication Engineering, ICASPACE 2023

作者： Gurjar, Prakhar Pavan Kumar, Balla Kumar, Arvind National Institute of Technology Kurukshetra India

ISBN: (纸本)9789819705610

The image quality is degraded in bad weather situations such as haze or fog. This problem can affect image processing applications such as computer vision, security, and some other real-time image processing systems. Hence, image dehazing is essential for these applications to improve their performance. There are many dehazing algorithms that are implemented earlier using the atmospheric light-scattering model and enhancement-based techniques. However, these algorithms are complex and consume more execution time to perform dehazing, which isn’t fit for real-time image processing applications. To overcome this drawback, an encoder–decoder deep neural network (EDDNN) is designed in this manuscript for fast image dehazing purposes. The proposed EDDNN contains a total of four layers, they are input, encoder, decoder, and output layers. The proposed EDDNN is trained and tested with the most popular dataset called Realistic single image dehazing (RESIDE). The proposed EDDNN is fast in execution that suits real-time image processing systems (RTIPS) and also effectively eliminates the haze effect from the image. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Asphalt Roadside Flatness Detection Based on Laser Sensing image processing 5

Asphalt Roadside Flatness Detection Based on Laser Sensing I...

引用

5th International conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Li, Ruihong Nantong Vocational University Architecture and Engineering Nantong226001 China

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

Smoothness assessment plays a key role in evaluating the quality of asphalt concrete pavements, which has a direct impact on vehicle comfort, safety, and pavement longevity. This study presents a novel approach to asphalt pavement smoothness detection based on laser-sensing image processing. The proposed method uses advanced image processing algorithms to extract relevant features indicative of flatness from high-resolution images acquired using laser sensing technology. Key steps include image pre-processing, laser sensing image processing model construction, and performance evaluation. Experimental results demonstrate the effectiveness of the proposed method, which consistently outperforms existing techniques in detecting flatness with an average accuracy of the 98.89%. Statistical analysis confirms the superiority and consistency of the proposed method, highlighting its potential for real-time road surface monitoring and maintenance. © 2024 IEEE.

关键词： Asphalt concrete

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Energy Efficient Approximate Bfloat-16 Floating Point Division Hardware for image processing

Energy Efficient Approximate Bfloat-16 Floating Point Divisi...

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2024 International conference on Smart Electronics and Communication systems, ISENSE 2024

作者： Nagakalyan, M. Sujatha, G. Sk, Noor Mahammad Sri Venkateswara College of Engineering Dept. of ECE Tirupati India IIITDM Kancheepuram Dept. of CSE Chennai India

ISBN: (纸本)9798331528126

Energy efficient designs are need of the hour and image processing applications are error tolerant application. Where the error present in the computing does not impact the output visual quality. This work proposes an efficient approach to design a Bfloat floating point division. This work proposes three algorithms that are implemented at the hardware level. All the proposed algorithms will produce lesser error in the output and does not impact the output visual quality. The proposed work reduces the energy sianlficantly than the existing works. © 2024 IEEE.

关键词： Approximate Computing Bfloat floating Point Division Energy Efficient

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