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

共 15578 条记录，以下是41-50 订阅

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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...

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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...

引用

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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Realization of Digital Protection Algorithm of National Traditional Culture Based on virtual Reality Technology

Realization of Digital Protection Algorithm of National Trad...

引用

2024 International conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Chen, Hongxia Hou, Lanbao School of Literature and Media Jingchu University of Technology Jingmen448000 China School of Mathematics and Physics Jingchu University of Technology Jingmen448000 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

National traditional culture and traditional technology are the products of historical precipitation and indispensable precious resources. The establishment of national cultural database is of great significance for the protection and inheritance of cultural heritage. Aiming at the specific functional requirements of traditional cultural digital protection system for image segmentation and retrieval, this paper focuses on the common algorithms of image segmentation and feature matching. This paper analyzes and compares the applicability and processing effect of each algorithm in ethnic patterns. EGBIS image segmentation algorithm and shape context feature matching algorithm are used to achieve high precision image processing. This paper designs and implements a national culture retrieval system that meets the application needs. Combined with the image characteristics of ethnic patterns and the application requirements of the system, this paper defines the concepts of source images, primitives and hyperprimitives, and establishes a relational database based on them. This study has laid a certain theoretical foundation for the study of digital inheritance of national culture. © 2025 SPIE.

关键词： image segmentation image retrieval Databases Feature extraction image processing algorithms and systems image processing visualization Distance measurement virtual reality visual analytics

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Research on the Design of Robot image Sensing System Based on Artificial Intelligence 6

Research on the Design of Robot Image Sensing System Based o...

引用

6th IEEE International conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Kang, Li Liu, Zhengshuo Shandong Institute of Commerce and Technology Shandong Jinan China National University of Singapore Lower Kent Ridge Road Singapore

ISBN: (纸本)9798350374315

This study focuses on the design and implementation of an artificial intelligence-driven robotic image perception system, aimed at enhancing robots' visual perception capabilities in complex environments. By exploring a cylindrical projection-based global calibration method, this research investigates its model, distortion correction algorithms, and viewpoint switching algorithms, offering an efficient and precise image processing solution. Furthermore, this paper provides an in-depth analysis of image anti-aliasing algorithms, including nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation, and conducts a detailed comparison among these three algorithms. The section on panoramic image rapid synthesis achieves efficient stitching and blending of images through SURF feature point extraction and matching. In terms of system design and testing, specific functional design schemes are proposed and comprehensively tested and analyzed, validating the effectiveness and practicality of the system. This study not only provides theoretical support and technological implementation for robotic vision systems but also serves as a robust reference for future developments in robotic vision technology. © 2024 IEEE.

关键词： Robot vision

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Recent advancements in image Steganography using Generative Adversarial Networks methods

Recent advancements in Image Steganography using Generative ...

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2024 International conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Fatima, Syeda Imrana Garapati, Yugandhar Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350360660

Generative Adversarial Networks are employed by GAN-based models in image steganography to efficiently conceal information from images while preserving their visual integrity. These models are made up of two primary parts: the discriminator which determines if an image is a real image or a stego-image, and the generator which embeds the hidden data into images. The generator learns how to embed data in a way that minimizes obvious distortions through adversarial training, where the discriminator and generator fight to make the hidden data less noticeable to detection algorithms and human observers. High data capacity and versatility are strengths of GAN-based steganography models, which enable efficient data concealment across a variety of image formats and steganographic techniques. They dynamically adjust the embedding procedure to strike a compromise between image quality and data capacity. They do, however, come with certain drawbacks such as the need for sophisticated model training, high processing overhead and hyperparameter sensitivity. Notwithstanding these drawbacks, GAN-based techniques mark a substantial breakthrough in the development of delicate and reliable image steganography techniques. © 2024 IEEE.

关键词： Generative adversarial networks

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A Multi-scale video Denoising Algorithm for Raw image 1st

A Multi-scale Video Denoising Algorithm for Raw Image

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World conference on Intelligent and 3-D Technologies (WCI3DT)

作者： Ma, Bin Hu, Yueli Lv, Xianxian Li, Kai Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China

ISBN: (纸本)9789811971860;9789811971846;9789811971839

video denoising for raw image has always been the difficulty of camera image processing. On the one hand, image denoising performance largely determines the image quality;moreover, denoising effect in raw image will affect the accuracy of the following operations of ISP processing flow. On the other hand, compared with image, video has motion information in time sequence;thus, motion estimation which is complex and computationally expensive is needed in video denoising. In view of the above problems, this paper proposes a video denoising algorithm for raw image, performing multiple cascading processing stages on raw-RGB image based on convolutional neural network, and carries out implicit motion estimation in the network. The denoising performance is far superior to that of traditional algorithms with minimal computation and bandwidth, and has computational advantages compared with most deep learning algorithms.

关键词： image video Denoising image Convolutional neural network

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