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共 128 条记录，以下是81-90 订阅

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Comparative Evaluation of Various Wavelet Techniques for image Compression

Comparative Evaluation of Various Wavelet Techniques for Ima...

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international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Hidayat Un Nisa Samrah Mehraj Shabir A. Parah dept. of Electronics and Instrumentation Technology University of Kashmir Srinagar India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

image compression is a critical component of digital systems, as it reduces the file size of images while preserving quality, thereby facilitating minimal storage requirements and seamless transmission. Wavelet transforms have emerged as powerful tools in image compression for providing both time and frequency information, making them particularly effective in capturing essential image features. Despite numerous advancements, there is still significant demand for image compression techniques that achieve higher compression ratios with minimal loss of quality. this paper presents a comprehensive analysis of various lossy image compression techniques, focusing on the strengths of wavelet-based methods. Techniques such as Discrete Wavelet Transform, Lifting Wavelet Transform, and Integer Wavelet Transform are examined in detail, with key metrics including Peak signal-to-Noise Ratio (PSNR), compression ratio, and time efficiency analyzed to provide a thorough understanding of each technique's effectiveness. the timing analysis is particularly crucial for assessing the practical applicability of these methods in real-world scenarios. this study aims to offer valuable insights for developing more advanced image compression methods that meet the growing demands of modern data management systems.

关键词： image quality Measurement Time-frequency analysis image coding PSNR Wavelet domain Wavelet analysis Real-time systems Discrete wavelet transforms Timing

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Real-Time Vehicles Speed Estimation

Real-Time Vehicles Speed Estimation

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Mohamed Alwindi Taha Tombokti Ali Ganoun Electrical and Electronic Engineering Department Faculty of Engineering University of Tripoli Tripoli Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

the requirement for precise and effective ways to track the speed of vehicles has grown in the fast-paced world of today. the precision and real-time monitoring of traditional speed measurement techniques, such radar guns and speed cameras, are limited. A new path for real-time vehicles speed measurement has emerged with the development of image processing tools. through the use of computer vision, image processing techniques can extract useful information from visual data, such as pictures or video frames. Real-time accurate speed estimation of vehicles can be achieved by analyzing their motion patterns captured by cameras using advanced algorithms. this paper presents two methods for estimating the real-time speed of vehicles using image processing techniques. the first method utilizes the Lucas-Kanade algorithm, while the second method employs the Gaussian Mixture Model (GMM). Both approaches aim to automatically estimate the speed of vehicles from a real time sequence captured by a stationary camera. the performance of these methods for tracking of vehicles is evaluated as well in this study.

关键词： Computer vision Visualization Accuracy image processing Estimation Object detection Cameras

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Face Makeup Transfer Algorithm Based on CycleGAN

Face Makeup Transfer Algorithm Based on CycleGAN

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Yunjin Gu Zihan Xu Dengyu Zhang International School Beijing University of Posts and Telecommunications Beijing China School of Information University of Sheffield Sheffield United Kingdom International School Zhejiang Gongshang University Hangzhou China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Face makeup transfer is a popular research direction in the domain of computer vision, where makeup style is considered a special style of facial images, and makeup transfer is a part of style transfer. the process of human face makeup transfer is to transfer the makeup of the reference makeup image to the original makeup image while keeping the facial characteristics of the original makeup image. Currently, Generative Adversarial Networks (GANs) have become the mainstream technology for solving the problem of face makeup transfer. Face makeup transfer will be widely used in the future, such as in beauty cameras, makeup software for one-click makeup, virtual makeup trials for cosmetics, and video makeup transfer (such as live streaming software makeup and TV drama character makeup), and has received widespread attention in recent years. It has significant research value both in academia and industry. In this study, the paper adopted a series of innovative strategies to optimize model performance, including the introduction of non-local attention mechanisms to enhance feature extraction capabilities, improved residual blocks to increase network depth and efficiency, and attention modules in discriminators to improve focus on key features. Additionally, the paper used multi-scale discriminators to capture feature information at different levels and added skip connections to promote gradient flow and information integration, thereby achieving more efficient network training and better performance.

关键词： Training Industries TV signal processing algorithms Streaming media signal processing Feature extraction Software User experience Faces

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CScGAN: Leveraging Compressed Sensing and Generative Adversarial Networks for image Super-Resolution Reconstruction

CScGAN: Leveraging Compressed Sensing and Generative Adversa...

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Data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Chang Li Xin Wang Zhengyu Li School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

We propose a novel image super-resolution technique, the Compressive Sensing combined with Generative Adversarial Network model (CScGAN). During the image super-resolution reconstruction process, artifacts that arise from the complexity of the reconstruction network, imbalances in training data, and instability in the training process can degrade the quality of the reconstructed images. Our method concentrates on minimizing reconstruction artifacts through advanced signal processing and structural optimization of the network. We elucidate our CScGAN model using architectural diagrams and mathematical notation. When compared with multiple public datasets and state-of-the-art methods, our approach significantly reduces the impact of artifacts during the reconstruction process and enhances the quality of the reconstructed images.

关键词： Training image coding Sparse approximation Superresolution Training data Generative adversarial networks Feature extraction Stability analysis image reconstruction Compressed sensing

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Deep Learning Fusion Ensemble for Enhanced Traffic Sign Detection Using the ICTS Dataset

Deep Learning Fusion Ensemble for Enhanced Traffic Sign Dete...

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international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Samir Kumar Majhi Rahul Kumar Gupta Binayak Ojha Aaron Sapkota Debendra Muduli Department of Computer Science C.V. Raman Global University Bhubaneswar India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Traffic Sign Identification and Detection(TSID) sys- tems are vital components of advanced and smart driving systems as well as autonomous vehicles. Our proposed model, based on deep learning, specifically trains and evaluates convolutional neural networks (CNN) using ensemble pre-trained models such as VGG16 and ResNet50 from the field of transfer learning for image classification and combines them in a fusion ensemble model. We leveraged data augmentation strategies to increase the training images during preprocessing. Our data and outcomes show that the fusion of the proposed model performed better, achieving a classification accuracy of 99.70 % using the ICTS (Indian Cautionary Traffic Sign) dataset compared to some traditional models.

关键词： Deep learning Training Adaptation models Accuracy Transfer learning Data models Road safety Convolutional neural networks Residual neural networks image classification

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Multi-scale target detection for airfield visual navigation of taxiing aircraft

Multi-scale target detection for airfield visual navigation ...

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Neural Networks, Information and Communication Engineering (NNICE), international conference on

作者： Qijun Luo Jiagang Chen Xuerui Zhang Tao Zhang Tianjin Intelligent Signal and Image Processing Experiment Civil Aviation University of China Tianjin China

In the context of visual navigation-based civil aircraft assistance systems, the scene target scales captured by onboard cameras exhibit significant variations, and the computational power of airborne platforms is limited. To promptly and accurately identify threat targets in airborne scenic images, a lightweight multi-scale object detection algorithm based on improved YOLOv5s is proposed. Firstly, on the foundation of the Weighted Bidirectional Feature Pyramid Network (BIFPN), a Coordinate Attention (CA) mechanism is integrated to design the CA-BIFPN feature fusion network. this enhances feature representation for small targets and improves the model's learning capability for multi-scale targets. Secondly, a cross-level sub-network module is employed to lightweightly process the neck section, reducing the model's parameter count and thereby improving the overall network's detection speed. Experimental results on a self-constructed scene image dataset demonstrate that the proposed method achieves a detection frame rate of 89FPS, an increase of 5fps. the average precision mean (mAP) on the test set is 70.6%, a 3.4% improvement over the original YOLOv5s. the detection accuracy surpasses typical algorithms such as YOLOv6, YOLOv7, YOLOX, and Faster-RCNN.

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advanced Model for Oracle Bone image Denoising and Feature Extraction Based on Multi-Algorithm Integration and Wavelet Transform 24

Advanced Model for Oracle Bone Image Denoising and Feature E...

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Proceedings of the 2024 4th international conference on Computational Modeling, Simulation and Data Analysis

作者： Yufen Zhang Zile Xu Mengguo Zhang Lin Yang Xudong Wang College of Science University of Shanghai for Science and Technology Shanghai China Department of Water Conservancy Engineering Henan Vocational College of Water Conservancy and Environment Zhengzhou Henan China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798400711831

As an important part of ancient Chinese cultural heritage, oracle bone image has important historical and cultural value. However, due to the unique image features and noise interference, effective image processing and analysis are still facing great challenges. In this paper, we propose an advanced model for oracle bone image denoising and feature extraction based on multi-algorithm ensemble and wavelet transform. Firstly, wavelet transform is used for image denoising to effectively remove noise and retain key image features. then, the image contrast is enhanced by gray inversion and binarization, and the image structure is simplified by skeletonization, which lays the foundation for subsequent feature extraction. In the frequency domain analysis, Fourier transform is applied to reveal the spectral characteristics of the image, so as to understand and analyze the image data more deeply. In order to accurately determine the edges and corners of oracle images, this study compared several edge detection algorithms (such as Canny, Sobel, and Prewitt) and corner detection algorithms (such as Harris, SURF, and ORB). the results show that Canny performs well in edge detection and noise suppression, while SURF and ORB are more suitable for real-time processing and resource constrained environments. this model provides a new method for the digital preservation and dissemination of oracle bone images, and establishes a benchmark for the optimization design of algorithms in the field of image analysis.

关键词： Binarization 3

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Implementation of a Deep Learning Technique for Detection of Objects on Roads for Autonomous Vehicles

Implementation of a Deep Learning Technique for Detection of...

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international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Kakarla. Deepti S. Aruna Deepti Ch. Neetu K.R. Deepthi Dept of ECE Vasavi college of Engineering Hyderabad India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Autonomous driving and vehicle safety things profoundly depend on object detection to guarantee precise an robust right-time performance under different environmental conditions. the effectiveness of this systems depends on precise and efficient processing algorithms. the manuscript aim is to contrast the correctness of three notable image processing algorithms in the context of auto driving: Convolutional Neural Networks (CNN), Retina Net, and You Only Look Once v8 (YOLOv8). the assessment is performed using the Roboflow and kaggle dataset. the first algorithm under assessment is the Convolutional Neural Networks(CNN), Retina Net, and You Only Look Once v8 (YOLOv8). Each algorithm's unique approach to object detection is reviewed, observing their strengths and weaknesses in accurately identifying things in complex road situations. A comparative analysis is performed using the Robo flow and Kaggle dataset, covering various real-world contexts. the algorithms are evaluated based on correctness, considering occurrences with and without occlusion and truncation. Computation time,a vital aspect for real-time applications, is also considered. the precision-recall curve is applied as a metric to determine the algorithms' overall performance. Retina Net, with its focal loss mechanism, is reviewed for its capability to manage occluded and truncated things and its influence on overall correctness. YOLOv8 emerges as a standout performer, displaying superior correctness in discovering challenging road target things under complex situations and adverse weather conditions. Its right-time deployment capabilities, combined with high correctness, position it as a compelling choice for autonomous driving systems. In conclusion, this comparative analysis shines a light on the strengths and deficiencies of notable image processing algorithms for object discovery in autonomous driving systems. YOLOv8 stands outas the most accurate and efficient algorithm, excelling in challenging real-

关键词： YOLO Accuracy Roads image processing Simulation Vehicle safety Retina Real-time systems Convolutional neural networks Autonomous vehicles

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Detection of Spinal Fractures Using image Enhancement Method and Comparison of PSNR and MSE Matric Based on CT-Scan images

Detection of Spinal Fractures Using Image Enhancement Method...

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), international conference on

作者： Sri Rahmawati Iskandar Fitri Gunadi Widi Nurcahyo Information System Department Universitas Putra Indonesia YPTK Padang Indonesia Information Technology Department Universitas Putra Indonesia YPTK Padang Indonesia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

this study explores the enhancement of diagnostic capabilities in Computed Tomography (CT) scans for detecting spinal fractures through advanced image processing techniques. the vertebral column's crucial role in physiological functions across all age groups necessitates precise imaging to identify impairments causing severe pain. the research focuses on preprocessing methods, particularly the cropping of the lumbar region to reduce intrinsic noise and improve CT image clarity. advanced enhancement techniques such as sharpening and contrast stretching are applied to augment image quality, facilitating the precise identification of spinal fractures. the method used in this research is named the image enhancement method which consists of three sub-methods, namely the enhancement method followed by the sharpening method and the linear contrast stretching method (LCM). Next, we compared and evacuated using Peak signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) metrics, comparing the enhanced images to the original scans. Results indicate that linear contrast stretching produces images with darker intensities and a broader intensity range, significantly improving image quality. Notably, the contrast-stretched image numbered ten achieved a PSNR of 22.5122 dB and an MSE of 0.074884, demonstrating superior enhancement. these findings highlight the potential of advanced image processing techniques to substantially improve CT imaging for spinal fracture diagnosis, offering a valuable tool for enhancing clinical outcomes.

关键词： image quality Measurement PSNR Pain Computed tomography Noise Physiology image enhancement signal resolution Medical diagnostic imaging

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Enhancing Liver Cancer Detection with AI-Powered image processing

Enhancing Liver Cancer Detection with AI-Powered Image Proce...

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Sustainable Expert Systems (ICSES), international conference on

作者： A. Suresh Kumar S. Rajathi Vineetha Vargheese M. Sudha Anju A P. Lokeshkiran Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India Department of Information Technology Karpagam Institute of Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

this project aims to identify liver tumors using deep learning techniques. the core of our approach is a sophisticated neural network trained on real CT scans of patients with liver tumors. By leveraging advanced signal-processing algorithms and deep learning models, our AI method accurately distinguishes between tumors and healthy liver tissue. Accurate identification of liver tumors on CT images is crucial for planning effective radiation therapy. CT and MRI scans provide detailed images of the internal structure of the body by analyzing tissues. Our primary objective is to develop an AI-driven neural network that can reliably identify liver tumors in these images. Given the variability in liver location among individuals, our approach employs specific techniques to ensure precision. Using U-net technology, our goal is to enhance the speed and accuracy of tumor identification, thereby improving patient access to timely and accurate diagnoses. this project underscores the significant potential of deep learning applications in health care.

关键词： Deep learning image segmentation Accuracy Computed tomography Neural networks Liver Planning Radiation therapy Object recognition Tumors

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