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

共 14631 条记录，以下是171-180 订阅

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Role, Significance, and Impact of Method Noise in Multi-Modal Medical image Fusion

Role, Significance, and Impact of Method Noise in Multi-Moda...

引用

Emerging systems and Intelligent Computing (ESIC), International conference on

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Multi modal medical image fusion in, for instance, combining information from computed tomography (CT) scans, positron emission tomography (PET) and magnetic resonance imaging (MRI) into one single dataset, improves the accuracy of diagnostics due to atomistic and functional details of the images. In this paper, the role, importance, and effect of method noise in multi-modal medical image fusion, is considered to assess and improve fusion algorithms. The effectiveness of an algorithm can be determined in terms of its efficacy at preserving salient image content and removing unwanted artifacts while maintaining the former, which is defined by method noise, the residual noise after image processing. In this paper, I review the theoretical basis of method noise, discuss its function in both fusion and denoising processes, and discuss its practical and conceptual implications, showing its importance for quality assessment and algorithm refinement. We apply method noise to the Non-Subsampled Shearlet Transform (NSST) to demonstrate application in multi-modal medical image fusion. Comparative experiments using NSST based fusion with /without method noise demonstrate significantly different visual clarity and quantitative metrics as measured with different metrics. Results show that method noise both maintains image integrity by preventing detail loss and aids in the integration of complementary detail from different modalities. The need for method noise in advancing image fusion techniques is emphasized in this paper, and a framework for future research and application of fusion in diagnostic imaging is presented.

关键词： Visualization Accuracy Reviews Magnetic resonance imaging Computed tomography Noise Refining Quality assessment image fusion Medical diagnostic imaging

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Depth and Dimension Estimation Using Computer Vision

Depth and Dimension Estimation Using Computer Vision

引用

International conference on Wireless Communications, Signal processing and Networking (WiSPNET)

作者： Ravindran S Srikanth S Raagul T S Madhankumar R M Ganesan M Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The growing importance of depth and dimension estimation in robotics, autonomous vehicles, and construction industries highlights the need for accurate environmental perception. Monocular depth-based cameras face challenges such as scale ambiguity and data scarcity, making precise distance measurements difficult. Stereo-vision setups provide improved scene information, thereby reducing the error rate. Deriving depth from stereo images using a Pseudo-LIDAR based approach enhances depth data. Pixel-to-length conversion from this depth information enables precise dimension estimation of objects. Developing a computer vision algorithm that calculates pixel-to-length ratios at various distances and adjusting for calibration discrepancies will streamline decision-making in autonomous systems, improving efficiency across industrial operations.

关键词： Wireless communication Computer vision Accuracy image edge detection Depth measurement Estimation Signal processing algorithms Streaming media Stereo vision Autonomous vehicles

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Research on the Application of Virtual Reality Technology in Digital Protection of Cultural Heritage Landscape

Research on the Application of Virtual Reality Technology in...

引用

Electronics, Energy systems and Power Engineering (EESPE), IEEE International conference on

作者： Yan Huang Yu Huang Nanning University Nanning China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper puts forward a kind of digital protection system based on VR, which uses 3D modeling and dynamic rendering to provide high quality digital protection and exhibition platform. First, the cultural heritage landscape is modeled with high precision through laser scanning and drone photogrammetry technology to obtain three-dimensional point cloud data. Subsequently, an efficient dynamic rendering algorithm is designed, which can enhance the realism and immersion of the landscape by simulating physical properties such as lighting, shadows, reflection and refraction. To ensure that the system can still run smoothly under high load, image processing optimization technology is used to stabilize the rendering frame rate at more than 60 frames per second and perform well under multiple dynamic scenes switching. At last, the paper builds a virtual reality simulation environment based on Unity and Unreal Engine. The experiment shows that the system can reconstruct the cultural heritage scene exactly, the rendering effect is high, and the user experience rate reaches 92%.

关键词： Solid modeling Three-dimensional displays Heuristic algorithms Virtual environments Rendering (computer graphics) Visual effects User experience image restoration Cultural differences Protection

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AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool for Non-Invasive Medical Diagnostics

AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool fo...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Aniruddha Ghosh Sayan Snigdha Pal Sumedha Das Spandan Bhattacharya Arijit Bhattacharya Nilanjana De Sudipta Sahana Computer Science and Engineering(Artificial Intelligence and Machine Learning) Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata West Bengal India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Thermography is an innovative technique that creates a heatmap of different portions of the human body by using infrared (IR) radiation where color scales vary for various temperature regions. This work's scope aims to extend thermal imaging applications with machine learning algorithms in conjunction with Azure API services to analyze thermal images and return a preliminary diagnosis. The project consists of generating heatmaps for better visualization results, analyzing them through image processing techniques, and feeding the information to Azure’s Custom Vision API to detect anomalies and classify health conditions and finally producing a proper report. This research allows individuals and healthcare professionals to have a quick, non-invasive diagnostic tool that empowers health monitoring and preventive care. It combines the capabilities of contemporary technologies with uses directed toward health practice, leading to inimical patient outcomes and true practices.

关键词： Visualization Machine learning algorithms image color analysis Infrared heating Infrared imaging Thermal analysis Medical diagnosis Predictive analytics Intelligent systems Monitoring

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Online Optimization of Offloading Video Analytics Tasks to Multiple Edges for Accuracy Maximization

Online Optimization of Offloading Video Analytics Tasks to M...

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

作者： Yu Liang Sheng Zhang Jie Wu School of Computer and Electronic Information and the School of Artificial Intelligence Nanjing Normal University State Key Laboratory for Novel Software Technology Nanjing University Center for Networked Computing Temple University US

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time video analytics (VA) presents challenges due to its computational intensity and latency sensitivity, especially when processed on mobile devices with limited local resources. We propose to offload VA tasks to edge servers with diverse computational capabilities. We present a "detect + track" approach with on-device object tracking and edge-assisted object detection. We formulate a long-term nonlinear integer programming to maximize the overall accuracy within detection frequency and latency constraints. We then design a queue-based online optimization algorithm to solve it: relax the original problem from the integer domain to the real domain, then employ a queue-based adaptation and randomized rounding strategy. Via rigorous proof, both dynamic regret regarding detection accuracy and the real-time requirement are ensured. Evaluation results also demonstrate the effectiveness of our approach.

关键词： Accuracy image edge detection Visual analytics Signal processing algorithms Streaming media Signal processing Real-time systems Servers Speech processing Optimization

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Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking Neural Networks with Dynamic Time Steps for Vision Transformers

Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking N...

引用

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

作者： Gourav Datta Zeyu Liu Anni Li Peter A. Beerel Dept. of Electrical Computer & Systems Engineering Case Western Reserve University Cleveland USA Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los Angeles USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN). These algorithms, when applied to the recently spotlighted vision transformers (ViT), either require a large number of time steps or fail to converge. Based on the analysis of the histograms of the ANN and SNN activation maps, we hypothesize that each ViT block has a different sensitivity to the number of time steps. We propose a novel training framework that dynamically allocates the number of time steps to each ViT module depending on a trainable score assigned to each timestep. In particular, we generate a scalar binary time step mask that filters spikes emitted by each neuron in a leaky-integrate-and-fire (LIF) layer. The resulting SNNs have high activation sparsity and require only accumulate operations (AC), except for the input embedding layer, in contrast to expensive multiply-and-accumulates (MAC) needed in traditional ViTs. This yields significant improvements in energy efficiency. We evaluate our training framework and resulting SNNs on image recognition tasks including CIFAR10, CIFAR100, and imageNet with different ViT architectures. We obtain a test accuracy of 95.97% with 4.97 time steps with direct encoding on CIFAR10.

关键词： Training Computer vision Sensitivity Neurons Signal processing algorithms Spiking neural networks Transformers Energy efficiency Convolutional neural networks Speech processing

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Multi-Sensor Satellite image Cloud Classification with Deep Learning

Multi-Sensor Satellite Image Cloud Classification with Deep ...

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International conference on

作者： Rakesh Asery Burungale Swapnil Birudev Pankaj Saini Bigdata Research & Supercomputing CSIR-Fourth Paradigm Institute Bangalore India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Advancements in remote sensing (RS) have greatly increased the availability of satellite imagery, enabling diverse applications. However, cloud-cover remains a significant challenge, obstructing the acquisition and analysis of optical satellite images. This necessitates advanced cloud classification techniques as a crucial preprocessing step. The proposed MobileNetV2-based convolutional neural network architecture demonstrates unprecedented cloud classification capabilities, effectively processing imagery across diverse spatial resolutions ranging from 10 to 56 meters. By leveraging sophisticated machine learning algorithms, the model transcends traditional pixel-based thresholding techniques, capturing complex inter-pixel spatial correlations with remarkable precision. Experimental validation across heterogeneous datasets including IRS (AWiFS, LISS-iii), Landsat, and Sentinel platforms reveals exceptional performance metrics, consistently achieving classification accuracies exceeding 99.94%.

关键词： Accuracy Satellites Clouds Scalability Landsat Robustness Satellite images Convolutional neural networks Spatial resolution Remote sensing

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Multi-Model Deep Learning Framework for Plant Disease Detection and Classification using YOLOv5, Xception, and DenseNet

Multi-Model Deep Learning Framework for Plant Disease Detect...

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Visual Analytics and Data Visualization (ICVADV), International conference on

作者： Kunal K. Meshram Sarika Khandelwal Jyoti Kumre Department of Computer Science and Engineering G H Raisoni College of Engineering Nagpur India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Plant diseases lead to food loss and yield reduction, which is a serious challenge to food security globally. Researchers have suggested deep learning models, which are trained using plant disease data like PlantVillage and PlantDoc, as a means of solving this issue. The traditional solutions usually do not work under practical agricultural environments. This research combines machine learning (ML) models and image processing methods for agricultural use. This study utilizes the algorithms, such as MobileNet, VGG16, YOLOv8, and Faster R-CNN, to upgrade plant disease identification and classification. Our method utilizes deep learning architectures for both object detection and plant disease classification. We offer real-time crop analysis to help farmers make informed decisions, achieve maximum productivity, and enhance sustainable agriculture. Leveraging previous work, we seek to enhance performance through DenseNet and Xception for classification and YOLOv5 for object detection. We also use a Flask-based, easy-to-use front-end for authentication-based client testing.

关键词： YOLO Deep learning Productivity Plant diseases image processing Visual analytics Food security Metadata Real-time systems Testing

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A Comprehensive Review of Hybrid V2X Communication and Edge processing Techniques for Real-Time Emergency Vehicle Detection and Prioritization in Urban Traffic systems

A Comprehensive Review of Hybrid V2X Communication and Edge ...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Jerim Justin Lokkeshkumar R Vijayasri P Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

This review paper presents the use of hybrid Vehicle-to-Everything (V2X) communication systems to detect and prioritize emergency vehicles in congested urban areas. To reduce response times, researchers are merging edge processing with Dedicated Short-Range Communication (DSRC) and Cellular Vehicle-to-Everything (C-V2X). The technology provides extremely fast, low-latency connections, which are essential for real-time applications, by utilizing recent developments in 5G and edge computing. To increase detection accuracy in noisy urban settings, researchers are experimenting with multi-sensor fusion systems that combine GPS, acoustic sensors, and AI-powered vision. Adaptive traffic signal preemption algorithms that dynamically modify traffic flows to grant priority access to emergency vehicles are also being investigated in some research. Despite its promise, there are still obstacles to be addressed, such as interoperability problems and network congestion. However, the benefits might be enormous: quicker emergency reactions, more seamless traffic patterns, and generally more effective urban transportation systems. In the upcoming years, we should witness some fascinating breakthroughs as researchers continue to hone these technologies.

关键词： Accuracy Reviews 5G mobile communication image edge detection Urban areas Vehicular ad hoc networks Real-time systems Dedicated short range communication Vehicle-to-everything Edge computing

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Learning to Reconstruct Signals With Inexact Sensing Operator via Knowledge Distillation

Learning to Reconstruct Signals With Inexact Sensing Operato...

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

作者： Roman Jacome Leon Suarez Romario Gualdrón-Hurtado Luis Gonzalez Henry Arguello Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga Colombia Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In computational optical imaging and wireless communications, signals are acquired through linear coded and noisy projections, which are recovered through computational algorithms. Deep model-based approaches, i.e., neural networks incorporating the sensing operators, are the state-of-the-art for signal recovery. However, these methods require exact knowledge of the sensing operator, which is often unavailable in practice, leading to performance degradation. Consequently, we propose a new recovery paradigm based on knowledge distillation. A teacher model, trained with full or almost exact knowledge of a synthetic sensing operator, guides a student model with an inexact real sensing operator. The teacher is interpreted as a relaxation of the student since it solves a problem with fewer constraints, which can guide the student to achieve higher performance. We demonstrate the improvement of signal reconstruction in computational optical imaging for single-pixel imaging with miscalibrated coded apertures systems and multiple-input multiple-output symbols detection with inexact channel matrix.

关键词： Wireless communication Computational modeling Symbols Optical imaging Signal reconstruction Robustness Sensors Speech processing Knowledge transfer image reconstruction

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