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

共 13605 条记录，以下是171-180 订阅

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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...

引用

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

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

引用

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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Proceedings of SPIE-IS&T Electronic Imaging - image processing: algorithms and systems iv

Proceedings of SPIE-IS&T Electronic Imaging - Image Processi...

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Proceedings of SPIE-IS and T Electronic Imaging - image processing: algorithms and systems iv

The proceedings contain 40 papers from the Proceedings of SPIE-IS&T Electronic Imaging - image processing: algorithms and systems iv. The topics discussed include: system architecture for the digital recovery of shredded documents;digital image comparison using feature extraction and luminance matching;sampling the parameter domain of image series;a multisensor system for texture-based high-speed hardwood lumber inspection;nonlinear anisotropic diffusion filtering of three-dimensional image data from two-photon microscopy;analysis of the sigma filter using robust estimation;and registration techniques for speckle suppression in 2D LADAR image sequences.

关键词： image analysis

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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 ...

引用

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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Lossless Medical image Compression Using ROI Segmentation with Deep Learning Techniques

Lossless Medical Image Compression Using ROI Segmentation wi...

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International conference on Inventive Computation Technologies (ICICT)

作者： Velidi Sai Baba Molaka Ramakrishna Reddy Kombathula Lakshmi Karthik Gandham Lakshmi Tharun Katreddy Surya Mithra Dept. of Information Technology Sagi Rama Krishnam Raju Engineering College Bhimavaram Andhra Pradesh India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

With The increasing use of medical imaging has put a greater requirement on fast and efficient storage and transmission. Compression algorithms need to strike a balance between reducing file sizes without distortion of valuable diagnostic information. In this paper, a hybrid lossless compression method is proposed that uses deep learning-based ROI segmentation. A U-Net model is used specifically to segment the cardiac anatomy on CT scans accurately. Lossless compression is used to preserve large diagnostic areas, and lossy compression is used to compress small areas. The approach strikes an effective balance between the two to yield high compression ratio without compromising medical integrity. Experimental evidence proves its efficacy in minimizing storage needs without compromising diagnostic accuracy. This work recognizes the potential of deep learning for medical image processing and paves the way for upcoming AI-based compression methods.

关键词： Heart Deep learning Learning systems image segmentation image coding Accuracy Computed tomography Transform coding Memory Medical diagnostic imaging

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Vehicle Registration Plate Recognition Using YoloV8 and IOT

Vehicle Registration Plate Recognition Using YoloV8 and IOT

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AIML-Applications for Engineering & Technology (ICAET), International conference on

作者： Juber Nadaf Amol Kadam Pramod Jadhav Prasad Kadam Vinod Patil Milind Gaikwad Department of Computer Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of CSBS Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of E & TC Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of Information Technology Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Number Plate recognition or the vehicle License Plate recognition is an interesting topic of research in smart cities which utilizes image processing techniques. Since the number of cars is rising at an exponential rate, automated systems are supposed to store vehicle data for a variety of applications. In this intended algorithm, an efficient technique has been identified for recognition of different vehicle number registration plates. Through this technique, we can easily deal with problems like low light, irregular font number plates, wrong angles. We use the method of Optical Character Recognition to identify characters that are extracted from the registration plate. Even though this project works efficiently for common use cases, it has its own drawbacks and limitations even though we are not using deep learning or any other machine learning algorithms. One of the important steps in vehicle plate recognition is the character recognition which is a technique to obtain individual text characters. YOLOv8 (You Only Look Once), and Darknet- 53 are the methods that is used to extract the features of the data. In this model, the input data is the image of the vehicle that is captured from the camera sensors or cell phones and later cropped to display only the registration plate. We undergo data-pre-processing to improve the quality or the pixels of image on the number plate. Testing is done using two different models one is the model which is obtained with more pre-processing data and the other obtained without any pre-processing data. We evaluate two datasets of number plate images: one with no interference and one where the colour intensity of the image has been decreased to test, the accuracy of the model

关键词： Deep learning YOLO Training image recognition Accuracy Feature extraction Data models Internet of Things Character recognition License plate recognition

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FPGA based Vehicle Collision Avoidance and Accident Warning using Sobel Operation and Manhattan Distance Metrics

FPGA based Vehicle Collision Avoidance and Accident Warning ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Vishal Pranao Amarnath Kumar Sanjana Bhattacharjee Harsh Kumar Rupam Mal Vaarshini Ravichandran K. Sivasankaran School of Electronics Engineering VIT University Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

A collision avoidance system in vehicles is a safety technology which is designed to prevent or reduce accidents caused due to any obstacles such as other vehicles or pedestrians. It utilizes cameras, or other sensors to monitor the vehicle's surroundings and alerting the driver to any potential danger. Advanced driver assistance systems (ADAS) and collision avoidance works hand in hand as this enhances overall vehicle safety, reduces any driver errors and also contributes to overall safety on road. This paper presents the design and implementation of a real-time collision avoidance system using Field-Programmable Gate Arrays (FPGAs). The proposed system processes the video input from a vehicle's camera and converts the video's frame to grayscale and applies Sobel edge detection on it which helps to calculate the distance between the two vehicles using Manhattan distance formula and hence effectively detects any potential collision. It utilizes FPGA's parallel processing capabilities, reconfigurability and low power to enhance the efficiency and speed of the collision detection and response in autonomous vehicles. The experiments were performed real-time on Altera Cyclone iv-E FPGA which demonstrated effectiveness of the system as the results indicated high accuracy and reliability in distance measurement and collision warning by analyzing parameters like dynamic color variation and background noise reduction for improving the safety and performance in autonomous vehicles.

关键词： image color analysis image edge detection Streaming media Parallel processing Gray-scale Real-time systems Collision avoidance Field programmable gate arrays Autonomous vehicles Vehicles

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