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

共 14657 条记录，以下是471-480 订阅

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A PROPER ORTHOGONAL DECOMPOSITION APPROACH FOR PARAMETERS REDUCTION OF SINGLE SHOT DETECTOR NETWORKS 29

A PROPER ORTHOGONAL DECOMPOSITION APPROACH FOR PARAMETERS RE...

引用

IEEE International conference on image processing (ICIP)

作者： Meneghetti, Laura Demo, Nicola Rozza, Gianluigi SISSA Math Area MathLab Via Bonomea 265 I-34136 Trieste Italy

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

As a major breakthrough in artificial intelligence and deep learning, Convolutional Neural Networks have achieved an impressive success in solving many problems in several fields including computer vision and image processing. Real-time performance, robustness of algorithms and fast training processes remain open problems in these contexts. In addition object recognition and detection are challenging tasks for resource-constrained embedded systems, commonly used in the industrial sector. To overcome these issues, we propose a dimensionality reduction framework based on Proper Orthogonal Decomposition, a classical model order reduction technique, in order to gain a reduction in the number of hyperparameters of the net. We have applied such framework to SSD300 architecture using PASCAL VOC dataset, demonstrating a reduction of the network dimension and a remarkable speedup in the fine-tuning of the network in a transfer learning context.

关键词： image processing Object Detection Dimensionality Reduction Convolutional Neural Network

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AI Mediated Surface Waste Auto-Detection on Water Bodies Using Cutting Edge Technology

AI Mediated Surface Waste Auto-Detection on Water Bodies Usi...

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2024 IEEE International conference on Cognitive Computing and Complex Data

作者： Choudhary, Vijaya Guha, Paramita Archana Dey, Sharmistha IILM Univ Sch Comp Sci & Engn Plot 16-18Knowledge Pk 2 Greater Noida Uttar Pradesh India Acad Sci & Innovat Res AcSIR CSIR Natl Phys Lab Dr KS Kushnan Marg New Delhi India OP Jindal Global Univ Sonipat India Galgotias Univ Sch Comp Sci & Applicat Greater Noida India

ISBN: (纸本)9798350352900;9798350352894

The proliferation of surface waste in water bodies poses significant environmental and ecological challenges. Traditional methods of waste detection are often labor-intensive and limited in scope. This paper presents a novel approach to surface waste detection using artificial intelligence (AI) and advanced imaging technologies. Leveraging cutting-edge techniques such as deep learning algorithms, high-resolution satellite imagery, and real-time data processing, our system offers an automated solution for identifying and monitoring waste in water bodies. We developed a robust AI model trained on diverse datasets, including satellite and drone-captured images, to detect various types of surface waste with high accuracy. The system integrates real-time processing capabilities to provide timely alerts and actionable insights for environmental management. Evaluation results demonstrate that our approach significantly improves detection accuracy and operational efficiency compared to conventional methods. This research contributes to the advancement of smart environmental monitoring systems and offers a scalable solution for mitigating the impact of surface waste on aquatic ecosystems.

关键词： Artificial Intelligence Garbage detections Convolutional neural network river research image processing computer vision

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Improving image Clarity with Artificial Intelligence-Powered Super-Resolution Methods 1

引用

3rd International conference on Mechanical and Energy Technologies, ICMET 2023

作者： Malathy, V. Poornima, M. Varun, V.L. Venugopal, Padmaja Acharjee, Purnendu Bikash Krishnaveni, S. School of Engineering SR University Telangana Warangal India Department of Mathematics SJB Institute of Technology Bengaluru India Computer Science CHRIST University Pune Lavasa Campus Lavasa India Department of Management Studies Adithya Institute of Technology Coimbatore Kovilpalayam India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

Super-resolution has advanced significantly in the last 20 years, particularly with the application of deep learning methods. One of the most important image processing methods for boosting an image's resolution in computer vision is image super-resolution besides providing an extensive overview of the most recent developments in artificial intelligence and deep learning for single-image super-resolution. This study delves into the subject of image enhancement by investigating sophisticated AI-based super-resolution techniques. High-quality photographs have become more and more in demand in a variety of industries recently, including medical imaging, satellite imaging, entertainment, and surveillance. Pixilation reduction and detail preservation are two areas where traditional image enhancing techniques fall short. Artificial intelligence has demonstrated amazing promise in addressing these issues, especially with regard to Deep Learning models. The applications, benefits, and difficulties of modern super-resolution techniques are thoroughly examined in this work. We also suggest new approaches and push the limits of image enhancement by experimenting with state-of-the-art artificial intelligence algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Fired:An image-Based Fire Detector for Smart Homes using Machine Learning algorithms

Fired:An Image-Based Fire Detector for Smart Homes using Mac...

引用

2024 International conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Srinath, G.M. Yashaswini, K.M. Sahana Shree, N. Supraja, B. Varshitha, V. Arfa Thareen, K. Visvesvaraya Technological University Department of Computer Science and Engineering Chickballapur India

ISBN: (纸本)9798350359688

Smart homes are to be protected from fire hazards which is a crucial safety concern. Existing ways of detecting fire is time consuming, hence causing maximum injuries and financial loss, so we have come up with an effective solution for detecting fire in smart homes using image processing and Machine Learning techniques. FireD extracts the features from photos and captured video frames using Convolutional Neural Network (CNN) and in turn sends them to Neural Network which implements clustering algorithm known as Yolov5 to ensure the ability to classify images as Fire and Non-Fire. Once the fire is detected, model will send the images to Heroku cloud which serves as container based cloud Platform as a Service (PaaS) to access, deploy and manage the images. On the detection of fire Notification is sent to Telegram through telegram bot. Overall performance data from model indicates that FireD is likely to perform well in real-world scenarios. Therefore, FireD is more reliable than other existing systems since FireD application is pushed to the docker container with all the required libraries and files. The application can be accessed and implemented easily. © 2024 IEEE.

关键词： Platform as a Service (PaaS)

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Research on algorithms of Computer image Intelligent Recognition and Deep Learning 2

Research on Algorithms of Computer Image Intelligent Recogni...

引用

2nd International conference on Signal image processing and Communication, ICSIPC 2022

作者： Shao, Zheneenji Zhou, Jingxuan Sichuan University Chengdu China Dalian University of Technology Dalian China

ISBN: (纸本)9781510657694

In recent years, with the rapid development of science and technology in my country, people's requirements for computer information technology have become higher and higher. In order to better explore new things, engineers and technicians need to continuously optimize computer information processing systems to help people better solve practical problems. The article mainly analyzes the development process of computer intelligent image recognition algorithms, expounds the computer intelligent image recognition algorithm, and finally puts forward the technical application of computer intelligent image recognition algorithm, which provides a certain research foundation for related staff. © 2022 SPIE.

关键词： image recognition

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Thorax Disease Classification Based on the Convolutional Network SqueezeNet 12

Thorax Disease Classification Based on the Convolutional Net...

引用

12th IEEE International conference on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Avanzato, Roberta Beritelli, Francesco University of Catania Dieei Catania95125 Italy

ISBN: (纸本)9798350358056

Chest radiography allows a detailed inspection of a patient's thorax via an imaging modality, but requires specialized training for proper interpretation. With the advent of high performance general purpose image analysis and computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. In this paper, we propose a classification algorithm distinguishing between chest radiographs performed on people with or without lung disease. The dataset used as the input to the SqueezeNet neural network is Chest X-ray. The only pre-processing phase applied to the dataset is the resize to 224×224 and the conversion to grayscale. The accuracy percentage obtained during the network testing phase is 94%. © 2023 IEEE.

关键词： computer vision Convolutional neural network deep learning healthcare image processing

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image Interpolation with Edges Preserving and Implementation on the Real ADAS Platform 5

Image Interpolation with Edges Preserving and Implementation...

引用

5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Kelava, Bozidar Vranjes, Mario Vranjes, Denis Lukac, Zeljko TTTechAuto CEE Osijek Croatia Fac Elect Engn Comp Sci & Informat Technol Osijek Croatia Fac Tech Sci Novi Sad Serbia

ISBN: (纸本)9781665462198

To save transmission, processing and memory resources in Advanced Driver Assistance systems (ADAS), it is often necessary to reduce the image resolution. Sometimes it is necessary to increase it after the transmission. Both resolution changes involve an image interpolation process. This paper describes implementation for three well-known interpolation methods, nearest neighbour interpolation (NN), bilinear interpolation (BL) and bicubic interpolation (BC), on a real automotive AMV ALPHA platform, using multiple processors on the same System on Chip (SoC). Implementation was done using C programming language and Vision Software Development Kit (VSDK). Specific attention is given to the optimal distribution of tasks to the certain processor. The results have shown that, on the real automotive AMV ALPHA platform, BL interpolation achieves the best trade-off between the quality of interpolated image for the usage in automotive image-processing based algorithms and execution time, especially for the algorithms where the lower frame rate is acceptable (like surround-view, park assist, etc.).

关键词： image interpolation ADAS real-time

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Deep Learning-Based image processing for Defect Detection in Electronic Components Manufacturing

Deep Learning-Based Image Processing for Defect Detection in...

引用

2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Mathivanan, B. Maruthupandi, J. Lakshmi, Namala Vijaya Suvitha, S. Karthik, K. Swaroop, Anand Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore641022 India New Horizon College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560103 India Kkr & Ksr Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh 522017 India S.A. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600077 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Institute of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow226021 India

ISBN: (纸本)9798331543617

In electronic factors manufacturing, icing product quality through disfigurement discovery is pivotal for maintaining trustability and performance norms. This exploration investigates the operation of deep literacy ways in image processing for disfigurement discovery. Deep literacy algorithms, particularly convolutional neural networks(CNNs), have shown remarkable success in automated image analysis tasks. This study explores their effectiveness in relating blights similar to face abnormalities, cracks, or indecorous assembly in electronic factors. By using annotated datasets and advanced neural network infrastructures, the proposed approach aims to enhance delicacy and effectiveness in disfigurement discovery compared to traditional styles. The methodology involves preprocessing raw images, training CNN models on labeled data, and validating their performance through rigorous testing and evaluation. Results demonstrate the eventuality of deep literacy to revise disfigurement discovery processes in manufacturing, offering more dependable examinations that contribute to bettered product quality and reduced product costs. This exploration contributes precious perceptivity to the integration of AI technologies for enhancing quality control in artificial settings. © 2024 IEEE.

关键词： Smart manufacturing

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image Classifications Methods Analysis with Differen Methods to for Identifying best image Layout with High Resolution

Image Classifications Methods Analysis with Differen Methods...

引用

2023 International conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Mahesh, T.R. Vivek, V. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350322309

This researcher investigated effective much eye opening and pattern detection algorithms. Finally, this article used two frameworks to argue that geospatial investigation systems for patterns are necessary. One of most popular method for analyzing photographs using image processing and analysis math is probably visual configuration methods. imagery description is also necessary for the evaluation of remote sensing tools used for thorough land investigation. The three main image configuration techniques that are often used for far detection are opinion piece visual analysis, controlled groupings, and solos visual categorization. The approach used in these image layout techniques is combined in the present study. The present study also briefly summarizes each approach's use in exploring distant sensing info while also outlining the steps for each way using stream outlines and frameworks. Generally speaking, a quick overview of image layout techniques is included in the test. © 2023 IEEE.

关键词： image classification

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Multiple image Augmentations for Enhanced YOLO-based Traffic Sign Detection 2

Multiple Image Augmentations for Enhanced YOLO-based Traffic...

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2nd International conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Long, Pinzhuang Hu, Yichen University of Nottingham Ningbo China Electrical and Electronic Engineering Ningbo315100 China University of Nottingham Mechanical Manufacturing and Material Engineering NottinghamNG7 2RD United Kingdom

ISBN: (纸本)9798350331417

The rapid growth of the automotive industry necessitates the implementation of robust passenger safety measures, especially in the domain of traffic sign recognition for autonomous driving. This study introduces an effective approach to enhance traffic sign detection, with a specific emphasis on the You Only Look Once (YOLO) architecture. The paper addresses the challenges associated with accurately localizing traffic signs by employing diverse image augmentation techniques, including flipping, color inversion, Gaussian blur, affine transformation, and brightness adjustment. Despite computational challenges, particularly in light of YOLOv5's superior accuracy and efficiency, there is still room for further refinement to meet the stringent requirements of autonomous driving research. This research underscores the potential of image augmentation in advancing traffic sign recognition, demonstrating its pivotal role in the development of intelligent transportation systems. In comparison to existing algorithms as YOLO-V3 and YOLO-V5, the proposed technique might successfully reach higher accuracy and less computational power and processing time. © 2023 IEEE.

关键词： Autonomous driving Data augment Traffic signs detection YOLO architecture Introduction

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