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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Harnessing Deep Learning Techniques for Lung Disease Diagnosis: A Comparative Study of CNN, Transformer and Mamba Models

Harnessing Deep Learning Techniques for Lung Disease Diagnos...

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Chenkai Tang Leicester International Institute Dalian University of Technology Panjin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Pneumonia, an infectious lung condition caused by bacteria, viruses, or other microorganisms, significantly impacts both pediatric and geriatric populations. The COviD-19 pandemic has underscored the necessity for swift and accurate diagnostic tools, with over 760 million infections reported. This study investigates the role of artificial intelligence in lung X-ray image classification by comparing the performance of three Convolutional Neural Networks, two Transformer models, and the vision Mamba model using a standardized dataset. The study’s objectives were to (1) identify the most effective models, (2) examine the impact of initializing models using transfer learning, and (3) evaluate the vision Mamba model’s performance relative to conventional models. Using a Kaggle dataset containing four X-ray image categories—COviD-19, normal, lung opacity, and viral pneumonia—six models were trained and tested with and without transfer learning. Results indicated that the Swin Transformer model outperformed others, and transfer learning significantly enhanced model accuracy. Although the vision Mamba model exhibited lower accuracy, its computational efficiency highlights its potential utility. This study provides critical insights into artificial intelligence applications in lung disease diagnosis, supporting clinicians with accurate diagnostic tools.

关键词： Deep learning Pneumonia Accuracy Computational modeling Lungs Transfer learning Transformers Convolutional neural networks X-ray imaging image classification

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Formation of an algorithm for increasing the accuracy of restoring the boundaries of objects obtained to create 3D structures 4

Formation of an algorithm for increasing the accuracy of res...

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conference on Artificial Intelligence and machine Learning in Defense applications IV

作者： Semenishchev, Evgenii Zelensky, Aleksandr Zhdanova, Marina Ilyukhin, Yury Sizyakin, Roman Voronin, viacheslav Tula State Univ TulSU Lab Cognit Technol & Simulat Syst 92 Sq Lenina Tula 300012 Tula Region Russia Moscow State Tech Univ STANKIN Ctr Cognit Technol & Machine Vis 1a Vadkovsky Moscow 127055 Russia

ISBN: (纸本)9781510655560

The article proposes an approach to improve the accuracy of restoring the boundaries of objects obtained to create 3D structures by analyzing data obtained by a machine vision system. At the first stage, the operation of reducing the number of color gradients is performed, the technique allows you to combine similar values into common enlarged structures. This operation allows you to simplify the analyzed objects, since small details are not important. In parallel with the first operation of denoising is performed. The paper proposes the application of the multicriteria processing method with the possibility of smoothing locally stationary sections and preserving the boundaries of objects. As an algorithm for strengthening the boundaries of objects, a modification of the combined multi-criteria method is used, which makes it possible to reduce the effect of salt/pepper noise and impulse failures, as well as to strengthen the detected boundaries of objects. The resulting images with enhanced boundaries are fed to the input of the block for constructing three-dimensional objects. The data obtained by both a stereo pair and a camera based on 3D construction using structured light were used in the work. On a set of synthetic data simulating the work in real conditions, the increase in the efficiency of the system using the proposed approach is shown. Based on field data under conditions of interfering factors in the form of dust/fog, the applicability of the proposed approach for solving problems of increasing the accuracy of restoring the boundaries of objects obtained to create three-dimensional structures is shown. images of simple shapes are used as analyzed objects.

关键词： boundaries preprocessing machine vision 3D structures image processing

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Development and future of information hiding in image transformation domain: A literature review 22

Development and future of information hiding in image transf...

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4th International conference on image processing and machine vision, IPMV 2022

作者： Yang, Yuer College Of Cyber Security Jinan University China School Of Economics Jinan University China

ISBN: (纸本)9781450395823

Information hiding technology is a technique to hide meaningful information in the public carrier information. When data elements are becoming more and more important, information hiding technology has a better performance than traditional encryption and decryption technologies such as single table substitution and virginia cipher. Since it contains redundant information, image is a common carrier for information hiding among many carrier types. As one of the primary means of information hiding technology, image transform-domain information hiding technology has been widely studied and used in academic and industrial fields. Information hiding in the image transform domain improves security and robustness effectively. This paper mainly introduces the concepts, principles, processes, mainstream algorithms and applications of image transform-domain information hiding techniques. A possible general future development of it is also depicted. © 2022 ACM.

关键词： image enhancement

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Research on Intelligent Driving System of Electric Vehicle Path Planning Based on Computer Deep Reinforcement Learning

Research on Intelligent Driving System of Electric Vehicle P...

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image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Ma Wei Hubei Industrial Polytechnic Shiyan Hubei China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

A human-machine sharing control method based on the automatic driving of electric vehicles is proposed. The system consists of steering system, braking system, vehicle acceleration system, PLC system and external environment sensing system. This paper uses binocular stereo vision and multithreading technology to realize the sense of the surrounding environment. Siemens S7200PLC is used as controller to realize automatic control and human-machine conversion. The goal of this project is the shortest path, the shortest waiting time for charging, the minimum additional energy consumption and the maximum battery life. A multi-dimensional path planning method based on path selection, battery discharge threshold and charge stop time is constructed with the restrictions of path selection, battery discharge threshold and charge stop time. The calculation results show that the proposed algorithm can provide the user with the optimal energy consumption and mileage. The designed low speed electric vehicle intelligent driving system control is relatively stable. The system is relatively inexpensive and reliable.

关键词： Energy consumption Human-machine systems Steering systems Electric vehicles Path planning Discharges (electric) Sensors Batteries Stereo vision Safety

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11th International conference on Robotics, vision, Signal processing and Power applications, RoviSP 2021

11th International Conference on Robotics, Vision, Signal Pr...

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11th International conference on Robotics, vision, Signal processing and Power applications, RoviSP 2021

ISBN: (纸本)9789811681288

The proceedings contain 166 papers. The special focus in this conference is on Robotics, vision, Signal processing and Power applications. The topics include: Development of COviD-19 Prediction Models from Chest X-Ray Using Transfer Learning;behavioural Study of Constrained Convolutional Neural Network on image Splicing Classification of Various Datasets;survey on Loss Function for Face Recognition;a Maximum Power Point Tracking Using Levy Particle Swarm;Sensing and Control of Autonomous Driverless Car on Road (SCAD);impact of Similarity Measure Functions on the Performance of Coherent Filtering Detection;UAV Control System with Time to Collision (TTC) Prediction Capability;AFSA-SLnO Variants for Enhanced Global Optimization;A Fast and Flexible Turret Based Automated vision Inspection (Avi) to Inspect 6 Sides of Small Discrete Components;development of Morphological image processing Learning Module for Mobile Application;Design a RFID Tag Antenna for Wearable Application System;automatic Detection of Cerebral Ischemia (Cerebral Occlusion) by Using Frame Differencing Method;a Hybrid Deep Learning Model for Face Sketch Recognition;Point Cloud Generation with Low-Cost Camera for visual SLAM;Development of Drowning Detection Algorithm for USV;latest Trends of Integration of Gas Leakage and Fire Detection Using IoT: A Survey;Safety Device for Runners (SDR) Using Arduino Microcontroller;analytical Subdomain Model for Double-Stator Permanent Magnet Synchronous machine;Dorsal Hand Vein Segmentation Using Vein-Generative Adversarial Network (V-GAN) Model;handling Illusive Text in Document to Improve Accuracy of Plagiarism Detection Algorithm;PQ Control Strategy in Single-Phase Inverter for Grid-Connected Photovoltaic Energy System Under Linear and Nonlinear Loads;preface;inchworm-Inspired Semi-autonomous Pole-Climbing Robot.

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Review on applications of Quantum image processing 13

Review on Applications of Quantum Image Processing

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13th International conference on Digital image processing (ICDIP)

作者： Kalonia, Mohini Gupta, Trasha Delhi Technol Univ New Delhi India

ISBN: (数字)9781510646018

ISBN: (纸本)9781510646018

Quantum image processing and its various applications developed in the last few years. These applications utilize different quantum algorithms for higher accuracy and reduced time complexity compared to classical computing. This paper reviews some applications of quantum image processing like edge extraction, Security, and denoising of images. applications like feature extraction, machine vision, and feature detection use edge extraction. In this paper, we observe the field of Security by dividing it into two sub-areas, i.e., cryptography and information hiding, used for authentication purposes. For removal of noise signals from input images, the method of denoising is used.

关键词： Quantum image processing Quantum Computing Qubit edge extraction QSobel denoising Security encryption watermarking

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A Novel Salp Swarm Algorithm with Attention-Densenet Enabled Plant Leaf Disease Detection and Classification in Precision Agriculture

A Novel Salp Swarm Algorithm with Attention-Densenet Enabled...

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2022 International conference on Advanced Computing Technologies and applications, ICACTA 2022

作者： Nirmala Devi, S. Muthukumaravel, A. Bharath Institute of Higher Education and Research Department of Computer Applications Chennai India Bharath Institute of Higher Education and Research Faculty of Arts and Science Chennai India

ISBN: (纸本)9781665495158

Recent technological advancements enable precision agriculture to improve crop productivity and quality. Since plant diseases mainly affect crop production and reduce profit, necessary tools are needed to detect plant diseases at an earlier stage. Automatic detection of plant diseases becomes essential to identify the occurrence of plant diseases and take remedial actions. The latest advancements of computer vision and artificial intelligence techniques can be used to design effective plant leaf disease detection models. This paper presents a novel salp swarm algorithm with attention-DenseNet enabled plant leaf disease detection and classification (SSADN-PLDDC) technique for precision agriculture. The major intention of the SSADN-PLDDC technique is to recognize the presence of plant leaf diseases using computer vision and image processing methods. The SSADN-PLDDC technique initially employs Gabor filtering to pre-process the input images. In addition, SSA with extreme learning machine (ELM) model is utilized as an image classification technique where the parametersinvolved in the ELM are optimally adjusted by using SSA. The experimental result analysis of the SSADN-PLDDC technique is validated using benchmark dataset and the experimental results reported the enhanced outcomes of the SSADN-PLDDC technique over the recent approaches. © 2022 IEEE.

关键词： Deep learning

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Pose-graph via Adaptive image Re-ordering 33

Pose-graph via Adaptive Image Re-ordering

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33rd British machine vision conference Proceedings, BMVC 2022

作者： Barath, Daniel Noskova, Jana Eichhardt, Ivan Matas, Jiri Computer Vision and Geometry Group ETH Zurich Switzerland Visual Recognition Group FEE CTU in Prague Czech Republic Algorithms and Applications Department Eotvos Lorand University Budapest Hungary

We introduce novel methods that speed up the pose-graph generation for global Structure-from-Motion algorithms. We replace the widely used "accept-or-reject" strategy for image pairs, where often thousands of RANSAC iterations are wasted on pairs with low inlier ratio or on non-matchable ones. The new algorithm exploits the fact that every unsuccessful RANSAC iteration reduces the probability of an image pair being matchable, i.e., it reduces its inlier ratio expectation. The method always selects the most promising pair for matching. While running RANSAC on the pair, it updates the distribution of its inlier ratio probability in a principled way via a Bayesian approach. Once the expected inlier ratio drops below an adaptive threshold, the method puts back the pair in the processing queue ordered by the updated inlier ratio expectations. The algorithms are tested on more than 600k real image pairs. They accelerate the pose-graph generation by an order-of-magnitude on average. The source code is available at https://***/danini/pose-graph-creation. © 2022. The copyright of this document resides with its authors.

关键词： Iterative methods

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Advanced Object Detection and Pose Estimation with Hybrid Task Cascade and High-Resolution Networks

Advanced Object Detection and Pose Estimation with Hybrid Ta...

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Yuhui Jin Yaqiong Zhang Zheyuan Xu Wenqing Zhang Jingyu Xu California Institute of Technology Pasadena USA University of Michigan Ann Arbor Ann Arbor USA University of Washington Sunnyvale USA Washington University in St. Louis Louis USA Northern Arizona University Arizona USA

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In the field of computer vision, 6D object detection and pose estimation are critical for applications such as robotics, augmented reality, and autonomous driving. Traditional methods often struggle with achieving high accuracy in both object detection and precise pose estimation simultaneously. This study proposes an improved 6D object detection and pose estimation pipeline based on the existing 6D-VNet framework, enhanced by integrating a Hybrid Task Cascade (HTC) and a High-Resolution Network (HRNet) backbone. By leveraging the strengths of HTC’s multi-stage refinement process and HRNet’s ability to maintain high-resolution representations, our approach significantly improves detection accuracy and pose estimation precision. Furthermore, we introduce advanced post-processing techniques and a novel model integration strategy that collectively contribute to superior performance on public and private benchmarks. Our method demonstrates substantial improvements over state-of-the-art models, making it a valuable contribution to the domain of 6D object detection and pose estimation.

关键词： Computer vision Accuracy image processing Pose estimation Pipelines Object detection Benchmark testing Robustness Autonomous vehicles Augmented reality

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Employing Satellite imagery on Investigation of Convolutional Neural Network image processing and Poverty Prediction Using Keras Sequential Model 5

Employing Satellite Imagery on Investigation of Convolutiona...

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5th IEEE International conference on Cybernetics, Cognition and machine Learning applications, ICCCMLA 2023

作者： Kumar, Voruganti Naresh Sonth, Mahesh V Mahvish, Arfa Koppula, vijaya kumar Anuradha, B. Reddy, L Chandrasekhar CMR Technical Campus Department of CSE Telangana Hyderabad India CMR Technical Campus Department of ECE Telangana Hyderabad India CMR Technical Campus Department of IT Telangana Hyderabad India CMR College of Engineering & Technology Department of CSE Telangana Hyderabad India CMR College of Engineering & Technology Department of CSD Telangana Hyderabad India

ISBN: (纸本)9798350338287

The primary objective of the proposed model is that the most of the world's poorest individuals reside in areas where national domestic assessments are used to gather info on deficiency. It is difficult to acquire current and precise data because it takes a lot of assets to conduct these surveys. Due to advancements in computer vision and the widespread availability of abundant data sources such as satellite images captured during daylight and nocturnal lighting, a practical solution to the problem of data scarcity is now feasible. This study is going to expand on previous research by processing daytime satellite photos and nighttime lights employing machine learning techniques for the purpose predict, the distribution of poverty at the local level in countries utilizing new technology and modern data sources. Innovative and fascinating possibilities, such as the detailed classification of specific objects on a per-pixel basis, have become feasible due to the availability of aerial satellite data. This study demonstrates the efficacy of a convolutional neural network (CNN) in efficiently and accurately classifying individual pixels inside satellite imagery of a compact urban area. The broad segmentation is then refined by incorporating the expected detailed pixel classifications, enhancing the overall accuracy and speed of the classification process. Examined and assessed are the several architectural decisions made for the CNN architecture. The five different types of terrain, ground cover, roads, structures, and water are all physically categorized and assigned to the study area's land mass. The correctness of classification is compared with other per-pixel classification methods for contrast tests conducted on different terrain areas with a comparable number of categories. Convolutional Neural Networks (CNNs) have demonstrated their efficacy in effectively addressing the task of segmenting and detecting objects in remote sensing data. This is clear from the compl

关键词： Satellite imagery

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