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检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

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CheXNet for the Evidence of Covid-19 Using 2.3K Positive Chest X-rays 4th

CheXNet for the Evidence of Covid-19 Using 2.3K Positive ...

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4th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2021

作者： Santosh, Kc Ghosh, Supriti 2AI: Applied Artificial Intelligence Research Lab Computer Science University of South Dakota VermillionSD57069 United States

ISBN: (纸本)9783031070044

CheXNet is not a surprise for Deep Learning (DL) community as it was primarily designed for radiologist-level pneumonia detection in Chest X-rays (CXRs). In this paper, we study CheXNet to analyze CXRs to detect the evidence of Covid-19. On a dataset of size 4, 600 CXRs (2, 300 Covid-19 positive cases and 2, 300 non-Covid cases (Healthy and Pneumonia cases)) and with k(=5) fold cross-validation technique, we achieve the following performance scores: accuracy of 0.98, AUC of 0.99, specificity of 0.98 and sensitivity of 0.99. On such a large dataset, our results can be compared with state-of-the-art results. © 2022, Springer Nature Switzerland AG.

关键词： Large dataset

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Character recognition of Vedic Sanskrit using Deep Learning Algorithms 3

Character Recognition of Vedic Sanskrit using Deep Learning ...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Pandey, Mrinal Pandey, Rashmikiran Nazarov, Alexey Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781665462006

One of the applications of deep learning is deciphering the unscripted text over the walls and pillars of historical monuments is the major source of information extraction. this information gives us an idea about the art and the culture of that respective era. In this paper we proposed a model for digital recognition of textual characters written in Sanskrit language using the 4-fold Convolutional Neural Network (CNN) architecture. At the first stage we remove the noise and subsequently segmentation of characters from the input image is performed, later we convert the image into binary image format by the help of image processing techniques. the research is performed over the Devanagari script which was developed during 1st to the 4th century CE and is common to many languages from the Indian subcontinent. © 2022 IEEE.

关键词： Character recognition

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Role of thermal images in Various Applications of computer vision

Role of Thermal Images in Various Applications of Computer V...

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Intelligent Engineering and Management (ICIEM), international conference on

作者： Ravina Gupta Sarika Jain Manoj Kumar AIIT Amity University Noida India EIS University of Wollongong Dubai

Object detection is an advanced area of image processing and computer vision. Its major applications are in surveillance, autonomous driving, face recognition, anomaly detection, traffic management, agriculture etc. this paper focuses on various object detection techniques in thermal images. A thermal imaging sensor is a device that creates an image by analyzing temperature differences between different objects in a scene and detecting radiation from those objects. In recent years, many machine learning and deep learning algorithms have been used to recognize objects in thermal images. this study makes a comparison of YOLO, YOLO DarkNet, Retinex algorithm, CNN-based machine learning model Support Vector Machine (SVM), and Gaussian Mixture Model (GMM), Mixer of Gaussian (MoG), Mean Shift Approach, Faster R-CNN and Deep Neural Network along with different datasets.

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Multi-scale processing of Noisy images using Edge Preservation Losses 25

Multi-scale Processing of Noisy Images using Edge Preservati...

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25th international conference on pattern recognition (ICPR)

作者： Ofir, Nati Keller, Yosi BIU Ramat Gan Israel

ISBN: (纸本)9781728188089

Noisy image processing is a fundamental task of computer vision. the first example is the detection of faint edges in noisy images, a challenging problem studied in the last decades. A recent study introduced a fast method to detect faint edges in the highest accuracy among all the existing approaches. their complexity is nearly linear in the image's pixels and their runtime is seconds for a noisy image. their approach utilizes a multi-scale binary partitioning of the image. By utilizing the multi-scale U-net architecture, we show in this paper that their method can be dramatically improved in both aspects of run time and accuracy. By training the network on a dataset of binary images, we developed an approach for faint edge detection that works in linear complexity. Our runtime of a noisy image is milliseconds on a GPU. Even though our method is orders of magnitude faster, we still achieve higher accuracy of detection under many challenging scenarios. In addition, we show that our approach to performing multi-scale preprocessing of noisy images using U-net improves the ability to perform other vision tasks under the presence of noise. We prove it on the problems of noisy objects classification and classical image denoising. We show that multi-scale denoising can be carried out by a novel edge preservation loss. As our experiments show, we achieve high-quality results in the three aspects of faint edge detection, noisy image classification, and natural image denoising.

关键词： image denoising

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Research on Intelligent Security System of Online Car Based on computer vision 4th

Research on Intelligent Security System of Online Car Based ...

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4th international conference on Wireless Communications and Applications, ICWCA 2020

作者： Sun, Hetao Zhong, Jianyong Jiaxing Vocational and Technical College Jiaxing314036 China

ISBN: (纸本)9789811651670

the online car is the product of the "Interonline+ " era. As a new city intelligent transportation mode, it breaks the old interest pattern and the barrier of taxi industry based on franchise. In recent years, the number of online car Hailing has increased sharply. However, due to the lack of effective supervision means, security problems have gradually emerged, such as rape, robbery, intentional injury, intimidation, abuse of passengers and other incidents. In order to solve this problem, this project proposes an intelligent security system for online car Hailing based on machine vision, constructs an advanced machine vision solution framework, and introduces graphics and image processing technology and machine learning abnormal behavior intelligent warning model. At the same time, combined with the embedded technology, it can automatically warn the abnormal behavior in the online car Hailing and quickly access the manual intervention and alarm, so as to timely detect the security threats and potential dangers in the process of taking the bus, so as to realize the purpose of protecting the safety of the online car Hailing passengers. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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A Moving Target Tracking Technology for Intelligent Police Based on Kalman Filter Method

A Moving Target Tracking Technology for Intelligent Police B...

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computer vision, image and Deep Learning (CVIDL), international conference on

作者： LongFei. Dang Miao. Yang Kai. Yi Binghe. Si Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China Department of Computer Science and Technology Sichuan Police College LuZhou China Department of Criminal Technology Sichuan Police College LuZhou China

Video image analysis mainly involves the analysis and processing of moving image sequences, which usually involves several processes such as motion detection, object classification, object tracking, and behavior understanding and description. Among them, moving object detection and tracking are at the bottom level of the entire visual monitoring system, and are the most basic methods in video image analysis. they are the foundation for various subsequent advanced processing. this article proposes a target detection and tracking algorithm based on Kalman filtering, which can effectively detect and track moving objects, providing a more efficient technical means for detecting and tracking target suspects in the era of big data.

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A Mobile Food recognition System for Dietary Assessment 1

引用

21st international conference on image Analysis and processing (ICIAP)

作者： Akti, Seymanur Qaraqe, Marwa Ekenel, Hazim Kemal Istanbul Tech Univ Istanbul Turkey Hamad Bin Khalifa Univ Ar Rayyan Qatar

ISBN: (数字)9783031133213

ISBN: (纸本)9783031133213;9783031133206

Food recognition is an important task for a variety of applications, including managing health conditions and assisting visually impaired people. Several food recognition studies have focused on generic types of food or specific cuisines, however, food recognition with respect to Middle Eastern cuisines has remained unexplored. therefore, in this paper we focus on developing a mobile friendly, Middle Eastern cuisine focused food recognition application for assisted living purposes. In order to enable a low-latency, high-accuracy food classification system, we opted to utilize the Mobilenet-v2 deep learning model. As some of the foods are more popular than the others, the number of samples per class in the used Middle Eastern food dataset is relatively imbalanced. To compensate for this problem, data augmentation methods are applied on the underrepresented classes. Experimental results show that using Mobilenet-v2 architecture for this task is beneficial in terms of both accuracy and the memory usage. With the model achieving 94% accuracy on 23 food classes, the developed mobile application has potential to serve the visually impaired in automatic food recognition via images.

关键词： Food recognition Assistive technology computer vision

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A Practical Algorithm for Max-Norm Optimal Binary Labeling of Graphs 13th

A Practical Algorithm for Max-Norm Optimal Binary Labeling ...

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13th IAPR-TC-15 international Workshop on Graph-Based Representations in pattern recognition, GbRPR 2023

作者： Malmberg, Filip Falcão, Alexandre X. Centre for Image Analysis Department of Information Technology Uppsala University Uppsala Sweden Institute of Computing University of Campinas Campinas Brazil

ISBN: (纸本)9783031427947

this paper concerns the efficient implementation of a method for optimal binary labeling of graph vertices, originally proposed by Malmberg and Ciesielski (2020). this method finds, in quadratic time with respect to graph size, a labeling that globally minimizes an objective function based on the L∞ -norm. the method enables global optimization for a novel class of optimization problems, with high relevance in application areas such as image processing and computer vision. In the original formulation, the Malmberg-Ciesielski algorithm is unfortunately very computationally expensive, limiting its utility in practical applications. Here, we present a modified version of the algorithm that exploits redundancies in the original method to reduce computation time. While our proposed method has the same theoretical asymptotic time complexity, we demonstrate that is substantially more efficient in practice. Even for small problems, we observe a speedup of 4–5 orders of magnitude. this reduction in computation time makes the Malmberg-Ciesielski method a viable option for many practical applications. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Combinatorial optimization

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A Comparison of Deep Learning Methods for Inebriation recognition in Humans 1

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21st international conference on image Analysis and processing (ICIAP)

作者： Bhango, Zibusiso van der Haar, Dustin Univ Johannesburg Kingsway Ave & Univ Rd ZA-2092 Johannesburg South Africa

ISBN: (数字)9783031064272

ISBN: (纸本)9783031064272;9783031064265

Excessive alcohol consumption leads to inebriation. Driving under the influence of alcohol is a criminal offence in many countries involving operating a motor vehicle while inebriated to a level that renders safely operating a motor vehicle extremely difficult. Studies show that traffic accidents will become the fifth most significant cause of death if inebriated driving is not mitigated. Inversely, 70% of the world population can be protected by mitigating inebriated driving. Short term effects of inebriation include lack of balance, inhibition and fine motor coordination, dilated pupils and slow heart rate. An ideal inebriation recognition method that operates in real-time is less intrusive, more convenient, and efficient. Deep learning has been used to solve object detection, object recognition, object tracking and image segmentation problems. In this paper, we compare deep learning inebriation recognition methods. We implemented Faster R-CNN and YOLO methods for our experiment. We created our dataset of sober and inebriated individuals made available to the public. Six thousand four hundred forty-three (6443) face images were used, and our best performing pipeline was YOLO with a 99.6% accuracy rate.

关键词： Deep learning computer vision R-CNN YOLO Inebriation recognition Inebriation detection Drunk driving

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Palm Template Protection Method Based on Nonlinear threshold Quantification 4

Palm Template Protection Method Based on Nonlinear Threshold...

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4th international conference on Natural Language processing, ICNLP 2022

作者： Jin, Ci Zhang, Xuefeng Jiang, Wen Xi'an University of Posts and Telecommunications Cyberspace Security Academy Xi'an China National Computer Network Emergency Response Technology Coordination Center Beijing China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

In view of the existing problem of the safety and insufficient matching performance of template protection methods using linear threshold quantification, a palm-print template protection method using nonlinear threshold quantification is proposed. the algorithm first extracts the multi-directional and multi-scale amplitude characteristics of palm-print images through gabor filter, then extracts the local uniform pattern LBP characteristics in chunks, then uses nonlinear threshold quantification to obtain the two-value matrix after combining the LBP features into a feature matrix, then performs many-to-one mapping through bloom filter, and finally multiplys with the user's proprietary key to obtain a revocable palmprint template. the theoretical analysis and experimental results show that compared with the linear threshold quantification method, the method can effectively protect the original palm print characteristics, but also has better recognition performance. © 2022 IEEE.

关键词： Gabor filters

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