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MIRIAMS: Models Integration for Reliable Identification and Accurate Multiple Sclerosis Segmentation 27th

MIRIAMS: Models Integration for Reliable Identification and...

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27th international conference on pattern recognition, ICPR 2024

作者： Pani, Alessandro Loddo, Andrea Putzu, Lorenzo Zedda, Luca Di Ruberto, Cecilia Department of Mathematics and Computer Science University of Cagliari Cagliari Italy Department of Electrical and Electronic Engineering University of Cagliari Cagliari Italy

ISBN: (纸本)9783031876592

Multiple sclerosis lesion segmentation is a critical task in medical imaging since it aims to identify and delineate brain lesions. In this study, we propose a new method called Models Integration for Reliable Identification and Accurate Multiple Sclerosis Segmentation (MIRIAMS), which is a robust ensemble approach that integrates three distinct neural network architectures to enhance segmentation performance in 3D volumes, setting it apart from the majority of existing methods which operate in 2D. MIRIAMS consists of three different 3D networks: a traditional 3D U-Net, a 3D UNETR that leverages vision Transformers, and a 3D Swin-UNETR, which employs Swin vision Transformers. this combination allows the model to capture features with varying receptive fields, improving the overall lesion segmentation. Unlike previous studies, we conduct a per-patient evaluation, ensuring a more individualised and clinically relevant assessment of the segmentation performance. Our results demonstrate that the ensemble approach outperforms individual models, providing a promising, reliable and effective tool for multiple sclerosis lesion segmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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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 ...

引用

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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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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Curriculum for a New Academic Program with Bachelor’s Degrees in Intelligent Systems Engineering and computer Engineering 20th

Curriculum for a New Academic Program with Bachelor’s Degre...

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20th international conference on Foundations of computer Science, FCS 2024, and 20th international conference on Frontiers in Education, FECS 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

作者： Solo, Ashu M. G. Maverick Trailblazers Inc.™ Wilmington Delaware United States

ISBN: (纸本)9783031859298

this research paper proposes a curriculum for a six-year academic program with a bachelor’s degree (honors) in intelligent systems engineering and a bachelor’s degree (honors) in computer engineering. this program includes courses in digital electronics, analog electronics, computer organization, computer architecture, computer communication networks, data structures, compiler design, operating system design, firmware design, database systems, computer graphics and virtual reality design, static and dynamic website design, development of chatbots and voice assistants, software engineering methodology, knowledge-based systems, fuzzy logic, neural networks, evolutionary computation, evolutionary multiojective optimization, machine learning, image processing, computer vision, pattern recognition, voice recognition, natural language processing, data science, control systems, intelligent control systems, robotics, digital signal processing, signals and systems, mathematics, engineering physics, biology, etc. these degrees will allow graduates to have a good understanding of all of the main branches of intelligent systems engineering and computer engineering. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Teaching

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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 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 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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Micro-expression recognition Using Pre-trained Model and Transformer 4

Micro-expression Recognition Using Pre-trained Model and Tra...

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4th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2022

作者： Fan, Yali Jia, Minghan Zhang, Yifan Yu, Yang National University of Defense Technology Changsha China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

In this study, we proposed a micro-expression recognition framework combining the vision Transformer (ViT) network based on the multi-head self-attention mechanism with a pre-trained model. In this framework, each image in the micro-and-macro expression warehouse (MMEW) dataset was divided into 224 patches, and these patches were then rearranged into a one-dimensional vector. Due to the difficulty of collection, the datasets of micro-expressions are usually small, making it difficult to achieve satisfactory results using traditional deep network models. therefore, in our framework we introduce a pre-trained model (ViT-base-patch16-224) that is trained on imageNet to address this problem. Given that the pre-trained model has sufficient parameters and the MMEW dataset is smaller, a freezing strategy is adopted to make it suitable for the MMEW data. To the best of our knowledge, this is the first work that combining used the ViT model and the pre-trained model for micro-expression recognition task. Our preliminary experiments results show that the proposed micro-expression recognition framework achieved an accuracy of 87% on MMEW after 10 epochs training, which is the state-of-the-art accuracy achieved in this data set. Moreover, the accuracy is further improved to 93% with 30 epochs training, reflecting the potential of our framework in micro-expression recognition. © 2022 IEEE.

关键词： computer vision

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