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检索条件"任意字段=Brazilian Symposium on Computer Graphics and Image Processing"

共 2979 条记录，以下是41-50 订阅

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Parallel Fast Orthogonal Search on graphics processing Unit for Equipment Health Monitoring on Ship-Radiated Noise 12

Parallel Fast Orthogonal Search on Graphics Processing Unit ...

引用

12th IEEE International symposium on Signal, image, Video, and Communications, ISIVC 2024

作者： Marasco, Nicholas Roberge, Vincent Elghamrawy, Haidy Tarbouchi, Mohammed Noureldin, Aboelmagd Royal Military College of Canada Kingston Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350385267

Equipment health monitoring (EHM) techniques are increasing in their ability to accurately diagnose defective equipment. This increase in capability comes with an increase in computational complexity. For these techniques to be useful in real applications, the algorithms must be computable in real time. The Fast Orthogonal Search (FOS) algorithm shows the potential to be effective in a variety of EHM applications. In this paper, we demonstrate that the FOS algorithm can be accelerated to real-time processing on real examples of ship-radiated noise by using parallel processing, making it suitable for use in EHM. © 2024 IEEE.

关键词： graphics processing unit

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Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution 35

Combining Attention Module and Pixel Shuffle for License Pla...

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35th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Nascimento, Valfride Laroca, Rayson Lambert, Jorge de A. Schwartz, William Robson Menotti, David Univ Fed Parana Dept Informat Curitiba Brazil Brazilian Fed Police Reg Superintendence Bahia Salvador Brazil Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

The License Plate Recognition (LPR) field has made impressive advances in the last decade due to novel deep learning approaches combined with the increased availability of training data. However, it still has some open issues, especially when the data come from low-resolution (LR) and low-quality images/videos, as in surveillance systems. This work focuses on license plate (LP) reconstruction in LR and low-quality images. We present a Single-image Super-Resolution (SISR) approach that extends the attention/transformer module concept by exploiting the PixelShuffle layers capabilities and that has an improved loss function based on LPR predictions. For training the proposed architecture, we use synthetic images generated by applying heavy Gaussian noise in terms of Structural Similarity Index Measure (SSIM) to the original high-resolution (HR) images. In our experiments, the proposed method outperformed the baselines both quantitatively and qualitatively. The datasets we created for this work are publicly available to the research community at https://***/valfride/lpr-rsr/.

关键词： Training image recognition Surveillance Superresolution Training data computer architecture Noise measurement

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Evaluating an XOR-based Hybrid Fault Tolerance Technique to Detect Faults in GPU Pipelines 26

Evaluating an XOR-based Hybrid Fault Tolerance Technique to ...

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26th IEEE-computer-Society Annual symposium on VLSI (ISVLSI)

作者： Braga, Giani Augusto Goncalves, Marcio M. Azambuja, Jose Rodrigo Fed Univ Rio Grande do Sul UFRGS Inst Informat PPGC PGMICRO Porto Alegre Brazil

ISBN: (纸本)9798350327694

graphics processing Units are consistently reaching new applications due to their massive parallel execution architectures. However, some safety-critical areas, such as avionics, come with unfriendly environments due to radiation effects caused by cosmic rays, effectively causing component failures. This work implements and tests a hybrid fault tolerance technique initially proposed by NVIDIA to protect a GPU's pipeline against radiation effects. Results show that the technique can be effective against data-flow errors but at a high cost in execution time overheads and potentially increased control-flow errors.

关键词： Fault tolerance graphics processing units pipeline single event upsets

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brazilian Road's Animals (BRA): An image Dataset of Most Commonly Run Over Animals 35

Brazilian Road's Animals (BRA): An Image Dataset of Most Com...

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35th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Ferrante, Gabriel S. Nakamura, Luis H. V. Andrade, Fernando R. H. Rocha Filho, Geraldo P. De Grande, Robson E. Meneguette, Rodolfo I. Univ Sao Paulo Inst Sci Math & Comp Sci Sao Carlos Brazil Fed Inst Sao Paulo Dept Informat Catanduva Brazil Univ Brasilia Dept Comp Sci Brasilia Brazil Brock Univ Dept Comp Sci St Catharines ON Canada

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

In Brazil, about 475 million animals, including small and large species, are killed on the roads every year. Although this problem with wildlife can be considered recurrent, it involves significant investments and resources whose governments cannot afford to allocate to develop new mechanisms and technologies to identify, monitor, and guarantee the safety of animals in high-risk areas. In the field of computer vision, there are already datasets about animals in the literature, but not focusing on animals that suffer most accidents on highways. This work thus aims to present a new dataset of labelled images to address this gap, the BRA-Dataset. The labeling concerns five medium and large mammals that die the most on brazilian roads, which can serve as a training dataset for future animal detection models on highways. The images were obtained using the proposed acquisition approach with a manual cleaning process;the dataset contain 1823 images in total and labelled in the YOLO Darknet and PASCAL VOC format. Also was effectuated experimentation on BRA-Dataset for validation of its performance on YOLO models training and tests. The results showed a high average precision achieved over the dataset in its first version without data augmentation or application process of improvement.

关键词： Training computer vision Animals Biological system modeling Roads Data models Safety

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Fine-grained Cars Recognition using Deep Convolutional Neural Networks 35

Fine-grained Cars Recognition using Deep Convolutional Neura...

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35th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Oliveira, Franklin Macena, Arianne Kamel, Otavio Souza, Wesley Freitas, Nicksson Vinuto, Tiago Univ Fed Pernambuco Comp Ctr Recife Brazil SiDi Recife Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Population growth and the high concentration of vehicles on urban roads have negatively impacted urban mobility and the global environment since the primary transportation modes occupy a lot of space on the streets and are one of the main polluting gas emitters. In this context of inefficient urban mobility and unsustainability, the Intelligent Transportation Systems (ITS) aims to solve or minimize urban traffic issues. ITS are also widely used in applications focused on traffic safety, such as vehicle recognition related to a traffic or law violation. For this task, the fine-grained vehicle classification technique is mainly used by computer vision and deep learning advances. However, identifying vehicles by the model can be problematic because the same vehicle can be easily misclassified when observed from different perspectives, with different colours, or by similar models. Knowing these inherent issues from vehicle recognition tasks, Deep Convolutional Neural Networks (DCNNs) are commonly used due to their ability to extract features from images. In that regard, the goal of this paper is to evaluate some state of art DCNNs architectures, conducting experiments with three different datasets to identify which architectures have the best performance metrics in the refined car classification task within ITS context.

关键词： Training computer vision Computational modeling Urban areas computer architecture Feature extraction Safety

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A First Look at Dataset Bias in License Plate Recognition 35

A First Look at Dataset Bias in License Plate Recognition

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35th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Laroca, Rayson Santos, Marcelo Estevam, Valter Luz, Eduardo Menotti, David Univ Fed Parana Dept Informat Curitiba Brazil Fed Inst Parana Irati Brazil Univ Fed Ouro Preto Dept Comp Sci Ouro Preto Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Public datasets have played a key role in advancing the state of the art in License Plate Recognition (LPR). Although dataset bias has been recognized as a severe problem in the computer vision community, it has been largely overlooked in the LPR literature. LPR models are usually trained and evaluated separately on each dataset. In this scenario, they have often proven robust in the dataset they were trained in but showed limited performance in unseen ones. Therefore, this work investigates the dataset bias problem in the LPR context. We performed experiments on eight datasets, four collected in Brazil and four in mainland China, and observed that each dataset has a unique, identifiable "signature" since a lightweight classification model predicts the source dataset of a license plate (LP) image with more than 95% accuracy. In our discussion, we draw attention to the fact that most LPR models are probably exploiting such signatures to improve the results achieved in each dataset at the cost of losing generalization capability. These results emphasize the importance of evaluating LPR models in cross-dataset setups, as they provide a better indication of generalization (hence real-world performance) than within-dataset ones.

关键词： graphics computer vision Costs Computational modeling Predictive models License plate recognition

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Randomrewardmechanisms (Loot Boxes): Behavioral Economics As A Regulatory Solution In brazilian Law 17

Randomrewardmechanisms (Loot Boxes): Behavioral Economics As...

引用

17th International Conference on Interfaces and Human computer Interaction 2023, IHCI 2023, the 17th International Conference on computer graphics, Visualization, computer Vision and image processing 2023, CGVCVIP 2023 and 16th International Conference on Game and Entertainment Technologies 2023, GET 2023

作者： Fantini, Laiane Maris Caetano PUC Minas. Centro DTIBR MG Belo Horizonte Brazil

ISBN: (纸本)9789898704498

The paper seeks to reflect on ways to create an effective regulation by understanding the aspects of random reward mechanisms in video games, also known as loot boxes. Players acquire digital assets of a game through microtransactions, and pays with in-game currency or real money. Among these digital assets, several games also offer loot boxes, which are systems of "surprise boxes" with a certain price but whose reward is unknown to the player (and is only revealed when the box is purchased and opened). A simple way of selling a digital asset has serious implications for players, mainly due to the similarity with gambling in making the reward relegated to chance. Also, adults and children players are encouraged to exhibit addictive behaviors, spending time and money without any control. brazilian law does not have enough apparatus to protect consumers from these practices and banning loot boxes from the market does not seem to be the solution, as similar mechanisms could arise even worse. This way of marketing digital assets hides the real transaction costs and makes consumers make decisions based on heuristics, and that's why the Behavioral Economic Theory can offer better solutions to protect players. © 2023 Proceedings of the International Conferences on Interfaces and Human computer Interaction 2023, IHCI 2023;computer graphics, Visualization, computer Vision and image processing 2023, CGVCVIP 2023;and Game and Entertainment Technologies 2023, GET 2023. All rights reserved.

关键词： Human computer interaction

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Domain Generalization in Medical image Segmentation via Meta-Learners 35

Domain Generalization in Medical Image Segmentation via Meta...

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35th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Oliveira, Hugo Cesar, Roberto M., Jr. Gama, Pedro H. T. dos Santos, Jefersson A. Univ Sao Paulo Inst Math & Stat Sao Paulo Brazil Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte Brazil Univ Stirling Comp Sci & Math Stirling Scotland

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Automatic and semi-automatic radiological image segmentation can help physicians in the processing of real-world medical data for several tasks such as detection/diagnosis of diseases and surgery planning. Current segmentation methods based on neural networks are highly data-driven, often requiring hundreds of laborious annotations to properly converge. The generalization capabilities of traditional supervised deep learning are also limited by the insufficient variability present in the training dataset. One very proliferous research field that aims to alleviate this dependence on large numbers of labeled data is Meta-Learning. Meta-Learning aims to improve the generalization capabilities of traditional supervised learning by training models to learn in a label efficient manner. In this tutorial we present an overview of the literature and proposed ways of merging this body of knowledge with deep segmentation architectures to produce highly adaptable multi-task meta-models for few-shot weakly-supervised semantic segmentation. We introduce a taxonomy to categorize Meta-Learning methods for both classification and segmentation, while also discussing how to adapt potentially any few-shot meta-learner to a weakly-supervised segmentation task.

关键词： meta-learning few-shot learning semantic segmentation medical imaging domain generalization

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Automated Sperm Head Morphology Classification with Deep Convolutional Neural Networks 35

Automated Sperm Head Morphology Classification with Deep Con...

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35th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Calijorne Soares, Marco Antonio Mourao Falci, Daniel Henrique Alves Farnezi, Marco Flavio Moreira Farnezi, Hana Carolina Parreiras, Fernando Silva Boechat Gomide, Joao Victor FUMEC Univ Grad Program Informat Syst & Knowledge Management Belo Horizonte Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Background and Objective: The morphological analysis of sperm cells is considered a tool in human fertility prognosis. However, this process is manual, time-consuming and dependent on professional expertise. From a computational perspective, this is a challenging problem due to the high inter-category similarity between the objects of interest and the amount of data available. In this paper, we propose a Convolutional Neural Network model to automate morphology analysis of human sperm heads. Methods: We performed K-Fold cross-validation experiments over two publicly available datasets and assessed the performance of the proposed approach using Accuracy, Precision, Recall and F1-Score. We also compared the proposed model with well-known Convolutional architectures and previous approaches on the same task. Results: Experimental evaluation showed that our approach achieved a macro-averaged F1-score of 0.95 while our best model attained an accuracy of 97.7%. The error analysis revealed a balanced classifier over different sperm head classes. Conclusions: We proved that the proposed approach outperformed the previous state-of-the-art results on this task.

关键词： Measurement Head image recognition Computational modeling Morphology computer architecture Manuals

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Face Super-Resolution Using Stochastic Differential Equations 35

Face Super-Resolution Using Stochastic Differential Equation...

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35th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： dos Santos, Marcelo Laroca, Rayson Ribeiro, Rafael O. Neves, Joao Proenca, Hugo Menotti, David Univ Fed Parana Dept Informat Curitiba Brazil Brazilian Fed Police Natl Inst Criminalist Brasilia Brazil Univ Beira Interior Inst Telecomunicacoes Covilha Portugal

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Diffusion models have proven effective for various applications such as images, audio and graph generation. Other important applications are image super-resolution and the solution of inverse problems. More recently, some works have used stochastic differential equations (SDEs) to generalize diffusion models to continuous time. In this work, we introduce SDEs to generate super-resolution face images. To the best of our knowledge, this is the first time SDEs have been used for such an application. The proposed method provides an improved peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and consistency than the existing super-resolution methods based on diffusion models. In particular, we also assess the potential application of this method for the face recognition task. A generic facial feature extractor is used to compare the super-resolution images with the ground truth, and superior results were obtained compared with other methods. Our code is publicly available at https://***/marcelowds/sr-sde.

关键词： PSNR Inverse problems Face recognition Superresolution Stochastic processes Differential equations Particle measurements

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