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检索条件"任意字段=28th SIBGRAPI Conference on Graphics, Patterns and Images SIBGRAPI"

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Revisiting age estimation on panoramic dental images 36

Revisiting age estimation on panoramic dental images

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36th sibgrapi conference on graphics, patterns and images (sibgrapi)

作者： Liang, Julian Cury, Patricia Oliveira, Luciano Univ Fed Bahia Inst Comp Intelligent Vis Res Lab Salvador Brazil Univ Fed Bahia Dept Periodont Salvador Brazil

ISBN: (纸本)9798350338737;9798350338720

Forensic dentistry has traditionally relied on bone or dental indicators, primarily utilizing dental radiographs, for age estimation. However, limited research has been conducted on automatic age estimation on panoramic images, needing a reevaluation of the existing methodologies to assess the performance of computer-based methods. this study proposes to revisit the analysis of age estimation methods using panoramic dental radiographs. We have curated the largest publicly available dataset of panoramic dental images, encompassing diverse dental conditions and age ranges. Specifically, our study focuses on evaluating three distinct classes of deep-learning architectures: ViT, ConvNeXt-V2, and EfficientNets, employing a comprehensive to assess their performances that better favor reproducibility. By comparing our approach with existing studies in the literature, we offer valuable insights for forensic investigations in the field of age estimation.

关键词： Deep learning

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the Gated Recurrent Conditional Generative Adversarial Network (GRC-GAN): application to denoising of low-dose CT images 34

The Gated Recurrent Conditional Generative Adversarial Netwo...

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34th sibgrapi conference on graphics, patterns and images (sibgrapi)

作者： de Almeida, Mateus Baltazar Alves Pereira, Luis F. Ren, Tsang Ing Cavalcanti, George D. C. Sijbers, Jan Univ Fed Agreste Pernambuco Garanhuns PE Brazil Univ Fed Pernambuco Recife PE Brazil Univ Antwerp Antwerp Belgium

ISBN: (纸本)9781665423540

the ionizing radiation that propagates through the human body at Computed Tomography (CT) exams is known to be carcinogenic. For this reason, the development of methods for image reconstruction that operate with reduced radiation doses is essential. If we reduce the electrical current in the electrically powered X-ray tubes of CT scanners, the amount of radiation that passes through the human body during a CT exam is reduced. However, significant image noise emerges in the reconstructed CT slices if standard reconstruction methods are applied. To estimate routine-dose CT images from low-dose CT images and thus reduce noise, the Conditional Generative Adversarial Network (cGAN) was recently proposed in the literature. In this work, we introduce the Gated Recurrent Conditional Generative Adversarial Network (GRC-GAN) that is based on the usage of network gates to learn the specific regions of the input image to be updated using the cGAN denoising operation. Moreover, the GRC-GAN is executed recurrently in multiple time steps. At each time step, different parts of the input image are denoised. As a result, our GRC-GAN better focus on the denoise criterium than the regular cGAN in the LoDoPaB-CT benchmark.

关键词： graphics Ionizing radiation Computed tomography Noise reduction Logic gates Reconstruction algorithms Generative adversarial networks

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Evaluation of Keypoint Extraction and Matching for Pose Estimation using Pairs of Spherical images 30

Evaluation of Keypoint Extraction and Matching for Pose Esti...

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30th sibgrapi conference on graphics, patterns and images Tutorials (sibgrapi-T)

作者： da Silveira, thiago L. T. Jung, Claudio R. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (纸本)9781538622193

Keypoint extraction and matching has been widely studied by the computer vision community, mostly focused on pinhole camera models. In this paper we perform a comparative analysis of four keypoint extraction algorithms applied to full spherical images, particularly in the context of pose estimation. Two of the methods chosen for the comparative study, namely A-KAZE and ASIFT, have been designed considering a perspective camera model, but were already applied in an omnidirectional structure from motion pipeline, generating successful results in the literature. the other two algorithms are properly adapted versions of the traditional descriptors SIFT and ORB to the spherical domain, subbed SSFIT and SPHORB. We conduct our tests on captures of omnidirectional cameras, both synthetic and real, arbitrarily translated and rotated with known ground-truth transformations. the extracted keypoints are fed to the well-known 8-point algorithm with RANSAC, allowing to estimate the relative camera poses. these poses (translation vector and rotation matrix) are then compared to the ground-truth transformation parameters, generating the error metrics used in our analysis. Our results indicated that spherical descriptors SSIFT and SPHORB did not produce better results than planar descriptors A-KAZE and ASIFT in the context of pose estimation, particularly in the evaluation with real image pairs.

关键词： Cameras Algorithm design and analysis Transmission line matrix methods Pose estimation three-dimensional displays Distortion Pipelines

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A comparison of Visual SLAM Algorithms ORB-SLAM3 and DynaSLAM on KITTI and TUM Monocular Datasets 36

A comparison of Visual SLAM Algorithms ORB-SLAM3 and DynaSLA...

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36th sibgrapi conference on graphics, patterns and images (sibgrapi)

作者： de Jesus, Kesse Jonatas Klan Pereira, Mateus Osvaldo Emmendorfer, Leonardo Ramos Tello Gamarra, Daniel Fernando Univ Fed Santa Maria Santa Maria Brazil

ISBN: (纸本)9798350338737;9798350338720

this paper focuses on comparing two visual Simultaneous Localization and Mapping (vSLAM) algorithms: ORB-SLAM3 and DynaSLAM, utilizing simulations with a monocular camera. ORB-SLAM3 is an open-source library known for its monocular vSLAM capabilities and is an evolution of the well-regarded ORB-SLAM2. In contrast, DynaSLAM extends ORB-SLAM2 by incorporating Mask R-CNN for dynamic object detection, filtering, and segmentation. Both algorithms were tested and evaluated using sequences of monocular images from two popular datasets, KITTI and TUM RGB-D. the experiments demonstrate the efficiency of the vSLAM algorithms. the results reveal that DynaSLAM consistently outperforms ORB-SLAM3 in the majority of cases. Overall, this research contributes to the understanding of these vSLAM methods, providing insights into their performance and highlighting the advantages of DynaSLAM over ORB-SLAM3 in various scenarios.

关键词： vslam DynaSLAM ORB-SLAM3 monocular images

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Segmentation and Removal of Markings in Metal Inspection images 36

Segmentation and Removal of Markings in Metal Inspection Ima...

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36th sibgrapi conference on graphics, patterns and images (sibgrapi)

作者： Soares, Luciane Evangelista, Eduardo Maurente, Vinicius Dos Santos, Matheus M. Maurell, Igor Pias, Marcelo Drews-Jr, Paulo Botelho, Silvia S. C. Univ Fed Rio Grande FURG Ctr Computat Sci C3 Rio Grande Brazil

ISBN: (纸本)9798350338737;9798350338720

the inspection process of metallic surfaces, especially FPSO tanks, is still heavily reliant on manual methods, requiring long production downtime and posing health risks to inspectors. Automating this analysis step will provide significant benefits to the management of these vessels' integrity, reducing expenses, downtime, and, most importantly, the exposure time of employees to hazards associated with inspection activities. During manual inspections, inspectors make annotations using paint, typically in white and yellow colors, directly on the tank walls, hindering the automation of the inspection process as it complicates the segmentation and identification of potential flaws on the tank wall using techniques such as neural network models. Recognizing this problem, this work presents a proposal for the identification and segmentation of these markings by segmenting them in the images, followed by the removal of the segmented markings using image texture-filling techniques.

关键词： Tanks (containers)

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A novel method for detecting the fovea in fundus images of the eye 29

A novel method for detecting the fovea in fundus images of t...

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29th sibgrapi conference on graphics, patterns and images, sibgrapi 2016

作者： De Moura Garcia, Pedro Kreutz, Diego Luis Welfer, Daniel Alegrete Rio Grande do Sul Brazil Santa Maria Rio Grande do Sul Brazil

ISBN: (纸本)9781509035687

In this paper we propose a novel method for finding the fovea in colored images of the eye fundus. We use an image correlation coefficient to find the fovea, which is calculated from a set of template images of the fovea. the results, using the DIARETDB1 database, indicate that our method detects the fovea region with an accuracy of 82,02%. © 2016 IEEE.

关键词： Eye protection

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Text/non-text classification of connected components in document images 30

Text/non-text classification of connected components in docu...

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30th conference on graphics, patterns and images (sibgrapi)

作者： Julca-Aguilar, Frank D. Maia, Ana L. L. M. Hirata, Nina S. T. Univ Sao Paulo Inst Math & Stat Dept Comp Sci Sao Paulo Brazil State Univ Feira de Santana UEFS Dept Exact Sci Feira De Santana Brazil

ISBN: (纸本)9781538622193

Text segmentation is an important problem in document analysis related applications. We address the problem of classifying connected components of a document image as text or non-text. Inspired from previous works in the literature, besides common size and shape related features extracted from the components, we also consider component images, without and with context information, as inputs of the classifiers. Multi-layer perceptrons and convolutional neural networks are used to classify the components. High precision and recall is obtained with respect to both text and non-text components.

关键词： Feature extraction Training Image segmentation Shape Data mining Neural networks Layout

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PTL-AI Furnas Dataset: A Public Dataset for Fault Detection in Power Transmission Lines Using Aerial images 35

PTL-AI Furnas Dataset: A Public Dataset for Fault Detection ...

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35th sibgrapi conference on graphics, patterns and images (sibgrapi)

作者： de Oliveira, Frederico Santos de Carvalho, Marcelo Tancredo Campos, Pedro Henrique da Silva Soares, Anderson Candido Junior, Arnaldo Rodrigues da Silva Quirino, Ana Claudia Fed Univ Mato Grosso UFMT Cuiaba Mato Grosso Brazil Eletrobras Furnas Rio De Janeiro RJ Brazil Univ Fed Goias UFG Goiania Go Brazil Univ Estadual Paulista UNESP Sao Jose Do Rio Preto SP Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

We present a new images dataset called PTL-AI Furnas Dataset as a new benchmark for fault detection in power transmission lines. this dataset has 6,295 images, with resolution 1280x720, extracted from the maintenance process of the energy transmission lines at Furnas company. It contains annotations of 17,808 components classified as baliser, bird nest, insulator, spacer and stockbridge. Furnas is a company that generates or transmits electricity to 51% of households in Brazil and more than 40% of the nation's electricity passes through their grid enabling generating the dataset in different backgrounds and climatic conditions. We performed experiments using data augmentation techniques to train Faster R-CNN, Single-Shot Detects (SSD) and YoloV5 models. the benchmark result was obtained using the metrics of Mean Average Precision (mAP) and the Mean Average Recall (mAR) with values mAP=91.9% and mAR=89.7%. the PTL-AI Furnas Dataset is publicly available at https://***/freds0/PTL-AI_Furnas_Dataset.

关键词： Performance evaluation Power transmission lines Transmission line matrix methods Fault detection Power transmission Companies Benchmark testing

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A Comparative Analysis of Deep Learning Techniques for Sub-tropical Crop Types Recognition from Multitemporal Optical/SAR Image Sequences 30

A Comparative Analysis of Deep Learning Techniques for Sub-t...

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30th sibgrapi conference on graphics, patterns and images Tutorials (sibgrapi-T)

作者： Castro, Jose Bermudez Feitosa, Raul Queiroz La Rosa, Laura Cue Diaz, Pedro Achanccaray Sanches, Ieda Pontifical Catholic Univ Rio de Janeiro Rio De Janeiro Brazil Univ Estado Rio De Janeiro Rio De Janeiro Brazil Natl Inst Space Res Sao Jose Dos Campos Brazil

ISBN: (纸本)9781538622193

Remote Sensing (RS) data have been increasingly applied to assess agricultural yield, production and crop condition. In tropical areas, crop dynamics are complex due to multiple agricultural practices such as irrigation, non-tillage, crop rotation and multiple harvest per year. Spatial and temporal information can improve the performance in land-cover and crop type classification tasks. In this context Deep Learning (DL) have emerged as a powerful state-of-the-art technique in the RS community. this work presents a comparative analysis of traditional and DL (supervised and unsupervised) approaches for crop classification on sequences of multitemporal optical and SAR images. three different approaches are compared: the image stacking approach, which is used as baseline, and two DL based approaches using Autoencoders (AEs) and Convolutional Neural Networks (CNNs). Experiments were carried out in two datasets from two different municipalities in Brazil, Ipua in Sao Paulo state and Campo Verde in Mato Grosso state. It is shown that CNN and AE outperformed the traditional approach based on image stacking in terms of Overall Accuracy and Class Accuracy.

关键词： Crop Recognition Multitemporal images Autoencoders Convolutional Neural Networks

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A Context-Aware Approach for Filtering Empty images in Camera Trap Data Using Siamese Network 36

A Context-Aware Approach for Filtering Empty Images in Camer...

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36th sibgrapi conference on graphics, patterns and images (sibgrapi)

作者： Alencar, Luiz Cunha, Fagner dos Santos, Eulanda M. Univ Fed Amazonas ICOMP Inst Comp BR-69067005 Manaus Amazonas Brazil

ISBN: (纸本)9798350338737;9798350338720

this paper presents a method based on a Siamese convolutional neural network (CNN) for filtering empty images captured by camera traps. the proposed method takes into account information of the environment surrounding the camera by comparing captured images with empty reference images obtained regularly from the same capture locations. Reference images are expected to highlight local vegetation features such as rocks, mountains and lakes. By calculating the similarity between the two images, the Siamese network determines whether or not the captured image contains an animal. We present a protocol to provide image pairs to train the models, as well as the data augmentation techniques employed to enhance the training procedure. three different CNN models are used as backbones for the Siamese network: MobileNetV2, ResNet50, and EfficientNetB0. In addition, experiments are conducted on three popular camera trap datasets: Snapshot Serengeti, Caltech and WCS. the results demonstrate the effectiveness of the proposed method due to the information of the capture location considered, and its potential for wildlife monitoring applications.

关键词： Convolutional neural networks

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