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

共 144 条记录，以下是31-40 订阅

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Information theory-based Detection of Noisy Bit Planes in Medical images 29

Information Theory-based Detection of Noisy Bit Planes in Me...

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29th sibgrapi conference on graphics, patterns and images (sibgrapi)

作者： de Oliveira, Hugo N. dos Santos, Jefersson A. de Melo, Matheus C. do Rego, thais G. Batista, Leonardo V. Univ Fed Minas Gerais Dept Ciencia Computacao Belo Horizonte MG Brazil Univ Fed Paraiba UFPB Ctr Informat Joao Pessoa Paraiba Brazil

ISBN: (纸本)9781509035687

Mammographic Computer-Aided Diagnosis systems are applications designed to assist radiologists in diagnosis of malignancy in mammographic findings. Most methods described in the literature do not perform a proper preprocessing step in mammographic images prior to classification, which can generate inconsistent results due to the potentially large amount of noise in medical images. this paper proposes a new method based on Information theory and Data Compression for detection of random noise in image bit planes. In order to validate the efficiency of the proposed noise removal method, we used Machine Learning algorithms to classify mammographic findings from the Digital Database for Screening Mammography. Results using texture features indicate that a reduction in the radiometric resolution of 4 or 5 bit planes in digitized screen film mammographic images result in a better classification performance.

关键词： noise detection mammogram classification information theory data compression

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Enhancement of Underwater images in Low-to-High Turbidity Rivers 29

Enhancement of Underwater Images in Low-to-High Turbidity Ri...

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29th sibgrapi conference on graphics, patterns and images (sibgrapi)

作者： Rodrigues, Daily Daleno de O. de Barros, Wagner F. de Queiroz-Neto, Jose P. Fontoura, Anderson G. Carvalho, Jose Reginaldo H. Univ Fed Amazonas IComp BR-69067005 Manaus Amazonas Brazil Inst Fed Amazonas IFAM BR-69100003 Manaus Amazonas Brazil IFNMG Montes Claros MG Brazil

ISBN: (纸本)9781509035687

the demand for efficient enhancement methods of underwater images of the rivers in the Amazon region is increasing. However, most of those in the region present moderate turbidity and low luminosity. this work aims to improve these images by non-linear filtering techniques, which promote the minimization of light interaction characteristics with the environment, loss of the contrast and colors. the proposed method is compared with two others techniques that requires a unique image as input. the results of the proposed method is promising, with better visual quality considering a wide range of experiments with simulation data and real outdoor scenes.

关键词： Underwater images Image Enhancement Degraded images

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Enhancing the Visualization of the Microvasculature of Extrahepatic Bile Ducts Obtained from Confocal Microscopy images 29

Enhancing the Visualization of the Microvasculature of Extra...

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29th sibgrapi conference on graphics, patterns and images (sibgrapi)

作者： Beltran, Lizeth A. C. dos Santos, Jorge Luiz Cruz, Carolina Uribe Freitas, Carla M. D. S. Fed Univ Rio Grande do Sul UFRGS Inst Informat Porto Alegre RS Brazil Univ Beira Interior Ctr Invest Ciencias Saude Covilha Portugal Lab Expt Hepatol & Gastroenterol Porto Alegre RS Brazil

ISBN: (纸本)9781509035687

Confocal microscopy is an important tool for visualizing 3D datasets of fluorescent specimens, and has been used to investigate the structure of biological specimens. However, such images are affected by the noise introduced during the specimen preparation and image acquisition processes. Anisotropic diffusion is a non-linear filter that can significantly improve image quality while removing noise without blurring edges. this study investigates the application of anisotropic diffusion in confocal microscopy images by exploring different models for parameters' estimation. Our data consists of several slices of extrahepatic bile ducts containing a network of small vessels named Peribiliary Vascular Plexus (PVP), which are affected by several sources of noise. Experimental results show that anisotropic diffusion improved the volumetric visualization of the PVP. We validated the results using MSE and PSNR quantitative approaches and qualitative description by an expert user.

关键词： Anisotropic diffusion Image processing Confocal microscopic images Volumetric Visualization

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Zero and Few-Shot Learning with Modern MLLMs to Filter Empty images in Camera Trap Data 37

Zero and Few-Shot Learning with Modern MLLMs to Filter Empty...

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37th 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)9798350376043;9798350376036

Camera traps are typically equipped with motion or heat sensors and capture images of wild animals with little human interference. the recording of an event is activated when the sensor is triggered, which often results in huge volume of images, mainly empty ones. this study addresses the challenge of filtering out empty images, which is a crucial step for efficient data storage, transmission and automatic classification. We investigate the use of large-scale multimodal language models (MLLMs) in zero-shot and few-shot approaches for filtering empty images. We analyze whether or not the visual and textual data integration performed by MLLMs enhance their ability to detect animal presence. three MLLMs are investigated: CLIP, BLIP, and Gemini. they are also compared to a model specially designed to filter out empty images in camera trap data: a ResNet50-Siamese. In our experiments, we compare the learning approaches across three datasets: Snapshot Serengeti, Caltech, and WCS. Our results indicate that few-shot learning significantly improves the performance of MLLMs, especially BLIP. However, these models face challenges such as high computational demands and sensitivity to environmental variations.

关键词： Zero-shot learning

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Exploiting Convolutional Neural Networks and preprocessing techniques for HEp-2 cell classification in immunofluorescence images 30

Exploiting Convolutional Neural Networks and preprocessing t...

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

作者： Rodrigues, Larissa Ferreira Naldi, Murilo Coelho Mari, Joao Fernando UFV Inst Ciencias Exatas & Tecnol Caixa Postal 22 BR-38810000 Rio Paranaiba MG Brazil

ISBN: (纸本)9781538622193

Autoimmune diseases are the third cause of mortality in the world. the identification of anti-nuclear antibody (ANA) via Immunofluorescence (IIF) test in human epithelial type-2 cells (HEp-2) is a conventional method to support the diagnosis of such diseases. In the present work, three popular Convolutional Neural Networks (CNNs) are evaluated for this task: LeNet-5, AlexNet, and GoogLeNet. We also assess the impact of six different pre-processing strategies on the performance of these CNNs. Additionally, data augmentation based on the rotation of the training set images after the pre-processing strategies was evaluated. Our work is the first to consider AlexNet and GoogLeNet models for the proposed analysis and classification of HEp-2 cells images, besides the LeNet-5. Experimental results allow to conclude that neither pre-processing strategies were essential to improve accuracy values of the CNNs. However, when data augmentation is considered, contrast enhancement followed by data centralization is significant in order to achieve good results. Additionally, our results were compared with results from other state-of-art papers. Our best results were achieved by GoogLeNet architecture trained with images with no preprocessing and no data augmentation, resulting in 98.17% of accuracy, which outperforms the results presented in other works in literature.

关键词： Convolutional neural networks HEp-2 cells staining patterns classification LeNet-5 AlexNet GoogLeNet pre-processing data augmentation

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Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB images 35

Learning to Detect Good Keypoints to Match Non-Rigid Objects...

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

作者： Melo, Welerson Potje, Guilherme Cadar, Felipe Martins, Renato Nascimento, Erickson R. Univ Fed Minas Gerais Belo Horizonte Brazil Univ Bourgogne Franche Comt Besancon France

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence. Our training framework uses true correspondences, obtained by matching annotated image pairs with a predefined descriptor extractor, as a ground-truth to train a convolutional neural network (CNN). We optimize the model architecture by applying known geometric transformations to images as the supervisory signal. Experiments show that our method outperforms the state-of-the-art keypoint detector on real images of non-rigid objects by 20 p.p. on Mean Matching Accuracy and also improves the matching performance of several descriptors when coupled with our detection method. We also employ the proposed method in one challenging real-world application: object retrieval, where our detector exhibits performance on par with the best available keypoint detectors. the source code and trained model are publicly available at https://***/verlab/LearningToDetect_sibgrapi_2022

关键词： Training graphics Deformable models Source coding Pipelines Detectors Convolutional neural networks

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Automatic detection of fovea in retinal images using fusion of color bands 27

Automatic detection of fovea in retinal images using fusion ...

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27th sibgrapi conference on graphics, patterns and images (sibgrapi)

作者： Veras, Rodrigo Silva, Romuere Aires, Kelson Medeiros, Fatima Univ Fed Piaui Dept Comp Teresina PI Brazil Univ Fed Ceara Dept Engn Teleinformat Fortaleza CE Brazil

ISBN: (纸本)9781479942602

this paper presents a new method for fovea detection in color retinal images. Automatic detection of this anatomical structure is a prerequisite for computer aided diagnosis of several retinal diseases, such as macular degeneration. the proposed algorithm detects the macula center by determining a region of interest (ROI) and taking into account optic disk (OD) coordinates and the fact that the central region, i.e. fovea, is a homogenous dark area without blood vessels. Our segmentation algorithm searches for the lowest mean color intensity window in the enhanced image that results from a fusion between the red and green channels. then, tests were carried on three public benchmark databases, which constitute a total of 254 images.

关键词： fovea detection retinal images

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Improving accuracy of automatic fracture detection in borehole images with deep learning and GPUs 30

Improving accuracy of automatic fracture detection in boreho...

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

作者： Quintanilla Cruz, Rommel Anatoli Cacau, Diego Carrico dos Santos, Renato Moraes Ribeiro Pereira, Evandro Jose Leta, Fabiana Rodrigues Gonzalez Clua, Esteban Walter Univ Fed Fluminense Comp Sci Inst Medialab Rio De Janeiro Brazil Univ Fed Fluminense Engn Sch LMDC Rio De Janeiro Brazil

ISBN: (纸本)9781538622193

the logging and further analysis of borehole images is a major step in the interpretation of geological events. Natural fractures and beddings are features whose identification is commonly performed using acoustic and electrical borehole imaging tools. Such identification is a tedious task and is made visually by geologists, who must be experts on classification. the correct identification of planar features, represented as sinusoids into an image projection, depends on the quality of the images. Due to the distortions and noises of the images, known as artifacts, the automatic features detection is not trivial through conventional image processing methods. Since the identification process has to ensure that the marked events are true with minimal inconsistencies, we propose a pioneering approach to improving the quality of the results by applying deep neural networks to confirm or exclude candidate features extracted by a regular Hough transform. this is the first approach in literature to improve the quality of geological auto-detected marks by applying deep learning techniques for borehole images where our implementation is able to exclude most of the false positive marks.

关键词： Acoustics Transforms Feature extraction Machine learning Geology Tools Training

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Learning to Segment Medical images from Few-Shot Sparse Labels 34

Learning to Segment Medical Images from Few-Shot Sparse Labe...

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

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

ISBN: (纸本)9781665423540

In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images. We investigate the effectiveness of our method, which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in the medical scenario, where the use of sparse labeling and few-shot can alleviate the cost of producing new annotated datasets. Our method uses sparse labels in the meta-training and dense labels in the meta-test, thus making the model learn to predict dense labels from sparse ones. We conducted experiments with four Chest X-Ray datasets to evaluate two types of annotations (grid and points). the results show that our method is the most suitable when the target domain highly differs from source domains, achieving Jaccard scores comparable to dense labels, using less than 2% of the pixels of an image with labels in few-shot scenarios.

关键词： graphics Image segmentation Costs Annotations Semantics Predictive models Prediction algorithms

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Analysis of the Eyes on Face images for Compliance with ISO/ICAO Requirements 29

Analysis of the Eyes on Face Images for Compliance with ISO/...

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29th sibgrapi conference on graphics, patterns and images (sibgrapi)

作者： Borges, Erick V. C. L. Andrezza, Igor L. P. Marques, Jose R. T. Mota, Rajiv A. T. Primo, Joao Janduy B. VSoft Tecnol Joao Pessoa PB Brazil Univ Fed Campina Grande CI Campina Grande PB Brazil

ISBN: (纸本)9781509035687

the face has been used in identity documents and represents the ideal biometric characteristic in many applications. the International Civil Aviation Organization endorsed the use of face as the globally interoperable biometric characteristic. Successively, the International Standard Organization proposed the ISO/IEC 19794-5 standard for face usage in travel documents. the purpose of this work is to evaluate the quality of face images for identification documents and check if the face images satisfy the requirements defined by the ISO/IEC 19794-5. this work presents approaches for the evaluation of the following ISO/ICAO requirements: eyes state, red eyes and looking away. In addition, an approach to estimate the location of the center of the eyes is proposed. the proposed methods to check ISO/ICAO requirements were evaluated using the BioLab-ICAO Framework. the results achieved by the proposed methods were satisfactory, overcoming almost all the works in the literature for this purpose.

关键词： ICAO requirements Eye detection Validate faces

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