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34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Laranjeira, Camila Mota, Virginia Fernandes dos Santos, Jefersson Alex Univ Fed Minas Gerais Comp Sci Dept Belo Horizonte MG Brazil Univ Fed Minas Gerais Colegio Tecn Belo Horizonte MG Brazil

ISBN: (纸本)9781665423540

Machine Learning bias is an issue with two main disadvantages. It compromises the quantitative performance of a system, and depending on the application, it may have a strong impact on society from an ethical viewpoint. In this work we inspect the literature on computer Vision focusing on human-centered applications such as computer-aided diagnosis and face recognition to outline several forms of bias, bringing study cases for a more thorough inspection of how this issue takes form in the field of machine learning applied to images. We conclude with proposals from the literature on how to solve, or at least minimize, the impacts of bias.

关键词： graphics Ethics computer vision Face recognition Focusing Machine learning Inspection

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Gravity Alignment for Single Panorama Depth Inference 34

Gravity Alignment for Single Panorama Depth Inference

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34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Bergmann, Matheus A. Pinto, Paulo G. L. da Silveira, Thiago L. T. Jung, Claudio R. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (纸本)9781665423540

Monocular depth inference methods based on 360 degrees images allow 3D reconstruction of entire rooms with a single capture. However, most state-of-the-art approaches assume gravity-aligned images and are highly sensitive to camera rotations. Such limitations result in poor depth estimates, which may jeopardize further 3D-based applications. Here, we present a pipeline for spherical single-image depth inference supplied by a novel rotation correction module. We show that our gravity alignment module can improve existing single-image depth estimation methods, being also useful for aligning color and depth to the horizon, which is highly desirable in many applications.

关键词： graphics Three-dimensional displays image color analysis Pipelines Estimation Cameras Gravity

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Cognitive Model of Agent Exploration with Vision and Signage Understanding

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computer graphics FORUM 2022年第8期41卷 143-154页

作者： Johnson, Colin Haworth, Brandon Univ Victoria Victoria BC Canada

Signage systems play an essential role in ensuring safe, stress-free, and efficient navigation for the occupants of indoor spaces. Crowd simulations with sufficiently realistic virtual humans provide a convenient and cost-effective approach to evaluating and optimizing signage systems. In this work, we develop an agent model which makes use of image processing on parametric saliency maps to visually identify signage and distractions in the agent's field of view. Information from identified signs is incorporated into a grid-based representation of wayfinding familiarity, which is used to guide informed exploration of the agent's environment using a modified A* algorithm. In areas with low wayfinding familiarity, the agent follows a random exploration behaviour based on sampling a grid of previously observed locations for heuristic values based on space syntax isovist measures. The resulting agent design is evaluated in a variety of test environments and found to be able to reliably navigate towards a goal location using a combination of signage and random exploration.

关键词： CCS Concepts • Computing methodologies → Multi-agent systems image manipulation • Applied computing → computer-aided design

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Non-local medians filter for joint Gaussian and impulsive image denoising 34

Non-local medians filter for joint Gaussian and impulsive im...

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34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Levada, Alexandre L. M. Univ Fed Sao Carlos Comp Dept Sao Carlos SP Brazil

ISBN: (纸本)9781665423540

image denoising concerns with the development of filters to remove or attenuate random perturbations in the observed data, but at the same time, preserving most of edges and fine details in the scene. One problem with joint additive Gaussian and impulsive noise degradation is that they are spread over all frequencies of the signal. Hence, the most effective filters for this kind of noise are implemented in the spatial domain. In this paper, we proposed a Non-Local Medians filter that combine the medians of every patch of a search window using two distinct similarity measures: the Euclidean distance and the Kullback-Leibler divergence between Gaussian densities estimated from the patches. Computational experiments with 25 images corrupted by joint Gaussian and impulsive noises show that the proposed method is capable of producing, on average, significant higher PSNR and SSIM than the combination of the median filter and the Non-Local Means filter applied independently.

关键词： graphics Degradation Additives Density measurement Perturbation methods image edge detection Euclidean distance

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Segmenting White Matter Hyperintensity in Alzheimer's Disease using U-Net CNNs 35

Segmenting White Matter Hyperintensity in Alzheimer's Diseas...

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

作者： Duarte, Kaue T. N. Gobbi, David G. Sidhu, Abhijot S. McCreary, Cheryl R. Saad, Feryal Das, Nita Smith, Eric E. Frayne, Richard Univ Calgary Hotchkiss Brain Inst Radiol & Clin Neurosci Calgary AB T2N IN4 Canada Foothills Med Ctr Calgary Image Proc & Anal Ctr Calgary AB T2N 2T9 Canada Univ Calgary Biomed Engn Grad Program Calgary AB T2N IN4 Canada Foothills Med Ctr Seaman Family MR Res Ctr Calgary AB T2N 2T9 Canada

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

White-matter hyperintensity (WMH) is associated with many disorders where it is suggestive of underlying cerebrovascular, small-vessel disease pathology. However, its role in Alzheimer's disease (AD), mixed, and vascular dementia remains an open area of research. The fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) imaging sequence is commonly used to visualize WMH because it provides good image contrast, not only between WMH and normal tissue, but also between WMH and cerebrospinal fluid. Manual segmentation of WMH lesions in brain volumes, on a slice-by-slice basis, is time-consuming with high inter-rater variability, however, this process remains the broadly accepted gold standard. In this study, variations of 2D, 2.5D and 3D U-shaped convolutional neural networks (U-Net CNNs) were used to perform semantic segmentation on FLAIR images. We evaluated these models in brain volumes obtained from 186 individuals from one of three disease classes: healthy normal (N = 94), mild cognitive impairment (N = 55), and AD (N = 37). Four common architectures (VGG16, VGG19, ResNet and EfficientNetB0) were employed as feature extractors. Results were assessed across the whole brain and by brain region (frontal, occipital, parietal, temporal lobes plus the insula) to identify differences in performance. In general, the predicted WMH volumes had an F-measure score >95% on the test data compared to manual segmentation. This work identified that 2.5D with either VGG16 or VGG19 was the most suitable configuration when segmenting WMH. WMH segmentation performance and measured volume was found to vary between regions and disease classes. U-Net CNN architectures have good performance and may provide valuable insights about the white matter pathology.

关键词： Training Pathology Volume measurement Manuals Brain modeling Feature extraction Convolutional neural networks

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Analysis of the statistical significance of 3D texture features in MRI images toward the detection of Tourette's Syndrome 35

Analysis of the statistical significance of 3D texture featu...

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

作者： de Barros, Murilo Costa Duarte, Kaue T. N. Lee, Wang-Tso Hsu, Chia-Jui de Carvalho, Marco Antonio G. Univ Estadual Campinas Sch Technol UNICAMP Comp Visual Lab R Paschoal Marmo 1888 Limeira Brazil Univ Calgary Vasc Imaging Lab 2500 Univ Dr NW Calgary AB T2N IN4 Canada Natl Taiwan Univ Childrens Hosp Dept Pediat Taipei Taiwan Natl Taiwan Univ Hosp Dept Pediat Hsinchu Branch Taipei Taiwan

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Tourette Syndrome (TS) is a genetically induced disorder that is believed to be caused by morphological alterations in brain structure, resulting in involuntary movements known as tics. The current clinical standard for diagnosing TS is by clinical assessments performed by physicians. Mild stages of TS, however, commonly go underdiagnosed as tics are infrequent or often suppressed. Brain imaging has been suggested to be a reliable tool to detect brain alterations and possible biomarkers that correspond to neurological disorders. In Magnetic Resonance Imaging (MRI), anatomical brain changes can be identified in the scan by variation in texture patterns of certain regions. The main goal of this work is to identify the statistical significance of texture features in specific brain regions to distinguish TS from control subjects. The proposed approach consists of four main steps: (i) image acquisition, where the data is also organized using demographic information;(ii) brain segmentation, where the structural MRI is parcellated into anatomical regions;(iii) registration, where functional MRI is aligned to structural MRI;(iv) obtaining texture features and statistical analysis, where texture features are extracted from the anatomical brain regions. We adopted 68 subjects aged between 6 to 14 years, divided equally into TS and Normal Control groups. We evaluated the texture features in a statistical manner, where our main findings are: (i) After False Discovery Rate (FDR) correction, only one texture feature was significant (p-value < 0.08) in structural MRI;(ii) Following FDR correction, eight texture features in functional MRI for three anatomical regions were considered significant;(iii) The right amygdala presented significance in distinct texture features, matching its importance in the literature. Texture features aligned with the literature can serve as a reliable tool to identify imaging changes, which can lead to future work applied to clinical studies.

关键词： Brain image segmentation Visualization Three-dimensional displays Statistical analysis Functional magnetic resonance imaging Feature extraction

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Fractional Super-Resolution of Voxelized Point Clouds

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IEEE TRANSACTIONS ON image processing 2022年 31卷 1380-1390页

作者： Borges, Tomas M. Garcia, Diogo C. de Queiroz, Ricardo L. Univ Brasilia Elect Engn Dept BR-70910900 Brasilia DF Brazil Univ Brasilia Gama Engn Coll BR-72444240 Gama Brazil Univ Brasilia Comp Sci Dept BR-70910900 Brasilia DF Brazil

We present a method to super-resolve voxelized point clouds downsampled by a fractional factor, using lookup-tables (LUT) constructed from self-similarities from their own downsampled neighborhoods. The proposed method was developed to densify and to increase the precision of voxelized point clouds, and can be used, for example, as improve compression and rendering. We super-resolve the geometry, but we also interpolate texture by averaging colors from adjacent neighbors, for completeness. Our technique, as we understand, is the first specifically developed for intra-frame super-resolution of voxelized point clouds, for arbitrary resampling scale factors. We present extensive test results over different point clouds, showing the effectiveness of the proposed approach against baseline methods.

关键词： Geometry Point cloud compression Three-dimensional displays Rendering (computer graphics) Octrees Superresolution Spatial resolution Point clouds super-resolution resampling

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Complex-Valued Embedding on Grassmann Manifolds for Pattern Set Representation

Complex-Valued Embedding on Grassmann Manifolds for Pattern ...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Bernardo B. Gatto Juan G. Colonna MTI Ltd Universidade Federal do Amazonas

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Complex-valued data arise in various domains, including signal and image processing. Analyzing and classifying such data pose unique challenges due to their inherent structural and modal characteristics. In this paper, we propose the Complex-valued Subspace Embedding (CvSE), a novel approach for fast, compact, and physically meaningful representation of complex-valued pattern sets. Our method advances the analysis and classification of complex data by employing complex subspaces. We construct complex subspaces that capture the highest variance features by applying complex Singular Value Decomposition (SVD) on pattern sets, achieving compactness and noise robustness. We validate CvSE through experiments on MNIST, Fashion-MNIST, CIFAR-10, and Washington RGBD, showcasing its potential. Our findings demonstrate that CvSE excels in handling complex data, offering improved performance in the analysis and classification of pattern sets.

关键词： Manifolds graphics image processing Noise robustness Singular value decomposition

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Single-channel, ultraportable, real-time imaging system based on deep learning: a proof-of-concept

Single-channel, ultraportable, real-time imaging system base...

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2023 IEEE International Ultrasonics symposium, IUS 2023

作者： Meacci, Valentino Bosco, Edoardo Ramalli, Alessandro Boni, Enrico Tortoli, Piero Mazierli, Daniele Spairani, Edoardo Matrone, Giulia University of Florence Department of Information Engineering Florence Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350346459

There is a growing demand for user-friendly technologies that may empower individuals to independently monitor their physiological parameters. Ultrasound systems are highly promising for healthcare self-monitoring. To address these challenges, in this work, we present the proof of concept (PoC) of a real-time ultraportable system with a single-channel transmission and reception strategy and a deep learning-based image reconstruction method. The PoC uses the ULA-OP 256 scanner, employing a monostatic synthetic aperture focusing technique, along with a convolutional neural network (CNN) trained to generate B-mode images. Testing was carried out using a CNN running on a mid-range graphics processing unit (GPU), both on phantoms and in vivo scenarios, comparing the image quality achieved by the CNN with that of a delay-and-sum (DAS) beamformer. The results highlight that the CNN outperformed the DAS, showing a better image quality. Furthermore, the CNN achieved a real-time frame rate of 37.0 frames per second, proving that ultraportable ultrasound systems are highly promising for the future of self-monitoring instruments. © 2023 IEEE.

关键词： graphics processing unit

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34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Andrade, Natan Faria, Fabio A. Cappabianco, Fabio A. M. Univ Fed Sao Paulo GIBIS Dept Sci & Technol Av Cesare Monsueto Giulio Lattes 1201 BR-12247014 Sao Jose Dos Campos SP Brazil

ISBN: (纸本)9781665423540

Brain image registration fuses and aligns sets of structural or functional images within individual and population studies. The similarity metric is an image registration component used for detecting the same target region in different images. Multi-modal image registration constitutes one of the greatest challenges in medical imaging as it adds even more variability to the tissue and organ appearance, shape, and positioning. This paper contains two contributions to solve this complex problem: (1) we propose a solution to compute the similarity metric based on a deep ensemble method. It combines multiple traditional and deep similarity metrics into a single improved similarity map;(2) we propose novel evaluation metrics to validate the results. Experiment results in the context of T1- and T2-weighted MR images of the human brain show a major improvement to the state-of-the-art, especially in reducing the false-positive region occurrences.

关键词： Measurement image registration Brain Shape Fuses Sociology Statistics

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