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检索条件"任意字段=SIBGRAPI 2006: 19th Brazilian Symposium on Computer Graphics and Image Processing"

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A Statistical Discriminant Model for Face Interpretation and Reconstruction

A Statistical Discriminant Model for Face Interpretation and...

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brazilian symposium on computer graphics and image processing (sibgrapi)

作者： Edson C. Kitani Carlos E. thomaz Duncan F. Gillies Department of Electrical Engineering Centro Universitário da FEI Sao Paulo Brazil Department of Computing Imperial College London London UK

Multivariate statistical approaches have played an important role of recognising face images and characterizing their differences. In this paper, we introduce the idea of using a two-stage separating hyper-plane, here called statistical discriminant model (SDM), to interpret and reconstruct face images. Analogously to the well-known active appearance model proposed by Cootes et. al, SDM requires a previous alignment of all the images to a common template to minimise variations that are not necessarily related to differences between the faces. However, instead of using landmarks or annotations on the images, SDM is based on the idea of using PCA to reduce the dimensionality of the original images and a maximum uncertainty linear classifier (MLDA) to characterise the most discriminant changes between the groups of images. the experimental results based on frontal face images indicate that the SDM approach provides an intuitive interpretation of the differences between groups, reconstructing characteristics that are very subjective in human beings, such as beauty and happiness

关键词： Principal component analysis Pixel image reconstruction Covariance matrix Face recognition image recognition Uncertainty Shape Active appearance model Statistical analysis

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Interpolation of computerized Tomography Slices using 3D Digital Inpainting

Interpolation of Computerized Tomography Slices using 3D Dig...

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brazilian symposium on computer graphics and image processing (sibgrapi)

作者： Sandrerley R. Pires Edna L. Flores Celia A. Z. Barcelos Marcos A. Batista Alves Faria Goiania Brazil Federal University of Uberlândia UFU Uberlandia Minas Gerais Brazil Federal University of Goiás Catalao Brazil

the visualization of image structures in 3D obtained from computerized tomography examinations aids the medical professional in the analysis of images and consequently, provides a more accurate diagnoses. As these images (slices) are spaced apart it becomes necessary to fill in the empty spaces to show the structure in 3D. the use of the virtual slice between the real slices following the restoration is a new approach to realizing slice interpolation aimed at 3D visualization. the goal of this article is to develop a method which produces a virtual slice with few empty regions and, through the use of an in-painting process using transportation and diffusion of information with a partial differential equation, complete the virtual slices. the experimental results, presented by images 2D and 3D show the efficiency of the proposed method

关键词： Interpolation Computed tomography Visualization Biomedical imaging image restoration image analysis Transportation Medical diagnostic imaging Partial differential equations image reconstruction

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Improving 2D mesh image segmentation with Markovian Random Fields

Improving 2D mesh image segmentation with Markovian Random F...

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brazilian symposium on computer graphics and image processing (sibgrapi)

作者： Alex J. Cuadros-vargas Leandro C. Gerhardinger Mario de Castro Joao Batista Neto Luis Gustavo Nonato Instituto de Ciencias Matematicas e de Computacao - USP Sao Carlos SP Brazil Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR

Traditional mesh segmentation methods normally operate on geometrical models with no image information. On the other hand, 2D image-based mesh generation and segmentation counterparts, such as Imesh (A. Cuadros-Vargas et. al, 2005) perform the task by following a set of well defined rules derived from the geometry of the triangles, but with no statistical information of the mesh elements. this paper presents a novel segmentation method that combines the original Imesh image-based segmentation approach with Markovian random field (MRF) models. It takes an image as input, generate a mesh of triangles and, by treating the mesh as a Markovian field, produces quality unsupervised segmentation. the results have demonstrated that the method not only provides better segmentation than that of original Imesh, but is also capable of producing MRF-like segmentation output for certain types of images, with considerable cut in processing times

关键词： image segmentation Mesh generation Pixel Solid modeling Biomedical imaging Information geometry image generation Stochastic processes Remote sensing Computed tomography

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Polygonization of volumetric reconstructions from silhouettes

Polygonization of volumetric reconstructions from silhouette...

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brazilian symposium on computer graphics and image processing (sibgrapi)

作者： Anselmo Antunes Montenegro Luiz Velho Paulo C. P. Carvalho Jonas Jr. Sossai Departamento de Informatica Ciencia da Computacao-IME-UERJ Brazil IMPA-Instituto Nacional de Matemática Pura e Aplicada Brazil

In this work we propose a method for the polygonization of octree-based reconstructions by dual contouring. Dual contouring is an adaptive method for determining contiguous polygonal meshes from signed octrees. It determines the positioning of the vertices of the mesh by minimizing a quadratic error expressed in terms of hermitian data. In order to apply dual contouring on volumetric reconstruction from silhouettes we devised a method that is able to determine the discrete topology of the contour in relation to the octree cells, as well as the hermitian data corresponding to the intersections and normals of conic volumes whose intersection approximates a structure known as visual hull. Due to the discrete and extremely noisy nature of the data used in the reconstruction we had to devise a different criterion for mesh simplification that applies topological consistency tests only when the geometric error measure is beyond a given tolerance. We present results of the application of the proposed method in the extraction of a mesh corresponding to the surface of objects of a real scene

关键词： image reconstruction Layout Surface reconstruction Data mining Shape Photography Cameras Topology Testing Application software

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Video Segmentation by Supervised Learning

Video Segmentation by Supervised Learning

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brazilian symposium on computer graphics and image processing (sibgrapi)

作者： G. Camara Chavez M. Cord F. Precioso S. Philipp-Foliguet Arnaldo De A. Araujo Federal University of Minas Gerais Computer Science Department Av. Antônio Carlos MG Brazil Equipe Traitment des Images et du Signal-ENSEA Cergy-Pontoise France Equipe Traitment des Images et du Signal-ENSEA France Federal University of Minas Gerais

In most of video shot boundary detection algorithms, proposed in the literature, several parameters and thresholds have to be set in order to achieve good results. In this paper, to get rid of parameters and thresholds, we explore a supervised classification method for video shot segmentation. We transform the temporal segmentation into a class categorization issue. Our approach defines a uniform framework for combining different kinds of features extracted from the video. Our method does not require any pre-processing step to compensate motion or post-processing filtering to eliminate false detected transitions. the experiments, following strictly the TRECVID 2002 competition protocol, provide very good results dealing with a large amount of features thanks to our kernel-based SVM classification method

关键词： Supervised learning Support vector machines Support vector machine classification Histograms Feature extraction Gunshot detection systems Statistical learning Machine learning Detection algorithms Motion detection

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Two-Level Interaction Transfer Function Design Combining Boundary Emphasis, Manual Specification and Evolutive Generation

Two-Level Interaction Transfer Function Design Combining Bou...

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brazilian symposium on computer graphics and image processing (sibgrapi)

作者： Francisco De Moura Pinto Carla M.D.S. Freitas Instituto de Informatica Universidade Federal do Rio Grande do Sul Instituto de Informática Universidade Federal do Rio Grande do Sul Brazil

Direct volume rendering plays an important role in the investigation of volumetric data. In order to display these data, their values must be associated to optical properties. this task is accomplished by transfer functions (TFs), which assign properties like color and opacity to voxel values in the volume. TFs are very important to produce informative images that reveal volume features, but their specification is a non-trivial and unintuitive problem. Without any help in the TF design process, the user goes through a frustrating and time-consuming trial-and-error process. this work proposes a two-level interactive framework combining a few useful semi-automatic design techniques for one-dimensional opacity and color transfer functions. We use the histogram approach proposed by Kindlmann and Durkin, manual design aided by dual domain interaction (space domain and TF domain), stochastic evolutive design and qualitative analysis of multiple TF possibilities using a design galleries-inspired method (thumbnails in the first level of interaction and refinement of zoomed-in images in the second level). the combination of these strategies offers precise control over the transfer function specification, plus some interactive guidance, as well as semi-automatic and evolutive search in the TF space

关键词： Transfer functions Rendering (computer graphics) Stochastic processes image analysis Displays Design methodology Data visualization computer graphics Isosurfaces Data mining

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Non-Extensive Entropy for CAD Systems of Breast Cancer images

Non-Extensive Entropy for CAD Systems of Breast Cancer Image...

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brazilian symposium on computer graphics and image processing (sibgrapi)

作者： J.S. Suri Ruey-Feng Chang G.A. Giraldi P.S. Rodrigues National Lab. for Sci. Comput. Rio de Janeiro

Recent statistics show that breast cancer is a major cause of death among women in all of the world. Hence, early diagnostic with computer aided diagnosis (CAD) systems is a very important tool. this task is not easy due to poor ultrasound resolution and large amount of patient data size. then, initial image segmentation is one of the most important and challenging task. Among several methods for medical image segmentation, the use of entropy for maximization the information between the foreground and background is a well known and applied technique. But, the traditional Shannon entropy fails to describe some physical systems with characteristics such as long-range and longtime interactions. then, a new kind of entropy, called non-extensive entropy, has been proposed in the literature for generalizing the Shannon entropy. In this paper, we propose the use of non-extensive entropy, also called q-entropy, applied in a CAD system for breast cancer classification in ultrasound of mammographic exams. Our proposal combines the non-extensive entropy, a level set formulation and a support vector machine framework to achieve better performance than the current literature offers. In order to validate our proposal, we have tested our automatic protocol in a data base of 250 breast ultrasound images (100 benign and 150 malignant). With a cross-validation protocol, we demonstrate system's accuracy, sensitivity, specificity, positive predictive value and negative predictive value as: 95%, 97%, 94%, 92% and 98%, respectively, in terms of ROC (receiver operating characteristic) curves and A z areas

关键词： Entropy Breast cancer Ultrasonic imaging image segmentation Proposals Protocols Statistics Biomedical imaging Medical diagnostic imaging Level set

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Determining the branchings of 3D structures from respective 2D projections

Determining the branchings of 3D structures from respective ...

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brazilian symposium on computer graphics and image processing (sibgrapi)

作者： Jorge J. G. Leandro Roberto M. Jr. Cesar Luciano Da Fontoura Costa Institute of Mathematics and Statistics Department of Computer Science USP Sao Paulo Brazil Instituto de Física de São Carlos Department of Physics and Informatics USP Sao Paulo Brazil

this work describes a new framework for automatic extraction of 2D branching structures images obtained from 3D shapes, such as neurons and retinopathy images. the majority of methods for neuronal cell shape analysis that are based on the 2D contours of cells fall short of properly characterizing such cells because crossings among neuronal processes constrain the access of contour following algorithms to the innermost regions of the cell. the framework presented in this article addresses, possibly for the first time, the problem of determining the continuity along crossings, therefore granting to the contour following algorithm full access to all processes of the neuronal cell under analysis. First, the raw image is preprocessed so as to obtain an 8-connected, one-pixel wide skeleton as well as a set of seed pixels for each subtree and all the branching/crossing regions. then, for each seed pixel, the algorithm labels all valid neighbors, until a branching/crossing region is reached, when a decision about the proper continuation is taken based on the tangent continuity. the algorithm has shown robustness for images with parallel segments and low densities of branching/crossing densities. the problem of too high densities of branching/crossing regions can be addressed by using a suitable data structure. Successful experimental results using real data (neural cell images) are presented

关键词： Genetics Shape measurement Neurons Algorithm design and analysis Animals Nervous system Mathematics Statistics computer science Retinopathy

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Extracting Discriminative Information from Medical images: A Multivariate Linear Approach

Extracting Discriminative Information from Medical Images: A...

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brazilian symposium on computer graphics and image processing (sibgrapi)

作者： Carlos E. thomaz Nelson A.O. Aguiar Sergio H.A. Oliveira Fabio L.S. Duran Geraldo F. Busatto Duncan F. Gillies Daniel Rueckert Department of Electrical Engineering Centro Universitario da FEI Sao Paulo Brazil Departments of Psychiatry and Radiology Faculty of Medicine University of São Paulo Brazil Department of Computing Imperial College London London UK

Statistical discrimination methods are suitable not only for classification but also for characterization of differences between a reference group of patterns and the population under investigation. In the last years, statistical methods have been proposed to classify and analyze morphological and anatomical structures of medical images. Most of these techniques work in high-dimensional spaces of particular features such as shapes or statistical parametric maps and have overcome the difficulty of dealing with the inherent high dimensionality of medical images by analyzing segmented structures individually or performing hypothesis tests on each feature separately. In this paper, we present a general multivariate linear framework to identify and analyze the most discriminating hyper-plane separating two populations. the goal is to analyze all the intensity features simultaneously rather than segmented versions of the data separately or feature-by-feature. the conceptual and mathematical simplicity of the approach, which pivotal step is spatial normalization, involves the same operations irrespective of the complexity of the experiment or nature of the data, giving multivariate results that are easy to interpret. To demonstrate its performance we present experimental results on artificially generated data set and real medical data

关键词： Data mining Biomedical imaging image analysis image segmentation Statistical analysis Anatomical structure Shape Performance analysis Performance evaluation Medical tests

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Presenter-Centric image Collection and Annotation: Enhancing Accessibility for the Visually Impaired

Presenter-Centric Image Collection and Annotation: Enhancing...

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brazilian symposium on computer graphics and image processing (sibgrapi)

作者： Luísa Ferreira Daniel Fernandes Fabio Cerqueira Marcos Ribeiro Michel Silva Department of Informatics Universidade Federal de Viçosa - UFV Viçosa Brazil Department of Production Engineering Universidade Federal Fluminense - UFF Petrópolis Brazil

With the recent COVID-19 pandemic, our daily lives and workplace became more dependent on technologies, e.g., the intensive use of video conferences. While a highly connected world enables remote working, it also raises new barriers for an already excluded community, the visually impaired people. Low or no eyesight prevents those people from capturing visual infor-mation, which makes it difficult to understand the overall context of a remote presentation. Most Artificial Intelligence methods are specific to sighted-people data domains due to the scarcity of datasets for the visually impaired. In this paper, we propose an approach to collect data automatically and a protocol to annotate this data specifically for this audience, aiming to support the development of Assistive Technology systems. We demonstrate the viability of the proposed methods by creating a dataset and evaluating its quality, diversity, and representativeness through analytical methods and machine learning models.

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