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Proceedings: 26th sibgrapi - conference on graphics, patterns and images tutorials, sibgrapi-t 2013

Proceedings: 26th SIBGRAPI - Conference on Graphics, Pattern...

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26th sibgrapi - conference on graphics, patterns and images tutorials, sibgrapi-t 2013

the proceedings contain 4 papers. the topics discussed include: getting started with videogame development;visual immersion issues in virtual reality: a survey;remote eye tracking systems: technologies and application...

ISBN: (纸本)9781479941766

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Proceedings - 2013 26th conference on graphics, patterns and images, sibgrapi 2013

Proceedings - 2013 26th Conference on Graphics, Patterns and...

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2013 26th conference on graphics, patterns and images, sibgrapi 2013

ISBN: (纸本)9780769550992

the proceedings contain 47 papers. the topics discussed include: finger spelling recognition from RGB-D information using kernel descriptor;feature learning by multidimensional scaling and its applications in object recognition;classifier selection based on the correlation of diversity measures: when fewer is more;using unsupervised learning for graph construction in semi-supervised learning with graphs;a multipotential field model for crowds with scalable behaviors;applications of conformal geometric algebra in mesh deformation;multiphase flow of incompressible fluids employing regional level set and volume control;exploiting space and time coherence in grid-based sorting;change detection in human crowds;a tensor motion descriptor based on multiple gradient estimators;and real-time object tracking in high-definition video using frame segmentation and background integral images.

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Real-time object tracking in high-definition video using frame segmentation and background integral images

Real-time object tracking in high-definition video using fra...

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2013 26th conference on graphics, patterns and images, sibgrapi 2013

作者： Moreira, Gustavo Feijo, Bruno Lopes, Helio Feitosa, Raul Queiroz Departamento de Informatica Pontificia Universidade Catolica Do Rio de Janeiro Rio de Janeiro RJ Brazil Departamento de Engenharia Eletrica Pontificia Universidade Catolica Do Rio de Janeiro Rio de Janeiro RJ Brazil

ISBN: (纸本)9780769550992

Object tracking in video sequences is still a challenging issue in real-time video processing. In this paper we propose an integrated detection and tracking method suitable for high-definition videos at real-time frame rates. In this method we implement a frame segmentation procedure using integral images of the background, which permits to discard the analysis of several image parts of each frame and achieve high frame rates. Also we extend the proposed algorithm to detect multiple objects in parallel. © 2013 IEEE.

关键词： tracking (position)

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Streamline-based topological graph construction with application to self-animated images

Streamline-based topological graph construction with applica...

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2013 26th conference on graphics, patterns and images, sibgrapi 2013

作者： Nascimento, Renata Lewiner, thomas Department of Mathematics Pontificia Universidade Catolica Do Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9780769550992

Vector field analysis and visualization is a fundamental tool in science and engineering applications, raising the need for robust methods to capture and represent the global vector field behavior. One such representation, the topological graph, partitions the domain into regions where the flow of the vector field is homogeneous. this work introduces a topological graph construction based on streamlines. It is guaranteed to produce a coherent result even when some singularities are not detected. this work also details an application of topological graphs to improve the generation of self-animated images. In this application, the streamline-based approach carries almost no overhead, since self-animated images already rely on streamlines, but leads to a threefold speed-up of the core processing. © 2013 IEEE.

关键词： Visualization

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Ranking methods for tensor components analysis and their application to face images

Ranking methods for tensor components analysis and their app...

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2013 26th conference on graphics, patterns and images, sibgrapi 2013

作者： Filisbino, tiene A. Giraldi, Gilson A. thomaz, Carlos Eduardo National Laboratory for Scientific Computing LNCC Petropolis Brazil Department of Electrical Engineering FEI Sao Bernardo do Campo Brazil

ISBN: (纸本)9780769550992

Higher order tensors have been applied to model multidimensional image databases for subsequent tensor decomposition and dimensionality reduction. In this paper we address the problem of ranking tensor components in the context of the concurrent subspace analysis (CSA) technique following two distinct approaches: (a) Estimating the covariance structure of the database, (b) Computing discriminant weights through separating hyper planes, to select the most discriminant CSA tensor components. the former follows a ranking method based on the covariance structure of each subspace in the CSA framework while the latter addresses the problem through the discriminant principal component analysis methodology. Both approaches are applied and compared in a gender classification task performed using the FEI face database. Our experimental results highlight the low dimensional data representation of both approaches, while allowing robust discriminant reconstruction and interpretation of the sample groups and high recognition rates. © 2013 IEEE.

关键词： Principal component analysis

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Explorable volumetric depth images from raycasting

Explorable volumetric depth images from raycasting

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2013 26th conference on graphics, patterns and images, sibgrapi 2013

作者： Frey, Steffen Sadlo, Filip Ertl, thomas Visualization Research Institute University of Stuttgart Germany

ISBN: (纸本)9780769550992

View-dependent image-based rendering techniques have become increasingly popular as they combine the high quality of images with the explorability of interactive techniques. However, in the context of volume rendering, previous approaches suffer from various shortcomings, including the limitation to surfaces, expensive generation, and insufficient occlusion and motion parallax impairing depth perception. In this paper, we propose Volumetric Depth images (VDI) to overcome these issues for view-dependent volume visualization by an extension of the Layered Depth Image (LDI) approach. Instead of only saving for each view ray of one camera configuration the depth and color values for a set of surfaces, as in LDIs, VDIs store so-called super segments, each consisting of a depth range as well as composited color and opacity. this compact representation is independent from the structure of the original data and can be generated by slight modification of ray casters with very low overhead. VDIs can be rendered efficiently at high quality with arbitrary camera configurations by means of proxy frustum geometry and an efficient depth ordering scheme. When viewing the scene from the initial view point, VDIs produce results identical to the original ray casting. As demonstrated by means of a prototype implementation and data from different fields, our approach can be useful for preview rendering and a-priori analysis in in-situ contexts among others. © 2013 IEEE.

关键词： Depth perception

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A Deep Learning Approach for Classification of Reaching targets from EEG images 30

A Deep Learning Approach for Classification of Reaching Targ...

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

作者： Carvalho, Schubert R. Filho, Iraquitan C. Resende, Damares O. Siravenha, Ana C. de Souza, Cleidson R. B. Debarba, Henrique Gomes, Bruno D. Boulic, Ronan ITV Lab Comp Aplicada Belem Para Brazil Univ Fed Para UFPA Inst Ciencias Biol Belem Para Brazil Artanim Fdn Geneva Switzerland Ecole Polytech Fed Lausanne Immers Interact Grp Lausanne Switzerland

ISBN: (纸本)9781538622193

In this paper, we propose a new approach for the classification of reaching targets before movement onset, during visually-guided reaching in 3D space. Our approach combines the discriminant power of two-dimensional Electroencephalography (EEG) signals (i.e., EEG images) built from short epochs, with the feature extraction and classification capabilities of deep learning (DL) techniques, such as the Convolutional Neural Networks (CNN). In this work, reaching motions are performed into four directions: left, right, up and down. to allow more natural reaching movements, we explore the use of Virtual Reality (VR) to build an experimental setup that allows the subject to perform self-paced reaching in 3D space while standing. Our results reported an increase both in classification performance and early detection in the majority of our experiments. to our knowledge this is the first time that EEG images and CNN are combined for the classification of reaching targets before movement onset.

关键词： Brain-Computer Interface Deep Learning EEG Imagens

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