- Email: [email protected]
Foreword to the Special Section on SIBGRAPI 2016
Computers & Graphics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
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Contents lists available at ScienceDirect
Computers & Graphics journal homepage: www.elsevier.com/locate/cag
Editorial
Foreword to the Special Section on SIBGRAPI 2016 art ic l e i nf o Keywords: Foreword Sibgrapi
1. Description SIBGRAPI – Conference on Graphics, Patterns, and Images – is annually promoted by the Brazilian Computer Society (SBC) through its special interest group on Graphics and Image Processing (CEGRAPI). Since 1988, this event gathers students, researchers and professionals to exchange experiences and research ideas regarding a broad set of areas in Visual Computing. SIBGRAPI 2016 was held on October 4–7 in the city of São José dos Campos, São Paulo, Brazil. For many years, extended versions of the very best papers presented at SIBGRAPI have been published in special issues of well-respected journals across the many areas covered by the conference. The Computers & Graphics Journal has being a longtime partner in that sense. In its 29th edition, for the first time, SIBGRAPI had an alternative track for paper submission to a Special Section of the Computers & Graphics Journal. Manuscripts submitted to the Special Section were subjected to at least two reviewing cycles with a rigorous peer-reviewing process. Eight papers successfully completed the reviewing process. Their research topics include global illumination [1], volumetric exploration [2], color mapping for diffusion tensor imaging [3], distributed volume rendering [4], computational geometry [5], and visual analytics [6–8]. Aguerre and Fernández [1] explore multiple singular value decompositions of the radiosity matrix and the Z-order curve to sort the patches of the model in order to solve the radiosity problem for fixed geometries at interactive times. The exploration of volumetric datasets for diagnostic and therapeutic purposes usually involves searching for regions of interest in the feature space. Barboza et al. [2] present a semiautomatic approach that uses a hierarchical structure computed from the 2D histogram of voxel data to identify similar volumetric regions. The resulting hierarchy can then be navigated using three simple operations, namely join, split and delete, allowing straightforward exploration of volumetric data, in real-time. Wu et al. [3] present a new line-coding color scheme for diffusion tensor imaging (DTI). The authors explore smooth transitions between primary and secondary colors in the classic RGB
http://dx.doi.org/10.1016/j.cag.2016.09.001 0097-8493/& 2016 Elsevier Ltd. All rights reserved.
color model to address the issue of the ambiguity found in conventional color-coding schemes for spatial line paths in DTI images. Interactive rendering of highly detailed 3D volumetric data of anatomical models via distributed volume rendering system has applications on surgical training and presurgical planning. PerezMonte et al. [4] describe a mathematical model of frame losses and present a performance evaluation comparing model predictions with experimental results of a heterogeneous parallel volume rendering system using Alternate Frame Rendering for frame distribution, and a best-effort rendering scheme. Shape matching and shape retrieval depend on the detection of robust shape descriptor. Affine invariance has been researched in the last few years for the development of invariant shape descriptors. Vieira et al. [5] introduce estimators for the affine structure of surfaces represented by triangle meshes. The identification of the impact of data attributes in pattern and group formation plays an important role in multidimensional data analysis and visualization. In their paper, Cedrim et al. [6] propose the use of non-parametric statistics (i.e., depth functions) as a quality measure for spatializations of multidimensional data. As an example, the authors describe how to use depth information to guide multidimensional projection techniques in data exploration. Garcia et al. [7], on the other hand, focus on the visualization of multidimensional data via a novel method called iStar, a method that combines attributes clustering and visualization resources to enable the analysis of high-dimensional data via Star Coordinates. Oliveira et al. [8] introduce an interactive visualization system to explore the dynamics of public bike sharing systems. The authors use data from the New York City's bike-sharing program to illustrate how their design supports the identification of several patterns in temporal and spatial domains. SIBGRAPI's scope covers many research areas, including Computer Graphics, Visual Analytics, Computer Vision, Pattern Recognition, and Image Processing. The conference is committed to attracting high quality works. The quality of the papers published in this Special Section highlights this continuous effort.
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Editorial / Computers & Graphics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
1 References 2 3 [1] Aguerre JP, Fernández E. A hierarchical factorization method for efficient radiosity calculations. Comput Graph 2016 [CAG-D-16-00045]. 4 Q3 [2] Barboza DP, Araujo MS, Marroquim R. Graph-based interactive volume 5 exploration. Comput Graph 2016 [CAG-D-16-00082]. 6 [3] Wu S-T, Voltoline R, Yasuda CL. A view-independent line-coding colormap for diffusion tensor imaging. Comput Graph 2016 [CAG-D-16-00068]. 7 [4] Perez-Monte CF, Rizzi S, Piccoli F, Luciano CJ, Perez MD. Modeling frame losses 8 in a parallel alternate frame rendering system with a computational best-effort 9 scheme. Comput Graph 2016 [CAG-D-16-00049]. [5] Vieira T, Martinez D, Andrade M, Lewiner T. Estimating affine-invariant struc10 tures on triangle meshes. Comput Graph 2016 [CAG-D-16-00057]. 11 [6] Cedrim D, Vad V, Paiva A, Gröller E, Nonato LG, Castelo Filho A. Depth functions 12 as a quality measure and for steering multidimensional projections. Comput Graph 2016 [CAG-D-16-00056]. 13 [7] Garcia G, Nonato LG, Gomez-Nieto E. iStar (in): an interactive star coordinates 14 approach for high-dimensional data exploration. Comput Graph 2016 [CAG-D15 16-00062]. 16 [8] Oliveira GN, Sotomayor JL, Torchelsen R, Silva CT, Comba JLD. Visual analysis of bike-sharing systems. Comput Graph 2016 [CAG-D-16-00070]. 17 18 19 20 Daniel G. Aliaga's research is primarily in the area of 21 3D computer graphics but overlaps with computer vision and with visualization. He focuses on (i) 3D 22 urban modeling (creating novel 3D urban acquisition 23 algorithms, forward and inverse procedural modeling, 24 and integration with urban design and planning), (ii) projector-camera systems (focusing on algorithms 25 for spatially augmented reality and for appearance 26 editing of arbitrarily shaped and colored objects), and 27 (iii) 3D digital fabrication (creating novel methods for digital manufacturing that embed into a physical object 28 information for genuinity detection, tamper detection, 29 and multiple appearance generation). Prof. Aliaga has 30 also performed research in 3D reconstruction, image-based rendering, rendering acceleration, and camera design and calibration. Dr. Aliaga's first computer graphics 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
publication was in 1990 and since has published over 100 peer reviewed publications, been a member of more than 50 program committees, as well as conference chair, papers chair, invited speaker, and invited panelist. In addition, Dr. Aliaga has served on several NSF panels, is on the editorial board of Computer Graphics Forum and of Graphical Models, and is a member of ACM SIGGRAPH and is an ACM SIGGRAPH Pioneer. His research has been whole or partially funded by NSF, MTC, Microsoft Research, Google, and Adobe Inc.
Leandro A.F. Fernandes is an Associate Professor at the Fluminense Federal University (UFF), in Brazil. He received a Bachelor of Science degree in Computer Science from FURB in 2002, and Master of Science and Doctor of Computer Science degrees from Federal University of Rio Grande do Sul (UFRGS) in 2006 and 2010, respectively. Leandro is co-head of the Graphics Processing Research Laboratory (Prograf) at UFF. His research interests span many sub-areas in Computer Graphics, including real-time rendering, image-based rendering, image-based modeling, image processing, and computer vision.
Daniel G. Aliaga Department of Computer Science, Purdue University, USA E-mail address: [email protected] URL: https://www.cs.purdue.edu/homes/aliaga Leandro A.F. Fernandes Department of Computer Science, Fluminense Federal University, Brazil E-mail address: [email protected] URL: http://www.ic.uff.br/ laffernandes Received 6 September 2016; accepted 9 September 2016
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Contents lists available at ScienceDirect
Computers & Graphics journal homepage: www.elsevier.com/locate/cag
Editorial
Foreword to the Special Section on SIBGRAPI 2016 art ic l e i nf o Keywords: Foreword Sibgrapi
1. Description SIBGRAPI – Conference on Graphics, Patterns, and Images – is annually promoted by the Brazilian Computer Society (SBC) through its special interest group on Graphics and Image Processing (CEGRAPI). Since 1988, this event gathers students, researchers and professionals to exchange experiences and research ideas regarding a broad set of areas in Visual Computing. SIBGRAPI 2016 was held on October 4–7 in the city of São José dos Campos, São Paulo, Brazil. For many years, extended versions of the very best papers presented at SIBGRAPI have been published in special issues of well-respected journals across the many areas covered by the conference. The Computers & Graphics Journal has being a longtime partner in that sense. In its 29th edition, for the first time, SIBGRAPI had an alternative track for paper submission to a Special Section of the Computers & Graphics Journal. Manuscripts submitted to the Special Section were subjected to at least two reviewing cycles with a rigorous peer-reviewing process. Eight papers successfully completed the reviewing process. Their research topics include global illumination [1], volumetric exploration [2], color mapping for diffusion tensor imaging [3], distributed volume rendering [4], computational geometry [5], and visual analytics [6–8]. Aguerre and Fernández [1] explore multiple singular value decompositions of the radiosity matrix and the Z-order curve to sort the patches of the model in order to solve the radiosity problem for fixed geometries at interactive times. The exploration of volumetric datasets for diagnostic and therapeutic purposes usually involves searching for regions of interest in the feature space. Barboza et al. [2] present a semiautomatic approach that uses a hierarchical structure computed from the 2D histogram of voxel data to identify similar volumetric regions. The resulting hierarchy can then be navigated using three simple operations, namely join, split and delete, allowing straightforward exploration of volumetric data, in real-time. Wu et al. [3] present a new line-coding color scheme for diffusion tensor imaging (DTI). The authors explore smooth transitions between primary and secondary colors in the classic RGB
http://dx.doi.org/10.1016/j.cag.2016.09.001 0097-8493/& 2016 Elsevier Ltd. All rights reserved.
color model to address the issue of the ambiguity found in conventional color-coding schemes for spatial line paths in DTI images. Interactive rendering of highly detailed 3D volumetric data of anatomical models via distributed volume rendering system has applications on surgical training and presurgical planning. PerezMonte et al. [4] describe a mathematical model of frame losses and present a performance evaluation comparing model predictions with experimental results of a heterogeneous parallel volume rendering system using Alternate Frame Rendering for frame distribution, and a best-effort rendering scheme. Shape matching and shape retrieval depend on the detection of robust shape descriptor. Affine invariance has been researched in the last few years for the development of invariant shape descriptors. Vieira et al. [5] introduce estimators for the affine structure of surfaces represented by triangle meshes. The identification of the impact of data attributes in pattern and group formation plays an important role in multidimensional data analysis and visualization. In their paper, Cedrim et al. [6] propose the use of non-parametric statistics (i.e., depth functions) as a quality measure for spatializations of multidimensional data. As an example, the authors describe how to use depth information to guide multidimensional projection techniques in data exploration. Garcia et al. [7], on the other hand, focus on the visualization of multidimensional data via a novel method called iStar, a method that combines attributes clustering and visualization resources to enable the analysis of high-dimensional data via Star Coordinates. Oliveira et al. [8] introduce an interactive visualization system to explore the dynamics of public bike sharing systems. The authors use data from the New York City's bike-sharing program to illustrate how their design supports the identification of several patterns in temporal and spatial domains. SIBGRAPI's scope covers many research areas, including Computer Graphics, Visual Analytics, Computer Vision, Pattern Recognition, and Image Processing. The conference is committed to attracting high quality works. The quality of the papers published in this Special Section highlights this continuous effort.
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Editorial / Computers & Graphics ∎ (∎∎∎∎) ∎∎∎–∎∎∎
1 References 2 3 [1] Aguerre JP, Fernández E. A hierarchical factorization method for efficient radiosity calculations. Comput Graph 2016 [CAG-D-16-00045]. 4 Q3 [2] Barboza DP, Araujo MS, Marroquim R. Graph-based interactive volume 5 exploration. Comput Graph 2016 [CAG-D-16-00082]. 6 [3] Wu S-T, Voltoline R, Yasuda CL. A view-independent line-coding colormap for diffusion tensor imaging. Comput Graph 2016 [CAG-D-16-00068]. 7 [4] Perez-Monte CF, Rizzi S, Piccoli F, Luciano CJ, Perez MD. Modeling frame losses 8 in a parallel alternate frame rendering system with a computational best-effort 9 scheme. Comput Graph 2016 [CAG-D-16-00049]. [5] Vieira T, Martinez D, Andrade M, Lewiner T. Estimating affine-invariant struc10 tures on triangle meshes. Comput Graph 2016 [CAG-D-16-00057]. 11 [6] Cedrim D, Vad V, Paiva A, Gröller E, Nonato LG, Castelo Filho A. Depth functions 12 as a quality measure and for steering multidimensional projections. Comput Graph 2016 [CAG-D-16-00056]. 13 [7] Garcia G, Nonato LG, Gomez-Nieto E. iStar (in): an interactive star coordinates 14 approach for high-dimensional data exploration. Comput Graph 2016 [CAG-D15 16-00062]. 16 [8] Oliveira GN, Sotomayor JL, Torchelsen R, Silva CT, Comba JLD. Visual analysis of bike-sharing systems. Comput Graph 2016 [CAG-D-16-00070]. 17 18 19 20 Daniel G. Aliaga's research is primarily in the area of 21 3D computer graphics but overlaps with computer vision and with visualization. He focuses on (i) 3D 22 urban modeling (creating novel 3D urban acquisition 23 algorithms, forward and inverse procedural modeling, 24 and integration with urban design and planning), (ii) projector-camera systems (focusing on algorithms 25 for spatially augmented reality and for appearance 26 editing of arbitrarily shaped and colored objects), and 27 (iii) 3D digital fabrication (creating novel methods for digital manufacturing that embed into a physical object 28 information for genuinity detection, tamper detection, 29 and multiple appearance generation). Prof. Aliaga has 30 also performed research in 3D reconstruction, image-based rendering, rendering acceleration, and camera design and calibration. Dr. Aliaga's first computer graphics 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
publication was in 1990 and since has published over 100 peer reviewed publications, been a member of more than 50 program committees, as well as conference chair, papers chair, invited speaker, and invited panelist. In addition, Dr. Aliaga has served on several NSF panels, is on the editorial board of Computer Graphics Forum and of Graphical Models, and is a member of ACM SIGGRAPH and is an ACM SIGGRAPH Pioneer. His research has been whole or partially funded by NSF, MTC, Microsoft Research, Google, and Adobe Inc.
Leandro A.F. Fernandes is an Associate Professor at the Fluminense Federal University (UFF), in Brazil. He received a Bachelor of Science degree in Computer Science from FURB in 2002, and Master of Science and Doctor of Computer Science degrees from Federal University of Rio Grande do Sul (UFRGS) in 2006 and 2010, respectively. Leandro is co-head of the Graphics Processing Research Laboratory (Prograf) at UFF. His research interests span many sub-areas in Computer Graphics, including real-time rendering, image-based rendering, image-based modeling, image processing, and computer vision.
Daniel G. Aliaga Department of Computer Science, Purdue University, USA E-mail address: [email protected] URL: https://www.cs.purdue.edu/homes/aliaga Leandro A.F. Fernandes Department of Computer Science, Fluminense Federal University, Brazil E-mail address: [email protected] URL: http://www.ic.uff.br/ laffernandes Received 6 September 2016; accepted 9 September 2016
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