.: SIBGRAPI 2013 - Invited Speakers :.
Manuel M. Oliveira
Title: Efficient Edge-Aware Filtering
Abstract: Edge-aware filtering is a fundamental building block to a wide range of image and video processing, and computer graphics applications. These include detail enhancement, denoising, stylization and non-photorealistic effects, recoloring and colorization, tone mapping, and photon-map filtering, just to name a few. While an edge-preserving filter can be implemented as a convolution with a spatially-invariant kernel in high-dimensional space, performing such an operation is computationally expensive, preventing its use in interactive and real-time scenarios. The talk will present two recent techniques we have developed for efficiently performing edge-aware filtering. The first one is based on a domain transform that defines an isometry between curves on 2-D image manifolds (embedded in n-D space) and the real line. High-dimensional geodesic filtering is then performed in linear time as a sequence of 1-D filtering steps using a spatially-invariant kernel. The second technique focuses on Euclidean filtering. It works by sampling and filtering the input signal using a set of 2-D manifolds adapted to the original data. It essentially queries the value of a multivariate function in n-D by interpolating several scattered samples using normalized convolution. Its cost is linear both in the number of pixels and in the dimensionality of the space in which the filter operates. These techniques have many desirable features. In particular, they are significantly faster than previous approaches, supporting high-dimensional filtering of images and videos in real time. In the talk, I will show several examples illustrating their use in image, video processing, and computer graphics applications.
Short Bio - Manuel M. Oliveira Manuel M. Oliveira is an Associate Professor of Computer Science at the Federal University of Rio Grande do Sul (UFRGS), in Brazil. He received his PhD from the University of North Carolina at Chapel Hill, in 2000. Before joining UFRGS in 2002, he was an Assistant Professor of Computer Science at the State University of New York at Stony Brook (2000 to 2002). In the 2009-2010 academic year, he was a Visiting Associate Professor at the MIT Media Lab. His research interests cover most aspects of computer graphics, but especially the frontiers among graphics, image processing, and vision (both human and machine). In these areas, he has contributed a variety of techniques including relief texture mapping, real-time filtering in high-dimensional spaces, efficient algorithms for Hough transform, new physiologically-based models for color perception and pupil-light reflex, and novel interactive techniques for measuring visual acuity. His work has been marked by a quest for solutions that produce high-quality results in real time. Manuel was program co-chair of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games 2010 (I3D 2010), and general co-chair of ACM I3D 2009. He was also program co-chair of the WSCG 2013 and SIBGRAPI 2006. He received the ACM Recognition of Service Award in 2009 and 2010.
Title: Trends in Visual Computing
Abstract: Data Visualization uses computer-supported, interactive, visual representations of (abstract) data to amplify cognition. Visualization is an essential part of Visual Computing which in turn is concerned with the acquisition, representation, processing, analysis, synthesis, and usage of visual information. The talk discusses various challenges in visual computing. In recent years data complexity and variability has increased considerably. This is due to new data sources as well as the availability of uncertainty, error and tolerance information. Visual steering supports decision making in the presence of alternative scenarios. Multiple, related simulation runs are explored through branching operations. To account for uncertain knowledge about the input parameters, visual reasoning employs entire parameter distributions. This can lead to an uncertainty-aware exploration of (continuous) parameter spaces. Multivariate and heterogeneous data call for visual analysis and knowledge-assisted interaction. To cope with intensified scalability issues and distributed processing approaches, advanced strategies are required like comparative visualization, integrated views and inclusion of fuzzy sets in the visualization process. Examples concerning the aforementioned topics will be presented in the talk.
Short Bio - Eduard Gröller Eduard Gröller (http://www.cg.tuwien.ac.at/staff/EduardGroeller.html) is professor at the Institute of Computer Graphics and Algorithms (ICGA), Vienna University of Technology. In 1993 he received his PhD from the same university. His research interests include computer graphics, visualization, and visual computing. He is heading the visualization group at ICGA. The group performs basic and applied research projects in all areas of visualization (http://www.cg.tuwien.ac.at/research/vis/). Dr. Gröller has given lecture series on visualization at various other universities (Tübingen, Graz, Praha, Bahia Blanca, Magdeburg, Bergen). He is a scientific proponent and key researcher of the VRVis research center (http://www.vrvis.at/) The center performs applied research in visualization, rendering, and visual analysis. Dr. Gröller is adjunct professor of computer science at the University of Bergen, Norway (since 2005). He co-authored more than 200 scientific publications and acted as a reviewer for numerous conferences and journals in the field. He also has served and serves on various program and paper committees. Examples include Computers&Graphics, IEEE Transactions on Visualization and Graphics, EuroVis conference, IEEE Visualization conference, Eurographics conference. He has been paper co-chair of Volume Graphics 2005, IEEE Visualization 2005 and 2006, and Eurographics 2006. He has been co-chair of the VisSym1999 symposium, the Eurographics 2011 conference, and the EuroVis 2012 conference. Dr. Gröller has been chief editor of the Journal Computer Graphics Forum (http://wileyonlinelibrary.com/journal/cgf) in the period 2008-2011. He became a fellow of the Eurographics association in 2009. Dr. Gröller is head of the working group on computer graphics of the Austrian Computer Society and member of IEEE Computer Society, ACM (Association of Computing Machinery), GI (Gesellschaft für Informatik), OCG (Austrian Computer Society).
Title: Establishing Good Benchmarks and Baselines in Artificial and Biological Vision
Abstract: Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain's anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, ostensibly "natural" images have become popular in the study of vision, and have been used to show apparently impressive progress in building such models. In this talk, we will demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially hindering progress and guiding the community in the wrong directions. Instead, we re-examine what it means for images to be natural and argue for a renewed focus on the core problem of object recognition -- real-world image variation.
Short Bio - Nicolas Pinto Nicolas Pinto is currently Chief Scientist and Chief Technology Officer of two Silicon Valley stealth startups, focusing on the research and development of human-level brain-inspired perception technologies and their real-time applications on low-power embedded devices. He holds two M.S. in Computer Science and Engineering from France (UTBM/ENSISA, 2007), and a Ph.D. in Neuroscience from the USA (MIT, 2010) supported by NSF, DARPA, NVIDIA, Google, Amazon and Microsoft. Previously he was a graduate-level Lecturer in Computer Science at Harvard SEAS/DCE teaching Massively Parallel Computing, and a Research Scientist in Prof. Jim DiCarlo's Lab at MIT and Prof. David Cox's Lab at Harvard developing large-scale computational models of the visual cortex. Nicolas was the IEEE CVPR 2011 publication chair, served on the program committee at ACM InPar 2012, IEEE CIGPU 2012, NIPS ”Big Learning” 2011, GPGPU 2011-2012, IEEE ECCV/CVGPU 2010, PASCO 2010, and reviewed for IEEE PAMI, NIPS, IEEE Transactions on Image Processing, Elsevier Vision Research, IEEE IJCNN, IEEE ICDL, etc.