Monthly Archives: October 2017

Tunç Aydın: Extracting transparent image layers for high-quality compositing

Tunç Aydın is a Research Scientist at Disney Research located at the Zürich Lab. My current research primarily focuses on image and video processing problems that address various movie production challenges, such as natural matting, green-screen keying, color grading, edge-aware filtering, and temporal coherence, among others. I have also been interested in analyzing visual content in terms of visual quality and aesthetic plausibility by utilizing knowledge of the human visual system. In my work I tend to utilize High Dynamic Range, Stereoscopic 3D, and High Frame-rate content, in addition to standard 8-bit images and videos.

Prior to joining Disney Research, I worked as a Research Associate at the Max-Planck-Institut für Informatik from 2006-2011, where I obtained my PhD degree under the supervision of Karol Myszkowski and Hans-Peter Seidel. I received the Eurographics PhD award in 2012 for my dissertation. I hold a Master’s degree in Computer Science from the College of Computing at Georgia Institute of Technology, and a Bachelor’s degree in Civil Engineering from Istanbul Teknik Universitesi. His talk takes place on Wednesday, November 1, 2017 at 13:00 in room A112.

Extracting transparent image layers for high-quality compositing

Compositing is an essential task in visual content production. For instance, a contemporary feature film production that doesn’t involve any compositing work is a rare occasion. However, achieving production-level quality often requires a significant amount of manual labor by digital compositing artists, mainly due to the limits of existing tools available for various compositing tasks. In this presentation I will talk about our recent work that aims on improving upon existing compositing technologies, where we focus on natural matting, green-screen keying, and color editing. We tackle natural matting using a novel affinity-based approach, whereas for green-screen keying and color editing we introduce a “color unmixing” framework, which we specialize individually for the two problem domains. Using these new techniques we achieve state-of-the-art results while also significantly reducing the manual interaction time.


Jakub Mareček: Urban Traffic Management – Traffic State Estimation, Signalling Games, and Traffic Control

Jakub Mareček is a research staff member at IBM Research. Together with some fabulous colleagues, Jakub develops solvers for optimisation and control problems at IBM’s Smarter Cities Technology Centre. Jakub joined IBM Research from the School of Mathematics at the University of Edinburgh in August 2012. Prior to his brief post-doc in Edinburgh, Jakub had presented an approach to general-purpose integer programming in his dissertation at the University of Nottingham and worked in two start-up companies in Brno, the Czech Republic. His talk takes place on Monday, October 16, 2017 at 13:30 in room D0207.

Urban Traffic Management: Traffic State Estimation, Signalling Games, and Traffic Control

In many engineering applications, one needs to identify a model of a non-linear system, increasingly using large volumes of heterogeneous, streamed data, and apply some form of (optimal) control. First, we illustrate why much of the classical identification and control is not applicable to problems involving time-varying populations of agents, such as in smart grids and intelligent transportations systems. Second, we use tools from robust statistics and convex optimisation to present alternative approaches to closed-loop system identification, and tools from iterated function systems to identify controllers for such systems with certain probabilistic guarantees on the performance for the individual interacting with the controller.