After just over ten years with Microsoft Research Cambridge I, Carsten Rother, will be moving to Dresden/Germany in October 2013 to start the new Computer Vision Lab Dresden (CVLD) as full (W3) Professor at TU Dresden Faculty of Computer Science. Below you will find the mission statement and future research areas. Further Information about teaching, lab members, and other activities in the lab will be announced at a later stage.

Looking for people! Join me in the exciting times of starting a new lab. I have several openings for Postdocs and PhD students. I am looking for motivated students who are excited to conduct high-quality research in one of the research areas listed below. You should have a strong mathematical background, ideally in optimization, machine learning, and probability theory. You should also have good programming skills and ideally have coded a small-scale vision application in the past. If you think that you are the right candidate please send me your CV and letter of motivation: Professor Carsten Rother (CVLDapplication@gmail.com).

Mission statement Computer Vision is a science that develops models and methods for understanding, analysing, acquiring and processing images, and more generally high-dimensional “visual” data. Computer Vision is part of many other fields such discrete optimization, machine learning, human computer interaction, computer graphics, and systems biology. The Computer Vision Lab Dresden works on a broad set of topics in computer vision and these related fields. The ultimate goal of the lab is to develop novel theoretical concepts which are practically relevant. The emphasis is on novelty and creativity, from both a theoretical and practical viewpoint, in order to influence the research field and potentially set new trends. While the set of applications is large and diverse, we often use the same modelling language of undirected graphical models. We are interested in finding the best trade-offs between model complexity, efficient and global optimization, and good learning. The labs’ activity can be grouped into the following six research areas. There are many potential synergy effects between these areas which we try to exploit. Note that most research activities have not yet started. For related work and previous research activities please see the Microsoft webpage.

Research Areas

Interactive Image and Data manipulation Many tasks in computer vision need the user in the loop, such interactive image segmentation, where the end result should be perceptually pleasing. We address various traditional interactive “low-level vision” topics. Additionally we plan to look at upcoming research topics such as interactive techniques for both Big Data analysis, which often occur in biological settings, as well as human-computer-interaction scenarios. Both application fields are in co-operation with partners from the respective research field. A shortlist of potential topics:

• Interactive Image and Video segmentation, matting, retargeting, and editing

• Unsupervised segmentation and co-segmentation from a single or multiple images

• Perceptual “loss” functions for low-level vision, such as perceptual loss for image de-noising

• Active and Interactive learning in Big Data Scenarios

• Learning to interact, such as learning a gestural interface and activity forecasting

People: Andrew Viergutz (PhD Candidate from Raimund Dachselt Lab at TU Dresden).
Project webpages: to come; please visit my MSR webpage

Applied Optimization, Models, and Learning The other research areas often require complex structured models, such as models with higher-order functions between many variables, which pose challenges on the optimization side. Furthermore, it is often hard to hand-code such functional relationship, and it derive them from ground truth measurements. The goal of this theme is to analyse the trade-offs between models, optimization and learning with the ultimate goal of achieving an algorithm which is as efficient and accurate as possible at test time. A shortlist of potential topics:

• Efficient inference for models with higher-order factors, such as object-connectivity

• Developing novel models such as the Decision Tree Field model, cascaded models, or Deep Belief Nets

• Learning models with complex loss functions, such as Interaction Loss or Perception Loss

• Inference in structured models with continuous or very large label spaces, such as PMBP

• Comparing generative and discriminative models in real-world settings

People: Dmitrij Schlesinger
Project webpages: to come; please visit my MSR webpage

Inverse rendering from moving images An ultimate goal in computer vision is to invert the image formation process, ideally coming up with a graphics script that models the geometry of the scene, camera, material and light such that the script renders the given set of images. While this has been studied intensively in isolation such as optical flow estimation, shape-from-shading and intrinsic image extraction, we plan to study the problem from a holistic viewpoint in order to optimally exploit synergy effects in a single, statically sound, framework. A potential shortlist of topics is:

· Dense motion from moving cameras, such as stereo and optical flow matching

· Image de-convolution and de-noising.

· Recovering light, shading and material (including simple BRDFs)

· Combining statistical priors with physically motivated priors

People: Michael Hornacek (PhD candidate, jointly supervised with Margrit Gelautz from TU Vienna),
Project webpages: to come; please visit my MSR webpage

3D Scene Understanding The ultimate goal of 3D Scene Understanding is to take a single RGB image and extract a so-called “scene-graph”, where each object is defined by a name, associated attributes, and its physical 3D properties (e.g. 3D bounding box). Additionally relationships between objects are given, such as “object A is supported by object B”. While this is hot research area with many competing efforts, we plan to tackle the problem from a slightly different angle. Firstly, we try to show that training models with large amount of synthetic data is a valuable option. Secondly, we plan to combine 3D Scene understanding with ”inverse rendering from moving images”, in order to exploit synergy effects.

· Pose estimation and segmentation of known objects and objects classes

· unsupervised 3D object candidate extraction from stereo (or moving) images

· Inference with global constraints, such as object stability

· 3D scene completion

People: Eric Brachman, Alexander Krull and Frank Michel (PhD Candidates from Stefan Gumhold’s group at TU Dresden)
Project webpages: to come; please visit my MSR webpage

Benchmarking and Label collection For learning models as well as comparing methods it is essential to gather high quality data and associated labels. We plan to gather new datasets and extend existing ones. Recently, the trend of gathering synthetic data as an alternative to real data has become popular. We are interesting in analysing this option. Most topics in this theme are in close collaboration with other partners. A shortlist of potential topics:

· Synthetic and real data for low-level vision, such as matting, material estimation, etc.

· Synesthetic and real data for 3D scene understanding

· Benchmarking Optimization techniques for discrete undirected graphical models

Students: to come.
Project webpages: to come; please visit webpages of past projects, where I have been involved: alpha matting , MRF minimization, OpenGM2

Image Analysis for System Biology Dresden has a very strong research environment in the area of Biology. In particular topics on Bio-Imaging grow in popularity. This is an exciting research field for latest computer vision, optimization and machine learning ideas. The concrete goals in this field are going to be defined soon.