CRC 1233 “Robust Vision”

Illustration of a self-driving car

The collaborative research center “Robust Vision – Inference Principles and Neural Mechanisms” (CRC 1233) deals with basic principles of biological and machine vision, and is a close collaboration between scientists from the University and the Max Planck Institute for Intelligent Systems. Human visual perception is amazingly robust: Even in highly variable environments, we are able to make reliable inferences about the spatial arrangement of the world from limited visual information. To achieve this, our brain must perform complex computations. Artificial vision systems, in turn – as used, for example, in self-driving cars – are making steep progress in reproducing the visual skills of humans. The goal of this centre will be to better understand the principles and algorithms that enable robust visual inference both in humans and machines.

Topics, aims and projects

Early Career Research Group on Holistic Scene Modeling

Aim 1a: Generative and causal modeling

  1. Physics-based scene understanding (Gehler, Lensch)
  2. Robust material inference (Lensch, Schölkopf)
  3. Comparing humans and machines on robust visual inference (Wallis, Bethge)
  4. Causal inference strategies in human vision (Wichmann, Schölkopf)

Aim 1b: Feedback and neural representations

  1. Task–dependend top down modulation of visual processing (von Luxburg, Franz)
  2. Top-down control of visual inference in sensory representations in early visual cortex (Nienborg, Macke, Wichmann)
  3. Large-scale neuronal interactions during natural vision (Siegel)
  4. Integration of bottom-up and top-down processing in perceptual learning during sleep (Rauss, Nienborg)

Aim 2: Dynamic input

  1. Natural dynamic scene processing in the human brain (Bartels, Black)
  2. Natural stimuli for mice: environment statistics and neural representations in the early visual system (Busse, Schaeffel, Euler)
  3. Stable vision in the presence of fixational eye movements: where and how is the retinal image perceptually stabilized? (Schaeffel, Hafed)

Aim 3: Precortical image transformations

  1. Image processing within a locally complete retinal ganglion cell population (Euler, Bethge)
  2. Visual processing of feedforward and feedback signals in the dLGN (Busse, Berens)
  3. Image-processing computations in artificial vision (Stingl, Macke, Zeck, Zrenner)
  • Mercator project: Learning representations for efficient, visual inference in dynamic scenes (Bohg)

Speakers

Prof. Dr. Matthias Bethge
(speaker)

Werner Reichardt Centre for Integrative Neuroscience
Otfried-Müller-Str. 25
D-72076 Tübingen

Phone: +49 7071 29-89017
E-mail: matthias.bethge[at]uni-tuebingen.de

Michael Black, Ph.D., Director
(deputy speaker)

Max Planck Institute for Intelligent Systems
Spemannstrasse 41
D-72076 Tübingen

Phone: +49 7071 601-1801
E-mail: black[at]tue.mpg.de

Partners

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Contact

Speaker

Prof. Dr. Matthias Bethge

University of Tübingen

Deputy speaker

Michael Black, Ph.D., Director

Max Planck Institute for Intelligent Systems

Coordination

Dr. Tina Gauger

Phone: +49 7071 29 89019

tina.gauger[at]uni-tuebingen.de


Dr. Judith Lam 
(on parental leave)

Phone: +49 7071 29 89019

judith.lam[at]uni-tuebingen.de

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