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Project Leader
Dr.-Ing. Cristobal CurioPhone: +49 7071 601-605
Fax: +49 7071 601-616
cristobal.curio[at]tuebingen.mpg.de
News
Intelligent Vehicles conference.2/03/2012
Martin Breidt successfully defended his PhD.
5/10/2011 Public release of medial feature grouping and superpixel segmentation
code.
09/ 2011 HMI system for image retrieval at
INTERACT 2011.
06/ 2011 Automotive HMI approach at
Intelligent Vehicles 2011.
03/2011 Paper presentation at
IEEE Automatic Face & Gesture Conference, Santa Barbara. Watch our latest facial analysis video with the Microsoft Kinect sensor on youtube.
02/2011 Congratulations to David Engel for his successful PhD defense.
10/2010 Book on
Dynamic Faces, MIT Press has appeared.
10/2010 Two
SIGGRAPH Asia 2010 Technical Sketches.
05/2010 Paper at CVPR Conference
Workshop on Feature Grouping.
03/2010 New
EU-FET-Open (Future and Emerging Technologies) project TANGO 'Emotional interaction grounded in realistic context'.
09/2009 Runner up prize for work on automatic 3D surface tracking for the generation of a 4D morphable face model.
Conference of the German Association for Pattern Recognition (DAGM).Teaching
Machine Learning II at the Graduate School of Neural Information Processing, Tübingen.
WS 2010/11
Statistical Methods in Artificial Intelligence at the Computer Science Department, Tübingen.
WS 2009/10
Advanced Topics in Machine Learning at the Computer Science Department, Tübingen.External activities
What's in a face? at ECCV, PC
KI-2012 PC
06/2011 Organization of workshop on interactive pedestrian behavior analysis and synthesis at IEEE sponsored
Intelligent Vehicles, Baden-Baden, June 5, Final program.11/2011 PC at 1st IEEE workshop on
Information Theory in Computer Vision and Pattern Recognition at ICCV 2011KI-2011 Program Committee
KI-2010 Area Chair
03/2008
COSYNE conference workshopFive most recent Publications
, Breidt M  and Bülthoff HH (July-2012) Render me Real? Investigating the Effect of Render Style on the Perception of Animated Virtual Humans
ACM Transactions on Graphics 31(4: Proceedings of ACM SIGGRAPH 2012).
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Engel D and Curio C (June-2012) Detectability Prediction in Dynamic Scenes for Enhanced Environment Perception
IEEE Intelligent Vehicles Symposium (IV 2012), 1-6.
accepted
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Herdtweck C and Curio C (June-2012) Experts of Probabilistic Flow Subspaces for Robust Monocular Odometry in Urban Areas
IEEE Intelligent Vehicles Symposium (IV 2012), 1-7.
accepted
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Chuang LL , Vuong QC and Bülthoff HH (May-2012) Learned non-rigid object motion is a view-invariant cue to recognizing novel objects
Frontiers in Computational Neuroscience 6(26) 1-8.
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, Chuang LL and (May-2012) How do image complexity, task demands and looking biases influence human gaze behavior?
Pattern Recognition Letters . accepted |
Cognitive Engineering Group
- Interfacing different disciplines for the development and optimization of human-centered interactive applications.
The Cognitive Engineering group develops applications based on Computer-Vision, Machine-Learning and Computer-Graphics in combination with methods that model Human cognitive processes. Highly controllable and realistic settings, real-world sensor data and simulations, offer the opportunity for advanced experiments and at the same time a framework to design and optimize processes in Human-Machine-Interfaces. Our approach ultimately opens a new window to modern industries such as entertainment computing (games), communication research (information transfer, multimedia), medical systems, and personal assistance systems (automotive safety).
For example, we develop realistic computer-generated models that offer as research tool new opportunities to study human cognitive processes in interactive situations. Results from such experiments are in turn used to build effective Human-Machine-Interfaces. We mainly develop and employ Computer-Vision and Machine-Learning algorithms to quantify human performance in natural interactive situations. The results from perception experiments provide constraints for designing artificial vision algorithms and thus lead to more efficient approaches in, for example, artificial environment understanding. Realistic virtual worlds and animations, generated with Computer-Graphics, support us with "ground truth" to prototype artificial recognition algorithms and to determine the critical factors in human decision making. The semantic modeling of high-dimensional data spaces is another important goal of our work. It provides the basis to effectively study and optimize Human-Computer interaction.
For example, we develop realistic computer-generated models that offer as research tool new opportunities to study human cognitive processes in interactive situations. Results from such experiments are in turn used to build effective Human-Machine-Interfaces. We mainly develop and employ Computer-Vision and Machine-Learning algorithms to quantify human performance in natural interactive situations. The results from perception experiments provide constraints for designing artificial vision algorithms and thus lead to more efficient approaches in, for example, artificial environment understanding. Realistic virtual worlds and animations, generated with Computer-Graphics, support us with "ground truth" to prototype artificial recognition algorithms and to determine the critical factors in human decision making. The semantic modeling of high-dimensional data spaces is another important goal of our work. It provides the basis to effectively study and optimize Human-Computer interaction.
Main research areas
We develop Computer Vision systems that can enhance and support the visual perception and decision making processes of humans. The components we investigate range from low-level image features to high level system components for, e.g., autonomous navigation or artificial scene interpretation.
[more]
Adaptive Human-Machine-Interfaces
We combine Computer Vision, Machine Learning and Computer Graphics with experimental methods of Cognitive Sciences in order to optimize technical interfaces between human and machine perception.
[more]
Automatic Movement Reconstruction Technologies
Rich movement models, for example dynamic morphable face models, have a large impact on the fields of computer vision, behavior monitoring, as well as computer graphics and animation. Attributes such as descriptiveness, semantics, and intuitive control are desirable properties. For robust applications we build models from high-quality and noisy depth data such as that produced by Time-of-Flight (ToF) cameras or devices such as Microsoft Kinect.
[more]
Signal Processing of Social Interactions
We can best understand human behavior in natural interactions. For example, we have developed a real-time facial expression control system. Major technological challenges are in general the reduction of noticeable feedback latencies without loosing accuracy. The inclusion of body motion capture allows to create animatable humans. Learning generative models of interactions is another important goal in order to understand complex behavior. Moreover, we want to understand the visual perception of social crowds, which in return will benefit the development of artificial vision systems in order to make complex predictions.
[more]
Selected publications
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Engel D , Herdtweck C , Browatzki B and Curio C (September-2011) Image Retrieval with Semantic Sketches
In: Human-Computer Interaction: INTERACT 2011, 13th IFIP TC13 Conference on Human-Computer Interaction, Springer, Berlin, Germany, 412-425.
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Engel D and Curio C (June-2011) Pedestrian Detectability: Predicting Human Perception Performance with Machine Vision
IEEE Intelligent Vehicles Symposium (IV 2011), IEEE, Piscataway, NJ, USA, 429-435.
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Breidt M , Bülthoff HH and Curio C (March-2011) Robust Semantic Analysis by Synthesis of 3D Facial Motion
Ninth IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011), IEEE, Piscataway, NJ, USA, 713-719.
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Breidt M , Bülthoff HH and Curio C (December-2010) Face Models from Noisy 3D Cameras
3rd ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia 2010), ACM Press, New York, NY, USA, 1-2.
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Engel D and Curio C (June-2010) Shape Centered Interest Points for Feature Grouping
CVPR 2010 Workshop on Perceptual Organization in Computer Vision (POCV 2010), IEEE, Piscataway, NJ, USA, 9-16.
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Walder C , Breidt M , Bülthoff HH , Schölkopf B and Curio C (September-2009) Markerless 3D Face Tracking (DAGM 2009)
In: Pattern Recognition, 31. Symposium of the German Association for Pattern Recognition (DAGM 2009), Springer, Berlin, Germany, 41-50.
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Curio C and Giese M (January-2005) Combining View-based and Model-based Tracking of Articulated Human Movements
IEEE Computer Society Workshop on Motion and Vision Computing (MOTION 2005), IEEE Computer Society, Los Alamitos, CA, USA, 261-268.
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, Curio C , , , , , , and (February-2003) Image Processing and Behaviour Planning for
Intelligent Vehicles
IEEE Transactions on Industrial Electronics 90(1) 62-75.
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Last updated: Tuesday, 22.05.2012