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% Publications of Radig % Encoding: utf-8 @inproceedings{BertelsmeierR77, author = {R. Bertelsmeier and Bernd Radig}, editor = {Hans-Hellmut Nagel}, title = {{Kontextunterst{\"u}tzte} {Analyse} von {Szenen} mit bewegten {Objekten}.}, booktitle = {Digital Bildverarbeitung - Digital Image Processing, GI/NTG Fachtagung, M{\"u}nchen, 28.-30. M{\"a}rz 1977}, year = {1977}, pages = {101-128}, publisher = {Springer}, isbn = {3-540-08169-0}, bibsource = {DBLP, http://dblp.uni-trier.de}, bib2html_pubtype={Refereed Conference Paper}, bib2html_rescat={}, bib2html_groups={IU}, abstract = {} } @inproceedings{Bee02Agi1, author = "Michael Beetz and Sebastian Buck and Robert Hanek and Thorsten Schmitt and Bernd Radig", title = "The {AGILO} Autonomous Robot Soccer Team: Computational Principles, Experiences, and Perspectives", booktitle = "International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS) 2002", year = "2002", pages = "805--812", address = "Bologna, Italy", bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Robot Learning, State Estimation, RoboCup}, bib2html_groups = {AGILO}, bib2html_funding = {AGILO}, bib2html_keywords = {Learning, Robot, State Estimation, Vision, Reasoning}, abstract = {This paper describes the computational model underlying the AGILO autonomous robot soccer team, its implementation, and our experiences with it. The most salient aspects of the AGILO control software are that it includes (1) a cooperative probabilistic game state estimator working with a simple off-the-shelf camera system; (2) a situated action selection module that makes amble use of experience-based learning and produces coherent team behavior even if inter-robot communication is perturbed; and (3) a playbook executor that can perform preprogrammed complex soccer plays in appropriate situations by employing plan-based control techniques. The use of such sophisticated state estimation and control techniques distinguishes the AGILO software from many others applied to mid-size autonomous robot soccer. The paper discusses the computational techniques and necessary extensions based on experimental data from the 2001 robot soccer world championship.} } @Article{Sch02Coo, author = "Thorsten Schmitt and Robert Hanek and Michael Beetz and Sebastian Buck and Bernd Radig", title = "Cooperative Probabilistic State Estimation for Vision-based Autonomous Mobile Robots", journal = "IEEE Transactions on Robotics and Automation", year = "2002", month = "October", volume = "18", number = "5", bib2html_pubtype = {Journal}, bib2html_rescat = {State Estimation, Robot Vision, RoboCup}, bib2html_groups = {AGILO}, bib2html_funding = {AGILO}, bib2html_keywords = {Robot, Vision, State Estimation}, abstract = {With the services that autonomous robots are to provide becoming more demanding, the states that the robots have to estimate become more complex. In this article, we develop and analyze a probabilistic, vision-based state estimation method for individual, autonomous robots. This method enables a team of mobile robots to estimate their joint positions in a known environment and track the positions of autonomously moving objects. The tate estimators of different robots cooperate to increase the accuracy and reliability of the estimation process. This cooperation between the robots enables them to track temporarily occluded objects and to faster recover their position after they have lost track of it. The method is empirically validated based on experiments with a team of physical robots.} } @Article{Bee04AGILO, author = {Michael Beetz and Thorsten Schmitt and Robert Hanek and Sebastian Buck and Freek Stulp and Derik Schr{\"o}ter and Bernd Radig}, title = {The {AGILO} Robot Soccer Team -- Experience-based Learning and Probabilistic Reasoning in Autonomous Robot Control}, journal = {Autonomous Robots}, year = 2004, volume = {17}, number = {1}, pages = {55--77}, bib2html_pubtype = {Journal}, bib2html_rescat = {Perception, Models, Learning, Planning, Action}, bib2html_groups = {AGILO}, bib2html_funding = {AGILO}, bib2html_keywords = {Learning, Robot, Reasoning}, abstract = {This article describes the computational model underlying the AGILO autonomous robot soccer team, its implementation, and our experiences with it. According to our model the control system of an autonomous soccer robot consists of a probabilistic game state estimator and a situated action selection module. The game state estimator computes the robot's belief state with respect to the current game situation using a simple off-theshelf camera system. The estimated game state comprises the positions and dynamic states of the robot itself and its teammates as well as the positions of the ball and the opponent players. Employing sophisticated probabilistic reasoning techniques and exploiting the cooperation between team mates, the robot can estimate complex game states reliably and accurately despite incomplete and inaccurate state information. The action selection module selects actions according to specified selection criteria as well as learned experiences. Automatic learning techniques made it possible to develop fast and skillful routines for approaching the ball, assigning roles, and performing coordinated plays. The paper discusses the computational techniques based on experimental data from the 2001 robot soccer world championship.} } @InProceedings{ SchroeterDagm04DetectClassGW, author = {D. Schr{\"o}ter and T. Weber and M. Beetz and B. Radig}, title = {{Detection and Classification of Gateways for the Acquisition of Structured Robot Maps}}, booktitle = {Proc. of 26th Pattern Recognition Symposium (DAGM), T\"ubingen/Germany}, year = {2004}, abstract = {{The automatic acquisition of structured object maps requires sophisticated perceptual mechanisms that enable the robot to recognize the objects that are to be stored in the robot map. This paper investigates a particular object recognition problem: the automatic detection and classification of gateways in office environments based on laser range data. We will propose, discuss, and empirically evaluate a sensor model for crossing gateways and different approaches to gateway classification including simple maximum classifiers and HMM-based classification of observation sequences.}}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Robot Mapping}, bib2html_groups = {IAS,EvI}, bib2html_funding = {EvI}, bib2html_keywords = {Environment Mapping}, } @InProceedings{beetz08assistive, author = {Michael Beetz and Freek Stulp and Bernd Radig and Jan Bandouch and Nico Blodow and Mihai Dolha and Andreas Fedrizzi and Dominik Jain and Uli Klank and Ingo Kresse and Alexis Maldonado and Zoltan Marton and Lorenz M{\"o}senlechner and Federico Ruiz and Radu Bogdan Rusu and Moritz Tenorth}, title = {{The Assistive Kitchen -- A Demonstration Scenario for Cognitive Technical Systems}}, booktitle = {IEEE 17th International Symposium on Robot and Human Interactive Communication (RO-MAN), Muenchen, Germany}, pages = {1-8}, year = {2008}, bib2html_pubtype = {Conference Paper}, bib2html_rescat = {Perception, Planning, Learning}, bib2html_groups = {Memoman, Cogito, EnvMod, Cogman, K4C, ProbCog}, bib2html_funding = {CoTeSys}, bib2html_domain = {Assistive Household}, note = {Invited paper.}, } @article{beetz10cpa, title={{Learning from Humans -- Cognition-enabled Computational Models of Everyday Activity}}, author={Michael Beetz and Martin Buss and Bernd Radig}, journal={K{\"u}nstliche Intelligenz}, year={2010}, publisher={Springer}, bib2html_pubtype = {Journal}, bib2html_funding = {CoTeSys}, bib2html_groups = {Cogito}, bib2html_domain = {Assistive Household} } @InProceedings{sosnowskiAISB2010, author={S.~Sosnowski and C.~Mayer and K.~K\"uhnlenz and B.~Radig}, title={Mirror my emotions! Combining facial expression analysis and synthesis on a robot}, booktitle={The Thirty Sixth Annual Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB2010)}, year={2010}, publisher = {}, series = {}, volume = {}, note={}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {}, bib2html_rescat = {}, bib2html_domain = {} } @InProceedings{wallhoff10realtime, author={Frank Wallhoff and Tobias Rehrl and Christoph Mayer and Bernd Radig}, title={Real-Time Face and Gesture Analysis for Human-Robot Interaction}, booktitle={Real-Time Image and Video Processing 2010}, year={2010}, publisher = {}, series = {Proceedings of SPIE}, volume = {}, note={invited paper}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {}, bib2html_rescat = {}, bib2html_domain = {} } @techreport{brscic10mujoa, author = {D. Br\v{s}\v{c}i\'{c} and M. Eggers and F. Rohrm\"uller and O. Kourakos and S. Sosnowski and D. Althoff and M. Lawitzky and A. M\"ortl and M. Rambow and V. Koropouli and J.R. Medina Hern\'{a}ndez and X. Zang and W. Wang and D. Wollherr and K. K\"uhnlenz and C. Mayer and T. Kruse and A. Kirsch and J. Blume and A. Bannat and T. Rehrl and F. Wallhoff and T. Lorenz and P. Basili and C. Lenz and T. R\"oder and G. Panin and W. Maier and S. Hirche and M. Buss and M. Beetz and B. Radig and A. Schub\"o and S. Glasauer and A. Knoll and E. Steinbach}, title = {{Multi Joint Action} in {CoTeSys} --- Setup and Challenges}, institution = {CoTeSys Cluster of Excelence: Technische Universit\"at M\"unchen \& Ludwig-Maximilians-Universit\"at M\"unchen}, year = {2010}, number = {CoTeSys-TR-10-01}, address = {Munich, Germany}, month = {June}, bib2html_pubtype = {Other}, bib2html_groups = {PARA, Mudis, Other}, bib2html_funding = {CoTeSys}, bib2html_rescat = {Perception, Human-Robot Interaction, Planning}, bib2html_domain = {Multi-Joint Action}} @InProceedings{mayer10towards, author={C.~Mayer and S.~Sosnowski and K.~K\"uhnlenz and B.~Radig}, title={Towards robotic facial mimicry: system development and evaluation}, booktitle={Proceedings of the 19th IEEE International Symposium on Robot and Human Interactive Communication}, year={2010}, publisher = {}, series = {}, volume = {}, note={}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {}, bib2html_rescat = {}, bib2html_domain = {} } @inproceedings{mayer11learning, author = {Christoph Mayer and Bernd Radig}, title = {Learning Displacement Experts from Multi-band Images for Face Model Fitting}, booktitle = {International Conference on Advances in Computer-Human Interaction}, month = {February 11}, year = {2011}, bib2html_groups = {Image Understanding} } @inproceedings{gonsior11improving, author = {Barbara Gonsior and Stefan Sosnowski and Christoph Mayer and J\"urgen Blume and Bernd Radig and Dirk Wollherr, and Kolja K\"uhnlenz}, title = {Improving Aspects of Empathy and Subjective Performance for HRI through Mirroring Facial Expressions}, booktitle={Proceedings of the 19th IEEE International Symposium on Robot and Human Interactive Communication}, year = {2011}, bib2html_groups = {Image Understanding} } @inproceedings{wimmer05adaptive, AUTHOR = {Matthias Wimmer and Bernd Radig}, TITLE = {Adaptive Skin Color Classificator}, BOOKTITLE = {Proceedings of the first International Conference on Graphics, Vision and Image Processing}, PUBLISHER = {ICGST}, PAGES = {324-327}, ADDRESS = {Cairo, Egypt}, VOLUME = {I}, ISBN = {21970/2005}, YEAR = {2005}, MONTH = {December}, EDITOR = {Ashraf Aboshosha et al.}, abstract = {A lot of computer vision applications benefit from robust skin color classification. But this is a hard challenge due to the various image conditions like camera settings, illumination, light source, shadows and many more. Furthermore people?s tans and ethnic groups also extend those conditions. In this work we present a parametric skin color classifier that can be adapted to the conditions of each image or image sequence. This is done by evaluating some previously know skin color pixels which are acquired by applying a face detector. This approach can distinguish skin color from very similar color like lip color or eye brow color. Its high speed and high accuracy makes it appropriate for real time applications such as face tracking and mimic recognition.}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, } @inproceedings{haemmerle05sensorbased, author = {Simone H{\"a}mmerle and Matthias Wimmer and Bernd Radig and Michael Beetz}, title = {Sensor-based Situated, Individualized, and Personalized Interaction in Smart Environments}, year = {2005}, month = {September}, pages = {261-265}, editor = {Armin B. Cremers and Rainer Manthey and Peter Martini and Volker Steinhage}, booktitle = {INFORMATIK 2005 - Informatik LIVE! Band 1, Beitr{\"a}ge der 35. Jahrestagung der Gesellschaft f{\"u}r Informatik~(GI)}, address = {Bonn, Germany}, otherbooktitle = {GI~Jahrestagung~(1)}, publisher = {GI}, series = {LNI}, volume = {67}, abstract = {Smart environments are sensor equipped areas that know about their environment thus being able to adapt to the user. We present sHOME, a multiagent based platform for integrating situated, individualized, and personalized information. sHOME acquires sensor data to determine the user's identity, his location, his gesture, and natural language commands and stores it in a central knowledge base.}, isbn = {3-88579-396-2}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, } @InProceedings{wimmer06learning, author = {Matthias Wimmer and Freek Stulp and Stephan Tschechne and Bernd Radig}, title = {Learning Robust Objective Functions for Model Fitting in Image Understanding Applications}, booktitle = {Proceedings of the 17th~British Machine Vision Conference~(BMVC)}, editor = {Michael J. Chantler and Emanuel Trucco and Robert B. Fisher}, year = {2006}, month = {September}, publisher = {BMVA}, address = {Edinburgh, UK}, volume = {3}, pages = {1159-1168}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, abstract = {Model-based methods in computer vision have proven to be a good approach for compressing the large amount of information in images. Fitting algorithms search for those parameters of the model that optimise the objective function given a certain image. Although fitting algorithms have been the subject of intensive research and evaluation, the objective function is usually designed ad hoc and heuristically with much implicit domain-dependent knowledge. This paper formulates a set of requirements that robust objective functions should satisfy. Furthermore, we propose a novel approach that learns the objective function from training images that have been annotated with the preferred model parameters. The requirements are automatically enforced during the learning phase, which yields generally applicable objective functions. We compare the performance of our approach to other approaches. For this purpose, we propose a set of indicators that evaluate how well an objective function meets the stated requirements.} } @inproceedings{wimmer06aperson, author = {Matthias Wimmer and Bernd Radig and Michael Beetz}, title = {A Person and Context Specific Approach for Skin Color Classification}, booktitle = {Procedings of the 18th~International Conference of Pattern Recognition~(ICPR 2006)}, volume = {2}, pages = {39-42}, year = {2006}, month = {August}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, abstract = {Skin color is an important feature of faces. Various applications benefit from robust skin color detection. Depending on camera settings, illumination, shadows, people?s tans, and ethnic groups skin color looks differently, which is a challenging aspect for detecting it automatically. In this paper, we present an approach that uses a high level vision module to detect an image specific skin color model. This model is then used to adapt parametric skin color classifiers to the processed image. This approach is capable to distinguish skin color from extremely similar colors, such as lip color or eyebrow color. Its high speed and high accuracy make it appropriate for real time applications such as face tracking and recognition of facial expressions.}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU} } @article{wimmer06adaptive, AUTHOR = {Matthias Wimmer and Bernd Radig}, TITLE = {Adaptive Skin Color Classificator}, JOURNAL = {ICGST International Journal on Graphics, Vision and Image Processing}, YEAR = {2006}, VOLUME = {Special Issue on Biometrics}, abstract = {Skin color is an important feature of faces. Various applications benefit from robust skin color detection. Skin color may look quite different, depending on camera settings, illumination, shadows, people's tans, ethnic groups. That variation is a challenging aspect of skin color classification. In this paper, we present an approach that uses a high level vision module to detect an image specific skin color model. This model is representative for the context conditions within the image and is used to adapt dynamic skin color classifiers to it. This approach distinguishes skin color from very similar color like lip color or eyebrow color. Its high speed and accuracy makes it appropriate for real time applications such as face model fitting, gaze estimation, and recognition of facial expressions.}, bib2html_pubtype = {Journal}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, } @inproceedings{ wimmer07sipbild, author = {Matthias Wimmer and Bernd Radig and Christoph Mayer}, title = {{SIPBILD -- Mimik- und Gestikerkennung in der Mensch-Maschine-Schnittstelle}}, booktitle = {Beitr{\"a}ge der 37.~Jahrestagung der Gesellschaft f{\"u}r Informatik~(GI)}, year = {2007}, month = {September}, address = {Bremen, Germany}, volume = {1}, pages = {271-274}, xxxisbn = {978-3-88579-203-1}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, abstract = {F{\"u}r eine nat\"urliche Mensch-Maschine Interaktion spielt die Interpretation visueller Informationen eine zentrale Rolle. Fehlende Kontrolle der Umgebungsbedingungen wie Helligkeit und Hintergrundfarbe stellt hohe Anforderungen an die Bilderkennungssoftware. SIPBILD schafft es, mit modellbasierter Bildinterpretation die menschliche Mimik und Gestik zu erkennen. Um diese Technik in nat\"urlichen Umgebungen einzusetzen, ist es allerdings notwendig, die bisherigen Techniken entscheidend zu verbessern. Insbesondere stellen wir eine Vorgehensweise vor, die robustes Model-Fitting ohne spezielles Fachwissen in der Bildverarbeitung erreicht und der Einsatz dieser Technik somit keinen Experten mehr verlangt.}, } @inproceedings{ schuller07audiovisual, author = {Bj\"orn Schuller and Matthias Wimmer and Dejan Arsic and Gerhard Rigoll and Bernd Radig}, title = {Audiovisual Behavior Modeling by Combined Feature Spaces}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing~(ICASSP)}, year = {2007}, month = {April}, address = {Honolulu, Hawaii, USA}, volume = {2}, pages = {733-736}, isbn = {1-4244-0728-1}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, } @inproceedings{ wimmer07learningDAGM, author = {Matthias Wimmer and Sylvia Pietzsch and Freek Stulp and Bernd Radig}, title = {Learning Robust Objective Functions with Application to Face Model Fitting}, booktitle = {Proceedings of the 29th~DAGM Symposium}, year = {2007}, month = {September}, address = {Heidelberg, Germany}, volume = {1}, pages = {486-496}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, abstract = {Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge is to determine the model parameters that best match a given image by searching for the global optimum of the involved objective function. Unfortunately, this function is usually designed manually, based on implicit and domain-dependent knowledge, which prevents the fitting task from yielding accurate results. In this paper, we demonstrate how to improve model fitting by learning objective functions from annotated training images. Our approach automates many critical decisions and the remaining manual steps hardly require domain-dependent knowledge. This yields more robust objective functions that are able to achieve the accurate model fit. Our evaluation uses a publicly available image database and compares the obtained results to a recent state-of-the-art approach.}, } @inproceedings{ wimmer07automatically, author = {Matthias Wimmer and Bernd Radig}, title = {Automatically Learning the Objective Function for Model Fitting}, booktitle = {Proceedings of the Meeting in Image Recognition and Understanding~(MIRU)}, year = {2007}, month = {July}, address = {Hiroshima, Japan}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, abstract = {Model-based image interpretation has proven to appropriately extract high-level information from images. A priori knowledge about the object of interest represents the basis of this task. Model fitting determines the model that best matches a given image by searching for the global optimum of an objective function. Unfortunately, the objective function is usually designed manually, based on implicit and domain-dependent knowledge. In contrast, this paper describes how to obtain highly accurate objective functions by learning them from annotated training images. It automates many critical decisions and the remaining manual steps hardly require domain-dependent knowledge at all. This approach yields highly accurate objective functions. Our evaluation fits a face model to a publicly available image database and compares the obtained results to a recent state-of-the-art approach.}, } @inproceedings{ wimmer07human, author = {Matthias Wimmer and Ursula Zucker and Bernd Radig}, title = {Human Capabilities on Video-based Facial Expression Recognition}, booktitle = {Proceedings of the 2nd Workshop on Emotion and Computing -- Current Research and Future Impact}, editor = {Dirk Reichardt and Paul Levi}, year = {2007}, month = {September}, address = {Osnabr\"uck, Germany}, pages = {7-10}, bib2html_pubtype = {Refereed Workshop Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, abstract = {A lot of promising computer vision research has been conducted in order to automatically recognize facial expressions during the last decade. Some of them achieve high accuracy, however, it has not yet been investigated how accurately humans accomplish this task, which will introduce a comparable measure. Therefore, we conducted a survey on this issue and this paper evaluates the gathered information regarding the recognition rate and the confusion of facial expressions.}, } @inproceedings{ wimmer07initial, author = {Matthias Wimmer and Bernd Radig}, title = {Initial Pose Estimation for {3D}~Models Using Learned Objective Functions}, booktitle = {Proceedings of the 8th~Asian Conference on Computer Vision~(ACCV07)}, origbooktitle = {Computer Vision -- ACCV 2007}, pages = {332-341}, editor = {Yasushi Yagi and Sing Bing Kang and In So Kweon and Hongbin Zha}, year = {2007}, month = {November}, address = {Tokyo, Japan}, publisher = {Springer}, location = {Heidelberg}, series = {LNCS}, volume = {4844}, isbn = {978-3-540-76389-5}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, abstract = {Tracking 3D models in image sequences essentially requires determining their initial position and orientation. Our previous work identifies the objective function as a crucial component for fitting 2D models to images. We state preferable properties of these functions and we propose to learn such a function from annotated example images. This paper extends this approach by making it appropriate to also fit 3D models to images. The correctly fitted model represents the initial pose for model tracking. However, this extension induces nontrivial challenges such as out-of-plane rotations and self occlusion, which cause large variation to the models surface visible in the image. We solve this issue by connecting the input features of the objective function directly to the model. Furthermore, sequentially executing objective functions specifically learned for different displacements from the correct positions yields highly accurate objective values.}, } @inproceedings{ wimmer07estimating, author = {Matthias Wimmer and Christoph Mayer and Freek Stulp and Bernd Radig}, title = {Estimating Natural Activity by Fitting {3D}~Models via Learned Objective Functions}, booktitle = {Workshop on Vision, Modeling, and Visualization~(VMV)}, year = {2007}, month = {November}, address = {Saarbr\"ucken, Germany}, volume = {1}, pages = {233-241}, bib2html_pubtype = {Refereed Workshop Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, abstract = {Model-based image interpretation has proven to robustly extract high-level scene descriptors from raw image data. Furthermore, geometric texture models represent a fundamental component for visualizing real-world scenarios. However, the motion of the model and the real-world object must be similar in order to portray natural activity. Again, this information can be determined by inspecting images via model-based image interpretation. This paper sketches the challenge of fitting models to images, describes the shortcomings of current approaches and proposes a technique based on machine learning techniques. We identify the objective function as a crucial component for fitting models to images. Furthermore, we state preferable properties of these functions and we propose to learn such a function from manually annotated example images.}, } @article{wimmer07learning, author = {Matthias Wimmer and Freek Stulp and Sylvia Pietzsch and Bernd Radig}, title = {Learning Local Objective Functions for Robust Face Model Fitting}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence~(PAMI)}, year = {2008}, volume = {30}, number = {8}, issn = {0162-8828}, pages = {1357-1370}, bib2html_pubtype = {Journal}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70793}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } @inproceedings{ wimmer07enabling, author = {Matthias Wimmer and Freek Stulp and Bernd Radig}, title = {Enabling Users to Guide the Design of Robust Model Fitting Algorithms}, booktitle = {Workshop on Interactive Computer Vision, held in conjunction with ICCV~2007}, year = {2007}, month = {October}, address = {Rio de Janeiro, Brazil}, pages = {28}, bib2html_pubtype = {Refereed Workshop Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, ISBN = {978-1-4244-1631-8}, ISSN = {1550-5499}, publisher = {Omnipress}, abstract = {Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge in model fitting is to determine the model parameters that best match a given image, which corresponds to finding the global optimum of the objective function. When it comes to the robustness and accuracy of fitting models to specific images, humans still outperform state-of-the-art model fitting systems. Therefore, we propose a method in which non-experts can guide the process of designing model fitting algorithms. In particular, this paper demonstrates how to obtain robust objective functions for face model fitting applications, by learning their calculation rules from example images annotated by humans. We evaluate the obtained function using a publicly available image database and compare it to a recent state-of-the-art approach in terms of accuracy.}, } @inproceedings{wimmer08face, author = {Matthias Wimmer and Christoph Mayer and Freek Stulp and Bernd Radig}, title = {Face Model Fitting based on Machine Learning from Multi-band Images of Facial Components}, booktitle = {Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, held in conjunction with CVPR}, year = {2008}, month = {June}, address = {Anchorage, AK, USA}, } @inproceedings{ wimmer08tailoring, author = {Matthias Wimmer and Christoph Mayer and Sylvia Pietzsch and Bernd Radig}, title = {Tailoring Model-based Techniques for Facial Expression Interpretation}, booktitle = {The First International Conference on Advances in Computer-Human Interaction~(ACHI08)}, year = {2008}, month = {February}, address = {Sainte Luce, Martinique}, } @inproceedings{ wimmer08lowlevel, author = {Matthias Wimmer and Bj\"orn Schuller and Dejan Arsic and Bernd Radig and Gerhard Rigoll}, title = {Low-level Fusion of Audio and Video Feature for Multi-modal Emotion Recognition}, booktitle = {3rd International Conference on Computer Vision Theory and Applications~(VISAPP)}, year = {2008}, month = {January}, address = {Madeira, Portugal}, volume = {2}, pages = {145-151}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, abstract = {Bimodal emotion recognition through audiovisual feature fusion has been shown superior over each individual modality in the past. Still, synchronization of the two streams is a challenge, as many vision approaches work on a frame basis opposing audio turn- or chunk-basis. Therefore, late fusion schemes such as simple logic or voting strategies are commonly used for the overall estimation of underlying affect. However, early fusion is known to be more effective in many other multimodal recognition tasks. We therefore suggest a combined analysis by descriptive statistics of audio and video Low-Level-Descriptors for subsequent static SVM Classification. This strategy also allows for a combined feature-space optimization which will be discussed herein. The high effectiveness of this approach is shown on a database of 11.5h containing six emotional situations in an airplane scenario.}, } @inproceedings{ pietzsch08face, author = {Sylvia Pietzsch and Matthias Wimmer and Freek Stulp and Bernd Radig}, title = {Face Model Fitting with Generic, Group-specific, and Person-specific Objective Functions}, booktitle = {3rd International Conference on Computer Vision Theory and Applications~(VISAPP)}, year = {2008}, month = {January}, address = {Madeira, Portugal}, volume = {2}, pages = {5-12}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, abstract = {In model-based fitting, the model parameters that best fit the image are determined by searching for the optimum of an objective function. Often, this function is designed manually, based on implicit and domain-dependent knowledge. We acquire more robust objective function by learning them from annotated images, in which many critical decisions are automated, and the remaining manual steps do not require domain knowledge. Still, the trade-off between generality and accuracy remains. General functions can be applied to a large range of objects, whereas specific functions describe a subset of objects more accurately. Gross et al. have demonstrated this principle by comparing generic to person-specific Active Appearance Models. As it is impossible to learn a person-specific objective function for the entire human population, we automatically partition the training images and then learn partition-specific functions. The number of groups influences the specificity of the learned functions. We automatically determine the optimal partitioning given the number of groups, by minimizing the expected fitting error. Our empirical evaluation demonstrates that the group-specific objective functions more accurately describe the images of the corresponding group. The results of this paper are especially relevant to face model tracking, as individual faces will not change throughout an image sequence.}, } @inproceedings{ wimmer08robustly, author = {Matthias Wimmer and Christoph Mayer and Bernd Radig}, title = {Robustly Classifying Facial Components Using a Set of Adjusted Pixel Features}, booktitle = {Proc. of the International Conference on Face and Gesture Recognition (FGR08)}, year = {2008}, month = {September}, address = {Amsterdam, Netherlands}, } @inproceedings{ wimmer08anasmfittingmethod, author = {Matthias Wimmer and Shinya Fujie and Freek Stulp and Tetsunori Kobayashi and Bernd Radig}, title = {An {ASM} Fitting Method Based on Machine Learning that Provides a Robust Parameter Initialization for {AAM} Fitting}, booktitle = {Proc. of the International Conference on Automatic Face and Gesture Recognition (FGR08)}, year = {2008}, month = {September}, address = {Amsterdam, Netherlands}, xxxvolume = {}, xxxpages = {}, bib2html_pubtype = {Refereed Conference Paper}, bib2html_rescat = {Image Understanding}, bib2html_groups = {IU}, abstract = {Due to their use of information contained in texture, Active Appearance Models~(AAM) generally outperform Active Shape Models~(ASM) in terms of fitting accuracy. Although many extensions and improvements over the original AAM have been proposed, on of the main drawbacks of AAMs remains its dependence on good initial model parameters to achieve accurate fitting results. In this paper, we determine the initial model parameters for AAM fitting with ASM fitting, and use machine learning techniques to improve the scope and accuracy of ASM fitting. Combining the precision of AAM fitting with the large radius of convergence of learned ASM fitting improves the results by an order of magnitude, as our empirical evaluation on a database of publicly available benchmark images demonstrates.}, } @inproceedings{ mayer08arealtime, author = {Christoph Mayer and Matthias Wimmer and Freek Stulp and Zahid Riaz and Anton Roth and Martin Eggers and Bernd Radig}, title = {A Real Time System for Model-based Interpretation of the Dynamics of Facial Expressions}, booktitle = {Proc. of the International Conference on Automatic Face and Gesture Recognition~(FGR08)}, year = {2008}, month = {September}, address = {Amsterdam, Netherlands}, } @inproceedings{ wimmer08areyouhappy, author = {Matthias Wimmer and Christoph Mayer and Martin Eggers and Bernd Radig}, title = {Are You Happy with Your First Name?}, booktitle = {Proceedings of the 3\textsuperscript{rd} Workshop on Emotion and Computing: Current Research and Future Impact}, year = {2008}, month = {September}, address = {Kaiserslautern, Germany}, pages = {23-29}, } @inproceedings{mayer08interpreting, author = {Christoph Mayer and Matthias Wimmer and Freek Stulp and Zahid Riaz and Anton Roth and Martin Eggers and Bernd Radig}, title = {Interpreting the Dynamics of Facial Expressions in Real Time Using Model-based Techniques}, booktitle = {Proceedings of the 3\textsuperscript{rd} Workshop on Emotion and Computing: Current Research and Future Impact}, year = {2008}, month = {September}, address = {Kaiserslautern, Germany}, pages = {45-46}, } @inproceedings{ wimmer08robustlyFWS, author = {Matthias Wimmer and Sylvia Pietzsch and Christoph Mayer and Bernd Radig}, title = {Robustly Estimating the Color of Facial Components Using a Set of Adjusted Pixel Features}, booktitle = {14. Workshop Farbbildverarbeitung}, year = {2008}, month = {October}, address = {Aachen, Germany}, pages = {85-96}, } @inproceedings{ wimmer08recognizing, author = {Matthias Wimmer and Christoph Mayer and Bernd Radig}, title = {Recognizing Facial Expressions Using Model-based Image Interpretation}, booktitle = {Verbal and Nonverbal Communication Behaviours, COST Action 2102 International Workshop}, year = {2008}, month = {April}, address = {Vietri sul Mare, Italy}, publisher = {}, } @article{ wimmer08itech, author = {Matthias Wimmer and Zahid Riaz and Christoph Mayer and Bernd Radig}, title = {Recognizing Facial Expressions Using Model-based Image Interpretation}, journal = {Advances in Human-Computer Interaction}, publisher = "I-Tech", editor = {Shane Pinder}, year = {2008}, month = {October}, volume = {1}, pages = {587-600}, isbn = {978-953-7619-15-2}, abstract = {}, } @inproceedings{ riaz08INMICJournal, author = {Zahid Riaz and Christoph Mayer and Matthias Wimmer and Bernd Radig}, title = {Model Based Face Recognition Across Facial Expressions}, booktitle = {Journal of Information and Communication Technology}, year = {2008}, month = {December}, address = {}, publisher = {}, xxxvolume = {1}, xxxpages = {}, bib2html_pubtype = {Journal}, bib2html_groups = {IU}, bib2html_funding = {CoTeSys}, bib2html_rescat = {Human-Robot Interaction}, bib2html_domain = {Multi-Joint Action} } @inproceedings{riaz09icb, title = {A Model Based approach for Expression Invariant Face Recognition}, author = {Zahid Riaz and Christoph Mayer and Matthias Wimmer and Michael Beetz and Bernd Radig}, booktitle = {3rd International Conference on Biometrics, Alghero Italy}, pages = {}, publisher = {Springer}, series = {}, volume = {}, year = {2009}, note={}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {CoTeSys}, bib2html_rescat = {Human-Robot Interaction}, bib2html_domain = {Multi-Joint Action} } @inproceedings{riaz09cost, title = {Facial Expressions Recognition from Image Sequences}, author = {Zahid Riaz and Christoph Mayer and Michael Beetz and Bernd Radig}, booktitle = {2nd International Conference on Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions, Prague, Czech Republic}, pages = {}, publisher = {Springer}, series = {}, volume = {}, year = {2009}, note={}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {CoTeSys}, bib2html_rescat = {Human-Robot Interaction}, bib2html_domain = {Multi-Joint Action} } @inproceedings{riaz09caip, title = {Model Based Analysis of Face Images for Facial Feature Extraction}, author = {Zahid Riaz and Christoph Mayer and Michael Beetz and Bernd Radig}, booktitle = {Computer Analysis of Images and Patterns, Munster, Germany}, pages = {}, publisher = {Springer}, series = {}, volume = {}, year = {2009}, note={}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {CoTeSys}, bib2html_rescat = {Human-Robot Interaction}, bib2html_domain = {Multi-Joint Action} } @Article{mayer09adjusted, author = {Christoph Mayer and Matthias Wimmer and Bernd Radig}, title = {Adjusted Pixel Features for Facial Component Classification}, journal = {Image and Vision Computing Journal}, year = {2009}, note = {}, bib2html_groups = {IU}, bib2html_pubtype = {Journals}, bib2html_rescat = {Image Understanding} } @inproceedings{ riaz08INMIC, author = {Zahid Riaz and Michael Beetz and Bernd Radig}, title = {Shape Invariant Recognition of Segmented Human Faces using Eigenfaces}, booktitle = {Proceedings of the 12th~International Multitopic Conference}, year = {2008}, month = {}, address = {}, publisher = {IEEE}, xxxvolume = {}, xxxpages = {}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {CoTeSys}, bib2html_rescat = {Human-Robot Interaction}, bib2html_domain = {Multi-Joint Action} } @inproceedings{mayer09facial, title = {Facial Expression Recognition with 3D Deformable Models}, author = {Christoph Mayer and Matthias Wimmer and Martin Eggers and Bernd Radig}, booktitle = {Proceedings of the 2nd International Conference on Advancements Computer-Human Interaction~(ACHI)}, pages = {}, publisher = {Springer}, series = {}, volume = {}, year = {2009}, note = {Best Paper Award}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {CoTeSys}, bib2html_rescat = {Perception}, bib2html_domain = {Multi-Joint Action}, } @inproceedings{riaz09icict, title = {Image Normalization for Face Recognition using 3D Model}, author = {Zahid Riaz and Michael Beetz and Bernd Radig}, booktitle = {International Conference of Information and Communication Technologies, Karachi, Pakistan}, pages = {}, publisher = {IEEE}, series = {}, volume = {}, year = {2009}, note={}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {CoTeSys}, bib2html_rescat = {Human-Robot Interaction}, bib2html_domain = {Multi-Joint Action} } @inproceedings{riaz09bioId, title = {3D Model for Face Recognition across Facial Expressions}, author = {Zahid Riaz and Christoph Mayer and Michael Beetz and Bernd Radig}, booktitle = {Biometric ID Management and Multimodal Communication, Madrid, Spain}, pages = {}, publisher = {Springer}, series = {}, volume = {}, year = {2009}, note={}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {CoTeSys}, bib2html_rescat = {Human-Robot Interaction}, bib2html_domain = {Multi-Joint Action} } @inproceedings{riaz09acii, title = {A Unified Features Approach to Human Face Image Analysis and Interpretation}, author = {Zahid Riaz and Suat Gedikli and Michael Beetz and Bernd Radig}, booktitle = {Affective Computing and Intelligent Interaction, Amsterdam, Netherlands}, pages = {}, publisher = {IEEE}, series = {}, volume = {}, year = {2009}, note={Doctoral Consortium Paper}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {CoTeSys}, bib2html_rescat = {Human-Robot Interaction}, bib2html_domain = {Multi-Joint Action} } @inproceedings{riaz09fit, title = {Multi-Feature Fusion in Advanced Robotics Applications}, author = {Zahid Riaz and Christoph Mayer and Saquib Sarfraz and Michael Beetz and Bernd Radig}, booktitle = {Internaional Conference on Frontier of Information Technology}, pages = {}, publisher = {ACM}, series = {}, volume = {}, year = {2009}, note={}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {CoTeSys}, bib2html_rescat = {Human-Robot Interaction}, bib2html_domain = {Multi-Joint Action} } @inproceedings{gast09did, title = {Did I Get it Right: Head Gesture Analysis for Human-Machine Interaction}, author = {J\"urgen Gast and Alexander Bannat and Tobias Rehrl and Christoph Mayer and Frank Wallhoff and Gerhard Rigoll and Bernd Radig}, booktitle = {Human-Computer Interaction. Novel Interaction Methods and Techniques}, pages = {}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = {}, year = {2009}, note={}, bib2html_pubtype = {Conference Paper}, bib2html_groups = {IU}, bib2html_funding = {}, bib2html_rescat = {}, bib2html_domain = {} } @INPROCEEDINGS{lenz10adistributed, author = {C. Lenz and T. R\"oder and M. Eggers and S. Amin and T. Kisler and B. Radig and G. Panin and A. Knoll}, title = {{A Distributed Many-Camera System for Multi-Person Tracking}}, booktitle = {Proceedings of the First International Joint Conference on Ambient Intelligence~(AmI 2010)}, year = {2010}, editor = {R. Wichert and B. de Ruyter}, month = {November}, publisher = {Springer Lecture Notes in Computer Science} }