ResearchIssueComputerVision

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Research in Computer Vision

Table of Content

Abstract

The Computer Vision Group is composed of 12 members including Ph.D., Post Doc, Master Thesis, SEP and Hiwi students. The group works on different fundamental computer vision problems such as:

  • Image matching
  • Markerless tracking
  • Deformable registration
  • Image classification
  • Marker detection

    The objective is to perform scientific contributions in these different topics in order to build fast, robust and reliable vision-based medical and industrial applications.

    Contact Person and Group Coordination

    Slobodan Ilic
    Nassir Navab




    Research Projects in Computer Vision

    Deformable Object Tracking

    Deformable Object Tracking

    We address the problem of visual tracking of arbitrary objects that undergo signifi cant scale and appearance changes. The classical tracking methods rely on the bounding box surrounding the target object. Regardless of the tracking approach, the use of bounding box quite often introduces background information. This information propagates in time and its accumulation quite often results in drift and tracking failure. This is particularly the case with the particle filtering approach that is often used for visual tracking. However, it always uses a bounding box around the object to compute features of the particle samples. Since this causes the drift, we propose to use segmentation for sampling. Relying on segmentation and computing the colour and gradient orientation histograms from these segmented particle samples allows the tracker to easily adapt to the object's deformations, occlusions, orientation, scale and appearance changes. We propose two particle sampling strategies based on segmentation. In the rest, segmentation is done for every propagated particle sample, while in the second only the strongest particle sample is segmented. Depending on this decision there is obviously a trade-o between speed and performance. This project comes from a collaboration with EADS Innovations Works, Germany.
    Activity Recognition and Pose Estimation using Wearable Inertial Sensors

    Activity Recognition and Pose Estimation using Wearable Inertial Sensors

    We propose a method for human full-body pose tracking from measurements of wearable inertial sensors. Since the data provided by such sensors is sparse, noisy and often ambiguous, we use a compound prior model of feasible human poses to constrain the tracking problem. Our model consists of several low-dimensional, activity-specific motion models and an efficient, sampling-based activity switching mechanism. We restrict the search space for pose tracking by means of manifold learning. Together with the portability of wearable sensors, our method allows us to track human full-body motion in unconstrained environments. In fact, we are able to simultaneously classify the activity a person is performing and estimate the full-body pose. Experiments on movement sequences containing different activities show that our method can seamlessly detect activity switches and precisely reconstruct full-body pose from the data of only six wearable inertial sensors.
    Intensity-based Tracking

    Intensity-based Tracking

    In this project, we are interested in the fundamental problem of simultaneously tracking a piecewise-planar scene and accurately estimating the 3D displacement of the camera. The objective is to have an algorithm with a higher convergence frequency and a higher convergence rate than standard optimization methods while having an equivalent computational complexity. Since the tracking is aimed to be used in industrial real-time augmented reality applications, in addition to the robustness and the speed performance, the algorithm should be very precise. For some applications, we are investigating alternatives where an off-line learning step improves the efficiency and the convergence properties of the tracking algorithm.
    Linear Predictors

    Linear Predictors

    We address the problem of fast and robust tracking of free-form templates. For this we use Linear Predictors, introduced by Jurie & Dhome in 'Hyperplane Approximation for Template Matching', 2002. This approach allows to track templates at high frame-rates (>1000Hz) very robustly. However, the involved learning requires a significant amount of time and is therefore not suitable for many tasks where the environment is not known a-priori. Within this project we introduced several methods addressing this problem, starting with an adaptive approach that starts with learning a small template and then iteratively grows it over time. This approach also allows to adapt the size and shape of the tracked template during tracking, making it possible to handle occlusions. To speed-up the direct learning of large templates, we introduced two different approaches, a reformulation of the learning equations and a dimensionality reduction step. Both resulting in speed-ups up to two orders of magnitude.
    Spatio Temporal Modeling of Dynamic Scenes

    Spatio Temporal Modeling of Dynamic Scenes

    A great part of Computer Vision research has been dedicated to shape recovery, tracking and detection of 3D objects in images and videos. While excellent results have been achieved in these areas, the majority of the methods still assume static scenes and rigid objects, and rarely explore temporal information. However, the world surrounding us is highly dynamic, and in many situations objects deform over time. This temporal information provides a richer and denser source of information and have not yet been extensively exploited. Our objective within this project is to explore spatio-temporal information in order to recover 3D shapes and the motion of the deformable objects. Therefore, we refer to this area as spatio-temporal or four dimensional modeling (4D modeling). With the increased popularity of 3D content in film industry, TV, Internet and games, tools and methods that exploit spatio-temporal information and allow fast and automated 3D content production are going to be indispensable.
    Human Motion Analysis using Time-of-Flight (ToF) Cameras

    Human Motion Analysis using Time-of-Flight (ToF) Cameras

    Time-of-Flight cameras provide a novel means of acquiring depth information in a scene in real-time. In this project, we focus on analyzing human motion using this imaging device. We develop methods for capturing the full-body pose in 3D form ToF? camera data and for recognizing activities performed by a person. In particular, we investigate learning-based approaches that allow us to predict full-body poses and classify performed activities based on prior knowledge learned in a training phase. We also explore methods for direct, discriminative pose estimation without relying on a training phase.
    Stereo Time-of-Flight

    Stereo Time-of-Flight

    This paper describes a novel method to acquire depth images based on combining a pair of ToF? (Time of Flight) cameras. As opposed to approaches that register point clouds posterior to the image acquisition, we propose to combine the measurements of the two cameras at the acquisition level. To do so, we actively control the infrared lighting of the scene such that the two cameras emit and measure the reflected infrared light first one after the other, and then simultaneously. Assuming the scene is static during these three stages and gathering the obtained depth measurements in each state, we derive a set of constraints that allow us to optimize the two depth images. In terms of hardware requirements, the cameras need to have access to the control of the infrared lights and should work with the same infrared wavelength and with exactly the same modulation frequency. A quantitative evaluation of the performance of the proposed method for different objects and setups is provided based on a simulation of the ToF? cameras. Results on real images are also provided. In both simulation and real images the stereo-ToF acquisition is able to produce more accurate depth measurements.
    Scene Understanding From a Moving Camera

    Scene Understanding From a Moving Camera

    Modern vehicles are equipped with multiple cameras which are already used in various practical applications. Advanced driver assistance systems (ADAS) are of particular interest because of the safety and comfort features they offer to the driver. Camera based scene understanding is an important scientific problem that has to be addressed in order to provide the information needed for camera based driver assistance systems. While frontal cameras are widely used, there are applications where cameras observing lateral space can deliver better results. Fish eye cameras mounted in the side mirrors are particularly interesting, because they can observe a big area on the side of the vehicle and can be used for several applications for which the traditional front facing cameras are not suitable.

    We present a general method for scene understanding using 3D reconstruction of the environment around the vehicle. It is based on pixel-wise image labeling using a conditional random field (CRF). Our method is able to create a simple 3D model of the scene and also to provide semantic labels of the different objects and areas in the image, like for example cars, sidewalks, and buildings.

    We demonstrate how our method can be used for two applications that are of high importance for various driver assistance systems - car detection and free space estimation. We show that our system is able to perform in real time for speeds of up to 63 km/h.
    Rigid 3D Object Detection

    Rigid 3D Object Detection

    Object detection and localization is a crucial step for inspection and manipulation tasks in robotic and industrial applications. We present an object detection and localization scheme for 3D objects that combines intensity and depth data. A novel multimodal, scale- and rotation-invariant feature is used to simultaneously describe the object's silhouette and surface appearance. The object's position is determined by matching scene and model features via a Hough-like local voting scheme. The proposed method is quantitatively and qualitatively evaluated on a large number of real sequences, proving that it is generic and highly robust to occlusions and clutter. Comparisons with state of the art methods demonstrate comparable results and higher robustness with respect to occlusions.
    Deformable Guide Wire Tracking

    Deformable Guide Wire Tracking

    These days, X-ray fluoroscopic imaging is the modality used most widely to guide physicians in angiographic interventions. Current guidance procedures are based on a roadmap, e.g. a digital subtracted angiography (DSA) acquired from a fixed viewpoint and the injection of contrast agent to visualize the current vessel anatomy. Such roadmaps cannot directly be blended with the fluoroscopic sequence due to misalignment caused by respiratory motion. A fundamental step toward a successful integration of any navigation application into clinical routine is the estimation and compensation of respiratory motion. We propose a novel method for deformable tracking of multiple ridge segments based on a MAP-MRF formulation and efficient discrete optimization. Ridges are modeled as B-Spline curves which allows us to apply smooth deformations based on a minimal number of parameters. Our tracking framework is well adapted to the noisy domain of fluoroscopic imaging and the handling of multiple segments.
    Catheter Extraction in Abdominal Fluoroscopic Image Sequences

    Catheter Extraction in Abdominal Fluoroscopic Image Sequences

    This work's focus lies in catheter and guide wire extraction from abdominal fluoroscopic sequences. Due to the fact that fluoroscopic X-ray images are of low quality and suffer from a lot of background clutter in the abdominal area the task we are working on is very difficult and not yet solved by the community. The detection process is very important since a properly detected catheter or guide wire is required by many applications that have been proposed in the last few years. One of the major goals is the enhancement of the navigation during abdominal cathterizations in order to reduce the time of interventions and thus the radiation exposure for the patient and especially the physician.
    Stereo Vision

    Stereo Vision

    This project focuses on the development of novel methods for efficient or accurate binocular stereo vision.
    Real-Time 3D Reconstruction

    Real-Time 3D Reconstruction

    We are working on a real-time 3D reconstruction system aimed at recovering the 3D shape of objects inside a working area using only camera images. The working area is observed by 16 cameras mounted on the ceiling. Using these images we reconstruct an occupancy map and extract individual objects. The long-term goal of the project is to use this occupancy map to detect possible collisions between a robot placed in the working area and other objects in its path.
    Hybrid Tracking

    Hybrid Tracking

    In this project a complete real-time model-based tracking system for piecewise-planar objects which combines template-based and feature-based approaches is being developed. The main contributions are an extension to the ESM algorithm used for template-based tracking and the formulation of a feature-based tracking approach, which is specifically tailored for use in a real-time setting. In order to cope with highly dynamic scenarios, such as illumination changes, partial occlusions and fast object movement, the system adaptively switches between template-based tracking, feature-based tracking and a global initialization phase. The tracking system achieves real-time performance by applying a coarse-to-fine optimization approach and includes means to detect a loss of track.
    Discrepancy Check Using Augmented Reality

    Discrepancy Check Using Augmented Reality

    Construction companies employ CAD software during the planning phase but what is finally built often does not match the original plan. The procedure of validating the model is called 'discrepancy check'. The system proposed here allows the user to easily obtain an augmentation in order to find differences between the planned 3D model and the built items. The main difference to previous body of work in this field is the emphasis on usability and acceptance of the solution. While standard image-based solutions use markers or rely on a 'perfect' 3D model to find the pose of the camera; our software uses Anchor-Plates. Anchor-Plates are rectangular structures installed on walls and ceiling in the majority of industrial edifices. We are using them as landmarks because they are the most reliable components often used as reference coordinates by constructors. Furthermore; for real industrial applications; they are the most suitable solutions in terms of general applicability. For this project we try to bring a innovative solution to the discrepancy check procedure by applying Augmented Reality.
    Deformable Shape Matching

    Deformable Shape Matching

    This project aims a real-time detection of two dimensional deformable patterns for industrial inspection. The fast, robust and accurate localization of a given 2D object template in images has been a research topic since many decades. The results of this research efforts has enabled numerous different industrial applications, as the detection of the objects pose is the natural prerequisite for any senseful operation like counting, defect detection or pick and place operations. In case that the object to be localized is rigid or articulated, different successful approaches have been proposed and are applied in everyday industrial practice. Various applications domains, which necessitate the detection of deformable objects, like medical, packaging, textile or paper industry, can still not be comprehensively solved. The goal of current research is the development of a method which is fast, robust to illumination changes, occlusion and clutter plus allows the objects shape to deform.
    Vision Targeted CAD Models

    Vision Targeted CAD Models

    This project investigates vision features and their intelligent application on known CAD models for pose estimation. The advantage of combining CAD models and vision features lies in the known 3D geometry of the CAD model. Exemplary vision features are e.g. SIFT features, Randomized Trees, SURF features and other well known methods. The main goal is to use such an extended CAD model for pose estimation and for tracking initialization.
    Workflow Analysis Using 4D Reconstruction Data

    Workflow Analysis Using 4D Reconstruction Data

    This project targets the worklow analysis of an interventional room equipped with 16 cameras fixed on the ceiling. It uses real-time 3D reconstruction data and information from other available sensors to recognize objects, persons and actions. This provides complementary information to specific procedure analysis for the development of intelligent and context-aware support systems in surgical environments.
    Endoscopic Video Processing

    Endoscopic Video Processing

    The diagnosis and surveillance of several gastrointestinal (GI) diseases such as colateral or oesaphageal cancer are performed under endoscopic guidance. However, currently there exists no computer aided navigation tool to support these procedures. The goal of this project is to provide an advanced visualisation and automatic recognition tool in order to support the navigation and targeting in GI endoscopic procedures. The aim of the project is twofold: reconstructing the organ surface from endoscopic videos using monoSLAM and automatically recognising the biopsy locations to support the navigation and targeting in endoscopy.

    Related Publications

    2012
    S. Hinterstoisser, C. Cagniart, S. Ilic, P. Sturm, N. Navab, P. Fua, V. Lepetit
    Gradient Response Maps for Real-Time Detection of Texture-Less Objects
    IEEE Transactions on Pattern Analysis and Maschine Intelligence (TPAMI). (bib)
    S. Holzer, S. Ilic, N. Navab
    Multi-Layer Adaptive Linear Predictors for Real-Time Tracking
    IEEE Transactions on Pattern Analysis and Maschine Intelligence (TPAMI). (bib)
    A. Ladikos, W. Wein
    Geometric calibration using bundle adjustment for cone-beam computed tomography devices
    SPIE Medical Imaging, February 2012, San Diego, USA. (bib)
    S. Holzer, S. Ilic, D. J. Tan , N. Navab
    Efficient Learning of Linear Predictors using Dimensionality Reduction (Oral)
    Asian Conference on Computer Vision (ACCV), Korea, Daejeon, November 2012 (bib)
    S. Hinterstoisser, V. Lepetit, S. Ilic, S. Holzer, G. Bradski, K. Konolige, N. Navab
    Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes
    Asian Conference on Computer Vision (ACCV), Korea, Daejeon, November 2012 (bib)
    S. Holzer, J. Shotton, P. Kohli
    Learning to Efficiently Detect Repeatable Interest Points in Depth Data
    12th European Conference on Computer Vision (ECCV), Firenze, Italy, October 2012. (bib)
    S. Holzer, M. Pollefeys , S. Ilic, D. J. Tan , N. Navab
    Online Learning of Linear Predictors for Real-Time Tracking
    12th European Conference on Computer Vision (ECCV), Firenze, Italy, October 2012. (bib)
    S. Holzer, R. B. Rusu, M. Dixon, S. Gedikli, N. Navab
    Real-Time Surface Normal Estimation from Organized Point Cloud Data Using Integral Images
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vila Moura, Algarve, Portugal, October 2012. (bib)
    C. Unger, E. Wahl, P. Sturm, S. Ilic
    Stereo Fusion from Multiple Viewpoints
    Joint 34th DAGM and 36th OAGM Symposium, Graz, Austria, August 28-31, 2012 (bib)
    M. Schneider, M. Baust, M. Beimforde
    Computer Vision im Fahrzeug: Systemanforderungen und Hardwarebetrachtungen zur kamerabasierten Umfeldwahrnehmung
    Automotive meets Electronics, 2012 (bib)
    2011
    J. Bohren, R. B. Rusu, E. G. Jones, E. Marder-Eppstein, C. Pantofaru, M. Wise, L. Mösenlechner, W. Meeussen, S. Holzer
    Towards Autonomous Robotic Butlers: Lessons Learned with the PR2
    2011 IEEE International Conference on Robotics and Automation, Shanghai, China, May 2011. (bib)
    C. Unger, E. Wahl, S. Ilic
    Parking assistance using dense motion-stereo
    Journal of Machine Vision and Applications, Special Issue, December 1st 2011 (bib)
    S. Hinterstoisser, S. Holzer, C. Cagniart, S. Ilic, K. Konolige, N. Navab, V. Lepetit
    Multimodal Templates for Real-Time Detection of Texture-less Objects in Heavily Cluttered Scenes (Oral)
    IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011. (bib)
    S. Hinterstoisser, V. Lepetit, S. Benhimane, P. Fua, N. Navab
    Learning Real-Time Perspective Patch Rectification
    International Journal of Computer Vision (IJCV), Springer Verlag, The final publication is available at www.springerlink.com (bib)
    D. Pangercic, V. Haltakov , M. Beetz
    Fast and Robust Object Detection in Household Environments Using Vocabulary Trees with SIFT Descriptors
    In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on Active Semantic Perception and Object Search in the Real World, 2011 (bib)
    C. Unger, E. Wahl, S. Ilic
    Efficient Stereo Matching for Moving Cameras and Decalibrated Rigs
    Intelligent Vehicles (IV), 6 - 8 June, Baden-Baden, Germany 2011 (bib)
    C. Unger, E. Wahl, S. Ilic
    Efficient Stereo and Optical Flow with Robust Similarity Measures
    33rd Annual Symposium of the German Association for Pattern Recognition, Frankfurt am Main, Germany, August 30th - September 2nd 2011 (bib)
    S. Lieberknecht, S. Benhimane, S. Ilic
    Simultaneous Reconstruction and Tracking of non-planar Templates
    33rd Annual Symposium of the German Association for Pattern Recognition, Frankfurt am Main, Germany, August 30th - September 2nd 2011 (bib)
    P. Schroeder, A. Bartoli, P. Georgel, N. Navab
    Closed-Form Solutions to Multiple-View Homography Estimation
    IEEE Workshop on Motion and Video Computing (WMVC), Kona, HI, USA, January 2011 (bib)
    2010
    C. Cagniart, E. Boyer, S. Ilic
    Probabilistic Deformable Surface Tracking From Multiple Videos
    11th European Conference on Computer Vision (ECCV), Crete, Greece, September 2010. (bib)
    A. Ladikos, E. Boyer, N. Navab, S. Ilic
    Region Graphs for Organizing Image Collections
    ECCV Workshop on Reconstruction and Modeling of Large-Scale 3D Virtual Environments Workshop, September 2010 , Crete, Greece. (bib)
    A. Ladikos, C. Cagniart, R. Gothbi, M. Reiser, N. Navab
    Estimating Radiation Exposure in Interventional Environments
    Medical Image Computing and Computer-Assisted Intervention (MICCAI), September 2010 , Beijing, China. (bib)
    S. Holzer, S. Ilic, N. Navab
    Adaptive Linear Predictors for Real-Time Tracking
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, California (USA), June 2010. (bib)
    N. Alt, S. Hinterstoisser, N. Navab
    Rapid Selection of Reliable Templates for Visual Tracking
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, California (USA), June 2010. (bib)
    S. Hinterstoisser, V. Lepetit, S. Ilic, P. Fua, N. Navab
    Dominant Orientation Templates for Real-Time Detection of Texture-Less Objects
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, California (USA), June 2010. (bib)
    C. Cagniart, E. Boyer, S. Ilic
    Free-From Mesh Tracking : a Patch-Based Approach
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, California (USA), June 2010. (bib)
    C. Cagniart, E. Boyer, S. Ilic
    Iterative Deformable Surface Tracking in Multi-View Setups ( Oral Presentation )
    5th International Symposium 3D Data Processing, Visualization and Transmission (3DPVT), May 17-20 2010, Paris France (bib)
    E. Wahl, C. Unger, A. Zeller, D. Rossberg
    3D-Environment Modeling as an Enabler for Autonomous Vehicles
    ATZ Automobiltechnische Zeitschrift, Ausgabe 02/2010. (bib)
    2009
    S. Hinterstoisser, O. Kutter, N. Navab, P. Fua, V. Lepetit
    Real-Time Learning of Accurate Patch Rectification (Oral Presentation)
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Miami, Florida (USA), June 2009. (bib)
    P. Georgel, A. Bartoli, N. Navab
    Simultaneous In-Plane Motion Estimation and Point Matching Using Geometric Cues Only
    IEEE Workshop on Motion and Video Computing (WMVC), Snowbird, Utah, USA, December 7-10 2009 (bib)
    P. Georgel, S. Benhimane, J. Sotke, N. Navab
    Recovering the Full Pose from a Single Keyframe
    IEEE Workshop on Applications of Computer Vision (WACV) , Snowbird, Utah, USA, December 7-8 2009 (bib)
    F. Schweiger, B. Zeisl, P. Georgel, G. Schroth, E. Steinbach, N. Navab
    Maximum Detector Response Markers for SIFT and SURF
    In Vision, Modeling and Visualization Workshop (VMV), Braunschweig (bib)
    P. Georgel, P. Schroeder, N. Navab
    Navigation Tools for Augmented CAD Viewing
    IEEE Computer Graphic and Application (CGA), special issue on 3D User Interfaces (bib)
    P. Georgel, S. Benhimane, J. Sotke, N. Navab
    Photo-based Industrial Augmented Reality Application Using a Single Keyframe Registration Procedure
    The 8th IEEE and ACM International Symposium on Mixed and Augmented Reality, Orlando, US, Oct. 19 - 22, 2009 (bib)
    C. Cagniart, E. Boyer, S. Ilic
    Iterative Mesh Deformation for Dense Surface Tracking
    The 2009 IEEE International Workshop on 3-D Digital Imaging and Modeling, October 3-4, 2009, Kyoto, Japan (bib)
    B. Zeisl, P. Georgel, F. Schweiger, E. Steinbach, N. Navab
    Estimation of Location Uncertainty for Scale Invariant Feature Points
    British Machine Vision Conference (BMVC), London (UK), September 7-10, 2009 (bib)
    C. Unger, S. Benhimane, E. Wahl, N. Navab
    Efficient Disparity Computation without Maximum Disparity for Real-Time Stereo Vision
    British Machine Vision Conference (BMVC), London (UK), September 7-10, 2009 (bib)
    A. Bigdelou, A. Ladikos, N. Navab
    Incremental Visual Hull Reconstruction
    British Machine Vision Conference (BMVC), London (UK), September 7-10, 2009 (bib)
    S. Holzer, S. Hinterstoisser, S. Ilic, N. Navab
    Distance Transform Templates for Object Detection and Pose Estimation
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Miami, Florida (USA), June 2009. (bib)
    A. Hofhauser, Carsten Steger, N. Navab
    Perspective planar shape matching
    IS&T/SPIE Electronic Imaging, San Jose, California, USA, January 2009. (bib)
    2008
    P. Georgel, P. Schroeder, S. Benhimane, M. Appel, N. Navab
    How to Augment the Second Image? Recovery of the Translation Scale in Image to Image Registration
    The 7th IEEE and ACM International Symposium on Mixed and Augmented Reality, Cambridge, UK, Sept. 15 - 18, 2008. (bib)
    S. Hinterstoisser, S. Benhimane, V. Lepetit, P. Fua, N. Navab
    Simultaneous Recognition and Homography Extraction of Local Patches with a Simple Linear Classifier
    British Machine Vision Conference (BMVC), Leeds (UK), September 1-4, 2008 (bib)
    D. Zikic, B. Glocker, M. S. Hansen, A. Khamene, N. Navab
    Construction of Statistical Shape Models from Minimal Deformations
    Workshop "Manifolds in Medical Imaging: Metrics, Learning and Beyond" in conjunction with Medical Image Computing and Computer-Assisted Intervention (MICCAI 2008), New York City, NY, USA, September 2008 (bib)
    D. Zikic, M. S. Hansen, B. Glocker, A. Khamene, R. Larsen, N. Navab
    Computing Minimal Deformations: Application to Construction of Statistical Shape Models.
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska (USA), June 24-26, 2008 (bib)
    M. S. Hansen, B. Glocker, N. Navab, R. Larsen
    Adaptive Parametrization of Multivariate B-splines for Image Registration
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska (USA), June 24-26, 2008 (bib)
    B. Glocker, N. Paragios, N. Komodakis, G. Tziritas, N. Navab
    Optical Flow Estimation with Uncertainties through Dynamic MRFs
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska (USA), June 24-26, 2008 (bib)
    A. Hofhauser, Carsten Steger, N. Navab
    Edge-based Template Matching and Tracking for Perspectively Distorted Planar Objects
    4th International Symposium on Visual Computing, Las Vegas, Nevada, USA, December 2008. (bib)
    A. Ladikos, S. Benhimane, N. Navab
    High Performance Model-Based Object Detection and Tracking
    Computer Vision and Computer Graphics. Theory and Applications, Communications in Computer and Information Science, Vol. 21, Springer, 2008 (bib)
    A. Ladikos, S. Benhimane, N. Navab
    Multi-View Reconstruction using Narrow-Band Graph-Cuts and Surface Normal Optimization
    British Machine Vision Conference (BMVC), Leeds (UK), September 1-4, 2008 (bib)
    A. Ladikos, S. Benhimane, N. Navab
    Real-time 3D Reconstruction for Collision Avoidance in Interventional Environments
    Medical Image Computing and Computer-Assisted Intervention, MICCAI, 2008, New York, USA, September 6-10 2008 (bib)
    A. Bigdelou, A. Ladikos, N. Navab
    Shape from Silhouette in Dynamic Scenes
    Technical Report at Technical University of Munich (TUM), Munich (DE), August 15, 2008 (bib)
    A. Ladikos, S. Benhimane, N. Navab
    Efficient Visual Hull Computation for Real-Time 3D Reconstruction using CUDA
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska (USA), June 2008. Workshop on Visual Computer Vision on GPUs (CVGPU) (bib)
    S. Hinterstoisser, S. Benhimane, N. Navab, P. Fua, V. Lepetit
    Online Learning of Patch Perspective Rectification for Efficient Object Detection
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska (USA), June 2008. (bib)
    C. Unger, M. Groher, N. Navab
    Image Based Rendering for Motion Compensation in Angiographic Roadmapping
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska (USA), June 24-26, 2008 (bib)
    A. Ladikos, S. Benhimane, M. Appel, N. Navab
    Model-Free Markerless Tracking for Remote Support in Unknown Environments
    International Conference on Computer Vision Theory and Applications, Funchal, Portugal, January 2008. (bib)
    A. Hofhauser, Carsten Steger, N. Navab
    Harmonic deformation model for edge based template matching
    International Conference on Computer Vision Theory and Applications, Funchal, Portugal, January 2008. (bib)
    S. Benhimane, H. Najafi, M. Grundmann , E. Malis, Y. Genc, N. Navab
    Real-time object detection and tracking for industrial applications
    International Conference on Computer Vision Theory and Applications, Funchal, Portugal, January 2008. (bib)
    A. Zaharescu, C. Cagniart, S. Ilic, E. Boyer, R. Horaud
    Camera Clustering for Multi-Resolution 3-D Surface Reconstruction
    ECCV 2008 Workshop on Multi Camera and Multi-modal Sensor Fusion Algorithms and Applications - 2008 (bib)
    2007
    S. Hinterstoisser, S. Benhimane, N. Navab
    N3M: Natural 3D Markers for Real-Time Object Detection and Pose Estimation
    IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, October 14-20, 2007 (bib)
    S. Benhimane, E. Malis
    Homography-based 2D Visual Tracking and Servoing
    Special Joint Issue IJCV/IJRR on Robot and Vision. Published in The International Journal of Robotics Research, Volume 26, No. 7, 661-676, July 2007. (bib)
    S. Benhimane, A. Ladikos, V. Lepetit, N. Navab
    Linear and Quadratic Subsets for Template-Based Tracking
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, June 2007 (bib)
    A. Ladikos, S. Benhimane, N. Navab
    A Real-Time Tracking System Combining Template-Based and Feature-Based Approaches
    International Conference on Computer Vision Theory and Applications, Barcelona, Spain, March 2007. (bib)
    2006
    G. Panin, A. Ladikos, A. Knoll
    An Efficient and Robust Real-Time Contour Tracking System
    IEEE International Conference on Computer Vision Systems, New York, USA, January 2006. (bib)
    2005
    F. Althoff, R. Lindl, L. Walchshäusl
    Robust Multimodal Hand- and Head Gesture Recognition for controlling Automotive Infotainment Systems
    VDI-Tagung - Der Fahrer im 21. Jahrhundert, Braunschweig, Germany, 21.11.2005 (bib)
    1999
    G. Klinker, D. Stricker, D. Reiners
    An Optically Based Direct Manipulation Interface for Human-Computer Interaction in an Augmented World
    Computers and Graphics 23(6), 1999. (Also in Proc. Workshop EGVE'99, Austria, 1999.) (bib)
    G. Klinker
    Augmented Reality: A problem in need of many computer vision-based solutions
    NATO Advanced Research Workshop at the 8. International Conference on the Computer Analysis of Images and Patterns (CAIP-99), org. by A. Leonardis and R. Bajcsy. Ljubljana Slowenia, Aug. 29-31, 1999. (bib)
    G. Klinker, D. Stricker, D. Reiners
    Augmented Reality: A Balance Act between High Quality and Real-Time Constraints
    Proc. 1rst International Symposium on Mixed Reality (ISMR'99): Y. Ohta and H. Tamura (eds.), "Mixed Reality - Merging Real and Virtual Worlds", 1999. (bib)
    1998
    D. Stricker, G. Klinker, D. Reiners
    A Fast and Robust Line-based Optical Tracker for Augmented Reality Applications
    Proc. 1rst International Workshop on Augmented Reality (IWAR'98), San Francisco, Nov. 1998, pp. 31-46. (bib)
    D. Reiners, D. Stricker, G. Klinker, S. Mueller
    Augmented Reality for Construction Tasks: Doorlock Assembly
    Proc. 1rst International Workshop on Augmented Reality (IWAR'98), San Francisco, Nov. 1998, pp. 31-46. (bib)
    G. Klinker, D. Stricker, D. Reiners
    The Use of Reality Models in Augmented Reality Applications
    Proc. European Workshop on 3D Structure from Multiple Images of Large-scale Environments (SMILE), in combination with ECCV'98, 1998; pp. 275-289. (bib)
    1997
    D. Koller, G. Klinker, E. Rose, D. Breen, R. Whitaker, M. Tuceryan
    Real-time vision-based camera tracking for augmented reality applications
    Proc. ACM Symp on Virtual Reality, Software and Technology (VRST'97), 1997, pp. 87-94. (bib)
    D. Koller, G. Klinker, E. Rose, D. Breen, R. Whitaker, M. Tuceryan
    Automated camera calibration and 3D egomotion estimation for augmented reality applications
    Proc. 7th Int. Conf. on Computer Analysis of Images and Patterns (CAIP'97), 1997. (bib)
    G. Klinker, K. Ahlers, D. Breen, P.-Y. Chevalier, C. Crampton, D. Greer, A. Kramer, E. Rose, M. Tuceryan, R. Whitaker
    Confluence of Computer Vision and Interactive Graphics for Augmented Reality
    PRESENCE - Teleoperators and Virtual Environments, Special Issue on Augmented Reality 6(4), 1997, pp. 433-451. (bib)
    1993
    G. Klinker
    A Physical Approach to Color Image Understanding
    Book published by AK Peters, Ltd., Wellesley MA, March 1993. (Based on Ph.D. thesis, Computer Science Department, Carnegie-Mellon University, May, 1988) (bib)
    1988
    G. Klinker
    A Physical Approach to Color Image Understanding
    Ph.D. thesis, Computer Science Department, Carnegie-Mellon University, May, 1988 (bib)

    Working Group

    Belagiannis
    Vasileios Belagiannis
    Duliu
    Alexandru O. Duliu
    Hinterstoisser
    Stefan Hinterstoisser
    Holzer
    Stefan Holzer
    Huang
    Chun Hao Huang
    Ilic
    Slobodan Ilic
    Kehl
    Wadim Kehl
    Mateus
    Diana C. Mateus
    Meng
    Ma Meng
    Tan
    David Tan
    Wieczorek
    Matthias Wieczorek


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