SlobodanIlic

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

Slobodan Ilic

News

Me PD Dr. Slobodan Ilic
Guest Research Scientist
E-Mail:
Phone: +49 089-289 17082
Skype: Slobodan.Ilic.jf
Address: Technische Universität München
Fakultät für Informatik, I-16
Boltzmannstr. 3
85748 Garching b. München
Germany
Room: Room:03.13.040

Senior Key Expert Research Scientist
E-Mail:
Phone: +49 (89) 636-23207
Fax: +49 (89) 636-46192
Address: Siemens AG
Corporate Technology Research & Technology Center
CT RTC SET INT-DE
Otto-Hahn-Ring 6
81739 München, Deutschland

Resume

In January 2014 I joined Siemens Corporate Technology in Munich as a Senior Key Expert Research Scientist. In parallel I still manage and collaborate with the Vision Group at Nassir Navab's Chair, which I joined in February 2009 as a leader of the Computer Vision Group . Form June 2006 I was a Senior Researcher at Deutsche Telekom Laboratories Deutsche Telekom Laboratories in Berlin. Before that I was a postdoctoral fellow for one year at Computer Vision Laboratory , EPFL, Switzerland, where I received my PhD in 2005. My research interests include mode-based Computer Vision, 3D modeling and reconstruction, tracking of rigid and deformable objects including objects and humans.

Research Topics

I am currently involved in conducting research in the following research areas.

  • 3D deformable object modeling, shape recovery and tracking in monocular videos.
  • Tracking 3D surface deformations from multi-view videos (4D modeling).
  • Real-time 3D object detection and tracking.
  • Real-time motion-stereo and stereo fusion.
  • 3D object(rigid and deformable) detection in depth data.
  • Real-time template tracking.
  • Human-pose estimation.
  • Segmentation.

Computer Vision Group Team

Computer Vision Group consists of internal and external PhD students. External PhD student are financed by our industrial partners, and work on commonly defined PhD topics. Currently I work and advise/co-advise the following PhD students.

PhD students:

Master and bachelor students:

  • Hassan Sarah
  • Ievgenia Dobryden

Alumni

Finishing PhD Students

Past PhD students

Past Master Students and Interns

  • Miroslava Slavcheva
  • Bibiana do Canto Angonese
  • Fausto Milletari
  • Andres Sanchez
  • Christian Amman
  • Christian Ruprecht
  • Simon Sklenak
  • Anke Schwarz
  • Jan Kremer
  • Maike Forberg
  • David Tan
  • Vasilis Belagianis
  • Vladimir Haltakov
  • Hagen Kaiser -- SIEMENS and LMU
  • Benoit Diotte -- TUM

Teaching

I teach the following courses:

I would like to thank all authors whose slides I used for the lecture in tracking and detection. I cited most of them on the slides, but there are still some I didn't, so please find this statement as a replacement for citations on the slides.

I previously thought the course "Selected Topics in Computer Vision" together with Vincent Lepetit at the EPFL, which was about deformable object tracking.

Research projects

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.
Geodesic pixel neighborhoods for multi-class image segmentation

Geodesic pixel neighborhoods for multi-class image segmentation

The problem of multi-class image segmentation is traditionally addressed either with a Conditional Random Field model or by directly classifying each pixel. In this paper we introduce a new classification framework built around the concept of pixel neighborhoods. A standard unary classifier is used to recognize each pixel based on features computed from the image and then a second classifier is trained on the predictions from the first one summarized over the neighborhood of each pixel by a new histogram feature. We define a local and a global pixel neighborhoods which adapt to the image structure by making use of the geodesic distance defined over image intensities. We evaluate our model on three challenging datasets and show that our model is able to capture both local and global context relations. We compare our method to two strongly related, well known methods and show increased performance.
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.
Semantic segmentation based traffic light detection at day and at night

Semantic segmentation based traffic light detection at day and at night

Traffic light detection from a moving vehicle is an important technology both for new safety driver assistance functions as well as for autonomous driving in the city. In this paper we present a machine learning framework for detection of traffic lights that can handle in real-time both day and night situations in a unified manner. A semantic segmentation method is employed to generate traffic light candidates, which are then confirmed and classified by a geometric and color features based classifier. Temporal consistency is enforced by using a tracking by detection method. We evaluate our method on a publicly available dataset recorded at daytime in order to compare to existing methods and we show similar performance. We also present an evaluation on two additional datasets containing more than 50 intersections with multiple traffic lights recorded both at day and during nighttime and we show that our method performs consistently in those situations.
Framework for generation of synthetic ground truth data for driver assistance applications

Framework for generation of synthetic ground truth data for driver assistance applications

High precision ground truth data is a very important factor for the development and evaluation of computer vision algorithms and especially for advanced driver assistance systems. Unfortunately, some types of data, like accurate optical flow and depth as well as pixel-wise semantic annotations are very difficult to obtain.

In order to address this problem, in this paper we present a new framework for the generation of high quality synthetic camera images, depth and optical flow maps and pixel-wise semantic annotations. The framework is based on a realistic driving simulator called VDrift [1], which allows us to create traffic scenarios very similar to those in real life.

We show how we can use the proposed framework to generate an extensive dataset for the task of multi-class image segmentation. We use the dataset to train a pairwise CRF model and to analyze the effects of using various combinations of features in different image modalities.
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.
Stereo Vision

Stereo Vision

This project focuses on the development of novel methods for efficient or accurate binocular stereo vision.

Publications

2016
C.-H. Huang, B. Allain, J.-S. Franco, N. Navab, S. Ilic, E. Boyer
Volumetric 3D Tracking by Detection
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, June 2016 (spotlight presentation) (supp., poster)
The first two authors contribute equally to this paper.
(bib)
T. Birdal, E. Bala, T. Eren, S. Ilic
Online Inspection of 3D Parts via a Locally Overlapping Camera Network
IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, March 2016 (bib)
C.-H. Huang, C. Cagniart, E. Boyer, S. Ilic
A Bayesian Approach to Multi-view 4D Modeling
International Journal of Computer Vision (IJCV), Springer Verlag. The final publication is available at www.springerlink.com.
The first two authors contribute equally to this paper.
(bib)
2015
W. Kehl, F. Tombari, N. Navab, S. Ilic, V. Lepetit
Hashmod: A Hashing Method for Scalable 3D Object Detection
British Machine Vision Conference (BMVC), Swansea, UK, September 2015 (bib)
V. Belagiannis, S. Amin, M. Andriluka, B. Schiele, N. Navab, S. Ilic
3D Pictorial Structures Revisited: Multiple Human Pose Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), To appear (bib)
D. J. Tan , F. Tombari, S. Ilic, N. Navab
A Versatile Learning-based 3D Temporal Tracker: Scalable, Robust, Online
International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015 (bib)
T. Birdal, S. Ilic
Point Pair Features Based Object Detection and Pose Estimation Revisited
IEEE Computer Society Conference on 3D Vision (3DV), Lyon, France, October 2015 (poster) (bib)
V. Haltakov , J. Mayr, C. Unger, S. Ilic
Semantic segmentation based traffic light detection at day and at night.
German Conference on Pattern Recognition, 7.-10. October 2015 (bib)
B. Drost, S. Ilic
Graph-Based Deformable 3D Object Matching
German Conference on Pattern Recognition (GCPR), Aachen, Germany, October 2015. (bib)
F. Milletari, W. Kehl, F. Tombari, S. Ilic, A. Ahmadi, N. Navab
Universal Hough dictionaries for object tracking
British Machine Vision Conference (BMVC), Swansea, UK, September 2015 (bib)
C.-H. Huang, E. Boyer, B. do Canto Angonese, N. Navab, S. Ilic
Toward User-specific Tracking by Detection of Human Shapes in Multi-Cameras
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, June 2015 (supp., poster) (bib)
M. Yigitsoy, V. Belagiannis, A. Djurka, A. Katouzian, S. Ilic, F. Pernus, A. Eslami, N. Navab
Random Ferns for Multiple Target Tracking in Microscopic Retina Image Sequences
International Symposium on Biomedical Imaging: From Nano to Macro, New York, USA, April 2015 (bib)
2014
W. Kehl, N. Navab, S. Ilic
Coloured signed distance fields for full 3D object reconstruction
British Machine Vision Conference (BMVC), Nottingham, UK, September 2014 (bib)
D. J. Tan , S. Ilic
Multi-Forest Tracker: A Chameleon in Tracking
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, USA, June 24, 2014 (bib)
V. Belagiannis, X. Wang, B. Schiele, P. Fua, S. Ilic, N. Navab
Multiple Human Pose Estimation with Temporally Consistent 3D Pictorial Structures
ChaLearn Looking at People Workshop, European Conference on Computer Vision (ECCV), Zurich, Switzerland, September 6-12, 2014. (bib)
J. Lallemand, A. Ronge, M. Szczot, S. Ilic
Pedestrian Orientation Estimation
GCPR 2014. 36th German Conference on Pattern Recognition. Münster, Germany. 2 – 5 September 2014 (bib)
V. Haltakov , C. Unger, S. Ilic
Geodesic pixel neighborhoods for multi-class image segmentation
British Machine Vision Conference, 1.-5. September 2014 (bib)
J. Lallemand, M. Szczot, S. Ilic
Human Pose Estimation in Stereo Images
AMDO 2014. VIII Conference on Articulated Motion and Deformable Objects. Palma, Mallorca, Spain. 16 – 18 July 2014 (bib)
V. Belagiannis, S. Amin, M. Andriluka, B. Schiele, N. Navab, S. Ilic
3D Pictorial Structures for Multiple Human Pose Estimation (Oral Presentation)
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, USA, June 24, 2014 (bib)
V. Belagiannis, C. Amann, N. Navab, S. Ilic
Holistic Human Pose Estimation with Regression Forests
AMDO 2014. VIII Conference on Articulated Motion and Deformable Objects. Palma, Mallorca, Spain. 16 – 18 July 2014 (bib)
C.-H. Huang, E. Boyer, N. Navab, S. Ilic
Human Shape and Pose Tracking Using Keyframes
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, USA, June 24, 2014 (supp., poster) (bib)
D. J. Tan , S. Holzer, N. Navab, S. Ilic
Deformable Template Tracking in 1ms (Oral)
British Machine Vision Conference, Nottingham, UK, September 1, 2014 (bib)
2013
B. Drost, S. Ilic
A Hierarchical Voxel Hash for Fast 3D Nearest Neighbor Lookup
German Conference on Pattern Recognition (GCPR), Saarbruecken, Germany, September 2013. (bib)
V. Haltakov , C. Unger, S. Ilic
Framework for generation of synthetic ground truth data for driver assistance applications
35th German Conference on Pattern Recognition, 3.-6. September 2013 (bib)
B.-K. Seo, H. Park, J. Park, S. Hinterstoisser, S. Ilic
Optimal Local Searching for Fast and Robust Textureless 3D Object Tracking in Highly Cluttered Backgrounds
IEEE Transactions on Visualization and Computer Graphics (TVCG) (bib)
C. Rupprecht, O. Pauly, C. Theobalt, S. Ilic
3D Semantic Parameterization for Human Shape Modeling: Application to 3D Animation. (Oral Presentation)
In Proc. International Conference on 3D Vision (3DV 2013) (bib)
J. Lallemand, O. Pauly, L. Schwarz, D. J. Tan , S. Ilic
Multi-task Forest for Human Pose Estimation in Depth Images. (Oral Presentation)
In Proc. International Conference on 3D Vision (3DV 2013) (bib)
C.-H. Huang, E. Boyer, S. Ilic
Robust Human Body Shape and Pose Tracking
International Conference on 3D Vision (3DV), Seattle, USA, June 29, 2013 (oral presentation, best paper award runner up) (slides, poster) (bib)
M. Pavlic, , S. Ilic
Classification of Images in Fog and Fog-Free Scenes for Use in Vehicles
Intelligent Vehicles Symposium, Gold Coast, 23-26 June 2013 (bib)
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)
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, 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)
T. Birdal, D. Mateus, S. Ilic
Towards A Complete Framework For Deformable Surface Recovery Using RGBD Cameras
IEEE IROS'12 Workshop on Color-Depth Fusion in Robotics, Vila Moura, Portugal, October 2012 (bib)
B. Drost, S. Ilic
3D Object Detection and Localization Using Multimodal Point Pair Features
Second Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization & Transmission (3DIMPVT), Zurich, Switzerland, 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)
V. Belagiannis, F. Schubert, N. Navab, S. Ilic
Segmentation Based Particle Filtering for Real-Time 2D Object Tracking
12th European Conference on Computer Vision (ECCV), Firenze, Italy, October 07-13, 2012 (bib)
M. Pavlic, H. Belzner, , S. Ilic
Image Based Fog Detection in Vehicles
Intelligent Vehicles Symposium, Alcalá de Henares 4.-6. June 2012 (bib)
V. Haltakov , H. Belzner, S. Ilic
Scene Understanding From a Moving Camera for Object Detection and Free Space Estimation
Intelligent Vehicles Symposium, Alcalá de Henares 4.-6. June 2012 (bib)
R. Stauder, V. Belagiannis, L. Schwarz, A. Bigdelou, E. Soehngen, S. Ilic, N. Navab
A User-Centered and Workflow-Aware Unified Display for the Operating Room
MICCAI Workshop on Modeling and Monitoring of Computer Assisted Interventions (M2CAI), Nice, France, October 2012 (bib)
2011
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. Lieberknecht, A. Huber, S. Ilic, S. Benhimane
RGB-D Camera-Based Parallel Tracking and Meshing
The 10th IEEE and ACM International Symposium on Mixed and Augmented Reality, Basel, Switzerland, Oct. 26 - 29, 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)
B. Diotte, C. Cagniart, S. Ilic
Markerless Motion Capture in the Operating Room.
Technical Report, Technische Universität München, München, Germany, Mai 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)
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)
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)
B. Drost, Markus Ulrich, N. Navab, S. Ilic
Model Globally, Match Locally: Efficient and Robust 3D Object Recognition
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)
2009
A. Ladikos, S. Ilic, N. Navab
Spectral Camera Clustering
ICCV Workshop on Emergent Issues in Large Amounts of Visual Data, Kyoto, Japan, Oct 4 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)
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)
2008
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)

Earlier publications

The list of my previous publications is listed here and majority of them can be downloaded from the publication page of CVLAB, EPFL.

  • Grujic, N., Ilic, S., Lepetit, V., Fua, P. 3D Facial Pose Estimation by Image Retrieval. In Automatic Face and Gesture Recognition (FG) (Amsterdam,Netherlands, September, 2008)

  • Ilic, S. Comparing Timoshenko Beam to Energy Beam for Fitting Noisy Data. In Asian Converence in Computer Vision (ACCV) (Tokyo, Japan, November, 2007)

  • Ilic, S., Fua, P. Non-Linear Beam Model for Tracking Large Deformations. In IEEE International Conference on Computer Vision (ICCV) (Rio de Janeiro, Oct 2007)

  • Ilic, S. Using Subdivision Surfaces for 3-D Reconstruction from Noisy Data. In DEFORM workshop in conjunction with British Machine Vision Conference BMVC (Edinburgh, Sep 2006).

  • Ilic, S., Salzmann, M., Fua, P. Implicit Mesh Make for Better Silhouettes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (San Diego, CA, June 2005).

  • Salzmann, M., Ilic, S., Fua, P. Physically Valid Shape Parameterization for Monocular 3{D Deformable Surface Tracking. In British Machine Vision Conference BMVC (Oxford, UK, September 2005).

  • Dimitrijevic, M., Ilic, S., Fua, P. Accurate Face Models from Uncalibrated and Ill-Lit Video Sequences. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Washington, DC, June 2004).

  • Ilic, S., Fua, P. Implicit Mesh Models for Modeling and Tracking. In IEEE conference on Computer Vision and Pattern Recognition (CVPR) (Madison,WI, June 2003).

  • Ilic, S., Fua, P. From Explicit to Implicit Surfaces for Visualization, Animation and Modeling. In ISPRS workshop on Visualization and Animation of Reality based 3D Models (Vulpera, Switzerland, February 2003).

  • Ilic, S., Fua, P. Generic Deformable Implicit Mesh Models for Automated Reconstruction. In International Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis in conjunction with ICCV (Nice, France, October 2003).

  • Ilic, S., Fua, P. Using Dirichlet Free Form Deformation to Fit Deformable Models to Noisy 3-D Data. In European Conference on Computer Vision ECCV(Copenhagen, Denmark, May 2002).


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Title: Dr.
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Firstname: Slobodan
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Lastname: Ilic
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Languages: English, German, French, Serbian
Groups: Computer Vision
Expertise: Computer Vision
Position: Scientific Staff
Status: Alumni
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Telephone: +49 89 289 17082
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