1. P ROJECT EXPERIENCES Donghun Kim Electrical and Computer Engineering Purdue University

2. A BRIEF HISTORY 2001 : Stereo Vision-based Control Algorithm for docking a nonholonomic mobile robot (M.S. Thesis) 2001~2006 Spring : Researcher for the Fingerprint Pattern Recognition Algorithm in the company, NITGen Co. Ltd. 2007 Fall : Line Tracking with Stereo Camera for Assembly-on-the-fly (Involved in the Image-based Visual Servoing) 2008 Human Modeling and Tracking in Multiple Camera Networks (2008) 3D Skeleton-based Human modeling using implicit surfaces (Metaballs) Shape Complexity of occluding contours using the Entropy of Multi-scales Curvature Distribution and the Contour Bending Energy 2008 Fall~ Present: Appearance-based 3D Rigid Object Tracking Approaches: Direct Image Alignment and Appearance-based Feature Matching 2

3. S TEREO V ISION - BASED C ONTROL A LGORITHM FOR DOCKING A NONHOLONOMIC MOBILE ROBOT (2001) [Kist Autonomous Robotic Assistant] [Controller Architecture] 3

4. K INEMATIC MODEL & VISUAL DOCKING RESULTS 4

5. F INGERPRINT PATTERN RECOGNITION (2001~2006) Research of pattern recognition in biometrics such as fingerprint, face and iris etc. Support to develop the Automatic Fingerprint Identification System for the Public Prosecutors Office Involved in the part of a Fingerprint Pattern Recognition Algorithm Development of the Algorithm Development Tool Feature Extraction Geometric Point Pattern Matching PCA-based Search Algorithm to indentify in a large database 5

6. A S IMPLE P ROCEDURE Feature patterns 6

7. E XTRACTION AND M ATCHING 7

8. H UMAN M ODELING AND T RACKING IN M ULTIPLE C AMERA N ETWORKS I (2008) Skeleton-based 3D parameterized human modeling using Implicit Surfaces (Metaballs) Develop a Modeling Tool using OpenGL & FLTK (Fast Light Tool Kits) Parameterized 3D Skeleton Model 3D Skeleton-based Human Model with Metaballs Rendering by Marching Cube Algorithm 8

9. A M ODELING P ROCEDURE 9

10. M ODELING T OOL - Made by Donghun Kim 10

11. M ODELING T OOL - Made by Donghun Kim 11

12. H UMAN M ODELING AND T RACKING IN M ULTIPLE C AMERA N ETWORKS II (2008) Shape complexity Analysis for Silhouette images of a human pose to find the principal view in multiple cameras system Using two criteria: The Entropy of a Multi-resolution Curvature Distribution(EMCD) Contour Bending Energy (CBE) 12

13. R ESULT FOR SIMPLE PATTERNS The most complex pattern 13

14. R ESULT OF MULTIPLE VIEWS FOR A HUMAN POSE 14

15. A PPEARANCE - BASED 3D R IGID O BJECT T RACKING (2008 F ALL ~ P RESENT ) Model-based 3D Object Tracking Approaches For Pose Initialization: Local Appearance Features (SIFT) based matching (3D model – 2D input image) For Pose Refinement: Direct Image Alignment considering appearance variation Use the Normalization Inverse Compositional Image Alignment approach Interests Manifold Learning, Online Learning Non-rigid object Tracking 15

16. 3D O BJECT T RACKING 16

17. D IRECT I MAGE A LIGNMENT A PPROACH 17

18. L OCAL A PPEARANCE F EATURE - BASED M ATCHING A PPROACH 18

19. Thank you. 19