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Research Results & Plans

Research Results & Plans. Nojun Kwak nojunk@ieee.org. Research Areas: Overview. Pattern Recognition Feature selection / extraction algorithms Face recognition / Image analysis Neural networks Machine learning Data Mining Robotics Control of robot manipulators

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Research Results & Plans

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  1. Research Results & Plans Nojun Kwak nojunk@ieee.org

  2. Research Areas: Overview • Pattern Recognition • Feature selection / extraction algorithms • Face recognition / Image analysis • Neural networks • Machine learning • Data Mining • Robotics • Control of robot manipulators • Implementation of Biped Robot and Control • Home Network Middleware • UPnP / OSGi / IEEE1394 device driver • Home Robot • Wireless communication protocol • WCDMA protocol • LTE (long term evolution) system Nojun Kwak

  3. Feature selection & extraction

  4. What kind of sensors should we use? How to collect data? Collect data How do we know what features to select, and how do we select them …? (Transforms, PCA, LDA, ICA … ) Choose features What type of classifier / neural network shall we use? Is there best classifier …? Choose model Train system How do we train? How do we evaluate our performance? Validate the results? Confidence in decision? Evaluate system Issues in Pattern Recognition from Robi Polikar’s tutorial at ICANN2006 Nojun Kwak

  5. Feature Selection & Extraction • Solve a pattern recog. problem with N features. • Is it possible to do the same job with fewer (M<N) input features?  Feature Selection & Extraction • Advantages of feature selection & extraction • Simpler structure of PR (classifier/regression) system • Fewer data for training • Shorter time of training • (possible) Better performance - Occam’s razor Original Dataset N features Feature Selection/Extraction New Dataset M features Nojun Kwak

  6. Various criteria on goodness of features • Comparing PR (classification) performance • Slow, Slow, Slow … • Correlation (with target/class) • How to cope with non-linearity? (e.g. f = t^2) • Mutual Information (Advantages over others) • Maximize the mutual information between target/class and the feature I(C;F). Nojun Kwak

  7. Methods • Feature Selection  TMFS, MIFS-U, PWFS • Feature Extraction  ICA-FX, PWFX • TMFS (Taguchi Method for Feature Selection) • MIFS-U (Mutual Information Feature Selector under Uniform Information Assumption) • PWFS (Parzen Window Feature Selector) • ICA-FX (Feature Extraction with Independent Component Analysis) • PWFX (Parzen Window Feature Extractor) Showed good performances Nojun Kwak

  8. Feature selection methods

  9. TMFS: Main idea • Taguchi method (Orthogonal Arrays) • Measure = actual performance of a classifier. • Design of Experiment (DoE): reduces the no. of experiments Nojun Kwak

  10. H(f) I(f;s) 1 H(s) I(f;C) 4 3 2 H(C) I(s;C) MIFS-U: Main idea • Ideal Greedy • Maximizes area 2+3+4 = I(f,s;C) • Area 2+4 is common for all features • Maximizes area 3 = I(f;C|s) Nojun Kwak

  11. PWFS: Main idea • Parzen Window: Approximation of PDF with finite number of samples. • Numerical method for computing I(C;F). p(f) x x x x x x x f Nojun Kwak

  12. Feature extraction methods

  13. Previous Works • Linear subspace methods • PCA (Principal Component Analysis) • ICA (Independent Component Analysis) • LDA (Linear Discriminant Analysis) • Nonlinear methods • Kernel methods (K-PCA, K-LDA) • Hidden neurons of MLP • SVM (Support Vector Machine) PWFX, ICA-FX Nojun Kwak

  14. PWFX: Main idea • Almost the same as PWFS • Maximize mutual information between new feature f = wx and class C.  argmax I(wx;C) • Direct calculation of mutual information. • Mutual information is approximated by Parzen window density estimation. • Stochastic gradient ascent in finding w. w Nojun Kwak

  15. ICA-FX: ICA preliminary (I) • Purpose • From the observations, estimate the sources. • Assumptions • Sources are mutually independent. • Observations X are linear combinations of S. • Mixing matrix A is invertible.  find W = A Sources S (S1,…,SN) Observations X (X1,…,XN) Estimates U (U1,…UN) A W X = AS How to U=S? -1 Nojun Kwak

  16. W g1( ) y1 x1 u1 g2( ) x2 u2 y2 g3( ) x3 u3 y3 gN( ) xN uN yN ICA-FX: ICA preliminary (II) • Structure of ICA learning • gi(.) : assumed to be cumulative density of ui Nojun Kwak

  17. ICA-FX: Main idea (I) • ICA shows good performance in finding independent components utilizing higher order statistics. • However, it does not fit to supervised learning because it does not utilize class/target information.  Include class/target information in the learning (make use of the characteristics of supervised learning) Include target information as an input for ICA Nojun Kwak

  18. ICA-FX: Main idea (II) • Feature Extraction Problem for classification (FX problem) • : N zero-mean normalized input features • : class information, Nc – no. of classes • Find new features from x containing maximal information about the class c. • Equality holds if fbis independent of c. • Interpret FX problem in the structure of ICA.  Find that maximize , where Nojun Kwak

  19. ICA-FX: Structure (I) Normal ICA • is a feature vector among which will be used as important features.  If fb is independent of c. = [ ] Nojun Kwak

  20. ICA-FX: Structure (II) fa = [f1, … , fM] fb = [fM+1, … , fN] = [uM+1, … , uN] c = [uN+1, … , uN+Nc]: independent of fb fa max. info on c fb c Indep. Nojun Kwak

  21. ICA-FX: Learning algorithm (ML) • Log likelihood • Gradient ascent Natural Gradient Nojun Kwak

  22. Training Image f3 d2 John d1 d3 Tom f2 New Image d1 < min (d2,d3)  good features f1 ICA-FX: Results (I) • Procedures • Classification • 1-NN Nojun Kwak

  23. Yale data (165 images, 15 persons) ICA-FX: Results (II) • AT&T data (400 images, 40 persons) Nojun Kwak

  24. Surface Defect Detect (POSCO)

  25. Data (Surface Defect) Scratch Dirt Nojun Kwak

  26. Procedure (Wavelet) & Results Surface Image Preprocessing (LPF & Segmentation) Adaptive Wavelet Packet Neural Networks (MLP/RBF) Classification Feature Extraction - hr: row entropy - hc: column entropy - h: total entropy - ratio: hr/hc Nojun Kwak

  27. Rail-car Inspection (UIUC)

  28. Rail-car Inspection: Methods • Template matching technique • Segmentation  Edge detection  Template matching  Thresholding Nojun Kwak

  29. Rail-car Inspection: Results Nojun Kwak

  30. Control of Robot Manipulators

  31. Motivation • Characteristics of robot system • Dynamics: • Nonlinear, modeling uncertainties • Hard to get exact dynamics. • Conventional control methods • Neural networks • slow in parameter updating • Robust control (feedback-linearization approach) • strict bounds on modeling error and disturbance • Fast but robust to poor parameter estimation • MBDA (Model Based Disturbance Attenuation) Nojun Kwak

  32. + + K + + P - - K + 1 + - K + 2 + M + Structure of MBDA : Plant and Model : Position vector of plant, model, and desired position : Input torque for plant and model : D gain : PD gains, Nojun Kwak

  33. Performance on Real Biped Robot • Initial & final positions  Got almost the same results as simulation. Nojun Kwak

  34. Biped Robot: Simulation & Implementation

  35. Biped Robot • Presented at the 1st Brain Science Conference. (http://bsrc.kaist.ac.kr/braintech/image/reports/1-year/HG0204A02/HG0204A02-02.html) • To enable stable walking of a biped robot, target trajectory is needed • Simulation modeling (Trajectory generation) Nojun Kwak

  36. Robot Model Simulator • 12 DoF (degree of freedom) • 2 in ankle • 1 in knee • 3 in hip • Trajectory generation & 3-D simulation (OpenGL) Nojun Kwak

  37. Nojun Kwak

  38. Implementation of Biped Robots • Supervised senior graduation project (1st Semester, 2000) • Structure : leg robot     - Height : 25cm • DOF : 10 DOF     - Ankle : 2 (*2)     - Knee : 1 (*2)     - Hip : 2 (*2) • Actuator : Servo motor (HS-615) • Motor controller : 80c196kc (*2) • Motion : PC, serial interface • Walking : static walking Nojun Kwak

  39. Brain Science Project (2000~2001) • Structure : leg robot     - Height : 50cm • DOF : 10 DOF     - Ankle : 2 (*2)     - Knee : 1 (*2)     - Hip : 2 (*2) • Actuator : DC-micro motor (minimotor 2224) • Motor controller : PC-based control • Interface : PCI-8136 board • PC OS : QNX • Controller programming : PICARD • Walking : static walking Nojun Kwak

  40. Nojun Kwak

  41. Works in Samsung

  42. MagicGate: Linux based Home Gateway • MG = Set-top box + Internet Gateway + Home server • Development of Linux 1394 device driver • Development of UPnP bundle for OSGi (Java) • Home Robot (Unified remocon + Surveillance camera) light Serial/LAN OLT ONU TV1 PLC Server AV cable Gas Valve Internet FTTH TV2 MG1 (PVR/DVD) Switch WLAN MG2 VOD server etc xDSL/E-net EOD server Terrestrial/ Cable/ Satellite CAM 1394 WLAN Portal serv. Windows Unified Remocon (Home Robot) E-net / WLAN E-Mail serv. PC CAM Nojun Kwak

  43. 3GPP (WCDMA) Standardization • 3GPP (3rd Generation Partnership Project) • Standardization body for WCDMA (European Wireless Com. Network) • 2004.4. ~ 2006. 8: Served as a 3GPP RAN WG3 main delegate for Samsung • Main research area • MBMS (Multimedia Broadcast & Multicast Service) • EUDCH (Enhanced Uplink Dedicated Channel) • LTE (Long-Term Evolution) • Output • +60 Submitted Patents • +30 Contributions Nojun Kwak

  44. Current Works (2006~2007)

  45. L1- Biased Discriminant Analysis • BDA is better suited for detection and verification problems. • Positive / Negative problem • Gaussian distribution assumption only on positive examples. • Has just developed L1-BDA. • Utilizes L1 measure instead of L2 measure. • Robust to outliers. • Applications: image registration, face detection … ETH80 database Nojun Kwak

  46. Eye detection & localization • Basic tool for face detection in arbitrary images • Can also be utilized for pose estimation (e.g. with nose or mouse detection) • Real-time automatic eye detection system by using Haar-like features Positive examples Negative examples Extracted features (AdaBoost) 7 level Image pyramid (on FERET database) Nojun Kwak

  47. Pose estimation • Previous works: illumination compensation Excerpt from “Shadow compensation in 2D images for face recognition”, Choi et. al, Pattern Recognition, accepted for publication 2007. • Future works: Pose estimation in combination with illumination compensation  better classification performances expected Nojun Kwak

  48. Future Works (2007 ~ )

  49. Enhancement to Linear Feature Extractors • Weighted samples • Each samples are treated differently. • E.g. by using posterior class probability • Suppression of outliers • E.g. by adapting shaping functions for target • Ensemble feature extractor • Via Bagging / Boosting techniques • Different assumption of input distribution on different classes Will be started soon … Nojun Kwak

  50. Feature Extraction Face Detection Classification (Full) Face Recognition System (I) Face Recognition Nojun Kwak

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