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Epitome

Spring 2004, CS7636 Computational Perception. Epitome. Ji Soo Yi and Woo Young Kim Instructor: Prof. James Rehg April 27, 2004. CONTENTS. Introduction Epitomic Image Experiment Results & Conclusion Future direction. Edited by Woo Young and Ji Soo. Introduction(1).

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Epitome

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  1. Spring 2004, CS7636 Computational Perception Epitome Ji Soo Yi and Woo Young Kim Instructor: Prof. James Rehg April 27, 2004.

  2. CONTENTS • Introduction • Epitomic Image • Experiment • Results & Conclusion • Future direction Edited by Woo Young and Ji Soo

  3. Introduction(1) Image representative model • Feature-based • Geometric approach • Template-based • Standard Euclidian error norms • Eigen spaces • Color histogram-based Edited by Woo Young and Ji Soo

  4. Introduction(2) • Epitomic image analysis • What is Epitome? • The miniature, condensed version of image. • Still consists of most constitutive elements. • Use a probabilistic measure of similarities. • Shape epitome and appearance epitome. Edited by Woo Young and Ji Soo

  5. Em Es S2 S1 M I Introduction(3) • Epitomic image analysis • Graphical model of epitomic analysis shape epitome appearance epitome I=M*S1+(1-M)*S2 + noise Edited by Woo Young and Ji Soo

  6. Introduction(4) • Epitomic image analysis • Probabilistic framework Input image X epitome e = (,) Tk Patch Zk = {zi,k}, zi,k= xi Tn Me, Ne Patch Zn M,N Edited by Woo Young and Ji Soo

  7. Introduction(5) • Epitomic image analysis • EM algorithm to extract an epitomic image E step: M step: Edited by Woo Young and Ji Soo

  8. Epitomic Image (1) Epitomic image Original image Edited by Woo Young and Ji Soo

  9. Epitomic Image (2) Input image Epitomic image Edited by Woo Young and Ji Soo

  10. Experiment (1) • Epitomic Modeling • Face Detection • Comparison with PCA Analysis Edited by Woo Young and Ji Soo

  11. Experiment (2) Epitomic Modeling Training data – a set of face images Each image : 100 by 75Epitomic image: 32 by 32 Epitomic image Edited by Woo Young and Ji Soo

  12. Experiment (3) Epitomic Modeling Training data – a synthetic image by tiling face images 75 by 75 pixels 100 by 75 pixels for each image 1000 by 375 pixels for total Edited by Woo Young and Ji Soo

  13. Experiment (4) Face Detection Histogram and clustering Edited by Woo Young and Ji Soo

  14. Experiment(5) Face Detection Patch matching – face image High log likelihood – good match Low log likelihood - poor match Edited by Woo Young and Ji Soo

  15. Experiment(6) Face Detection Patch matching – non face image Low log likelihood – good match High log likelihood - poor match Edited by Woo Young and Ji Soo

  16. Experiment(7) Comparison with PCA analysis – PCA Rigid data Non-Rigid data Edited by Woo Young and Ji Soo

  17. Experiment(8) Comparison with PCA analysis – Epitome Rigid data Non-Rigid data Edited by Woo Young and Ji Soo

  18. Results & Conclusion • Epitomic image modeling • Parameter settings • Comparison with PCA Analysis • Statistics Edited by Woo Young and Ji Soo

  19. Future direction • Computational time saving • Shape epitome • Other applications Edited by Woo Young and Ji Soo

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