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Enhanced Reality

Enhanced Reality. Jonah McBride Sheldon Provost. EN 193, S18: Image Processing Final Project Presentation – December 14, 2000. What Is Enhanced Reality?. ER vs. VR Display Tracking Level Of Interaction Location Orientation. Research In This Field. Very Little…

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Enhanced Reality

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  1. Enhanced Reality Jonah McBride Sheldon Provost EN 193, S18: Image Processing Final Project Presentation – December 14, 2000

  2. What Is Enhanced Reality? • ER vs. VR • Display • Tracking • Level Of Interaction • Location • Orientation

  3. Research In This Field • Very Little… • Azarbayejani, A. et al., 1993 – Head Tracking • One Camera • Use of Hessian Value to Find Key Points • Extended Kalman Filter to Determine 3D Motion • Faugeras, O., 1993 – 3D Computer Vision • Tracking • 3D Projections • Jähne & Haußecker, 2000 – Computer Vision • Optical Flow

  4. A Virtual Game of Tennis • Program written in Visual C++ • Goals For The Project • Computer Tracks The Movement Of The Racquet • Virtual Ball Will ‘Bounce’ Off Of The Racquet • Develop Fast and Robust Tracking Algorithms • Adapt and Combine Existing Research with Our Own Techniques

  5. Image Processing Element • Image Processing VS Programming • From 2 Images, Calculate: • X,Y,Z coordinates of the center of the racquet • 2 Additional Non-Linear Points to Determine the Plane of the Racquet • Programming: • Calculate Velocity of the Racquet • Calculate Trajectory of a Ball using simple principles of physics

  6. Methods • Segmentation • Tracking • Perspective Corrections • Screen Display

  7. Segmentation • Extract Key Points On The Racquet • Each point is a primary color • Segmentation Techniques • RGB Segments Camera: Front

  8. Tracking & Orientation • Identify the same key points from both views • Simulation: Matlab • Combine coordinates from both views to determine 3D coordinates • Front(x,z) + Side (y,z) -> 3D(x,y,z) Camera: Side

  9. Perspective Corrections • Camera Positions • Front View: along +y-axis • Side View: along +x-axis • 3D(x,y,z) -> Front(x,z) • Can’t assume the image is a 2D projection of 3D space • The 3D Coordinates of the Key Points Will Be Distorted • Perform corrections based on: • The Properties of the Camera’s Lens • Distance From the Camera • 3D Image Warping Function In 2 Directions

  10. Screen Display-Matlabized • Overlay an image of a tennis court on the screen • Calculate Trajectory of the Ball and Display it on the Screen • Changing the view of the court based on movement of the player

  11. Results • Make Tracking Faster and More Robust • Goal of 10 FPS • Use Perfect Matte, Primary Colors • Use Optical Flow to Estimate Next Position • Track Head Position of Player • Update View of Tennis Court

  12. References • A. Azarbayejani, T. Starner, B. Horowitz, A. Pentland, “Visually Controlled Graphics”, IEEE PAMI 15 (6), June 1993. • Faugeras, O. Three-Dimensional Computer Vision, MIT Press, 1993. • B. Jähne & H. Haußecker, Computer Vision and Applications, Academic Press, 2000.

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