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Computer Vision for Interactive Computer Graphics

Computer Vision for Interactive Computer Graphics. Mrudang Rawal. Introduction. Human-computer interaction Computers interpret user movements, gestures and glances via fundamental visual algorithms. Visual algorithms: tracking, shape recognition and motion analysis

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Computer Vision for Interactive Computer Graphics

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  1. Computer Vision for Interactive Computer Graphics Mrudang Rawal

  2. Introduction • Human-computer interaction • Computers interpret user movements, gestures and glances via fundamental visual algorithms. • Visual algorithms: tracking, shape recognition and motion analysis • Interactive apps : response time is fast, algorithms work for different subject and environment, and economical.

  3. Tracking Objects • Interactive applications track objects – large and small • Different methods and techniques used.

  4. Large Object Tracking • Large objects like hand or body tracked. • Object is in front of camera. • Image properties (Image moments), and artificial retina chip do the trick.

  5. Step 1: Shape recognition • Training and Testing of object. • Technique = Orientation Histogram • Set of each shape oriented in possible direction. • Match current shape orientation with the ones in the set.

  6. Step 2: Shape recognition • Optical flow: sense movements & gestures • Frequency of alternation of horizontal and vertical velocity (frame avgs) used to determine gestures. • Fast Flow Optical algorithm: • Temporal difference, current – previous frame • If pixel temporal diff != 0  if -ve motion towards adj pixel with greater luminance in current frame  if +ve towards lower luminance in current frame • Apply the 1-d direction estimation rules to four orientations at each pixel • Average out motion estimates at each pixel, then average flow estimate compared to its neighboring 8 pixels

  7. Small Object Tracking • Large objects tracking techniques not adequate. • Track small objects through template based technique – normalized correlation

  8. Normalized Correlation • Examine the fit of an object template to every position in the analyzed image. • The Location of maximum correlation gives the position of the candidate hand. • The value of that correlation indicates how likely the image region is to be a hand.

  9. Example : Television Remote • To turn on the television, the user holds up his hand. • A graphical hand icon with sliders and buttons appears on the graphics display. • Move hand to control the hand icon

  10. Conclusion • Simple vision algorithms with restrictive interactivity allows human-computer interaction possible. • Advances in algorithms and availability of low-cost hardware will make interactive human-computer interactions possible in everyday life.

  11. References [1] R. Bajcsy. Active perception. IEEE Proceedings, 76(8):996-1006, 1988. [2] A. Blake and M. Isard. 3D position, attitude and shape input using video tracking of hands and lips. In Proc. SIGGRAPH 94,pages 185{192, 1994. In Computer Graphics, Annual Conference Series. [3] T. Darrell, P. Maes, B. Blumberg, and A. P.Pentland. Situated vision and behavior for interactive environments. Technical Report 261, M.I.T. Media Laboratory, Perceptual Computing Group, 20 Ames St., Cambridge, MA 02139, 1994. [4] I. Essa, editor. International Workshop on Automatic Face- and Gesture- Recognition.IEEE Computer Society, Killington, Vermont, 1997. [5] W. T. Freeman and M. Roth. Orientation histograms for hand gesture recognition. In M. Bichsel, editor, Intl. Workshop on automatic face and gesture-recognition, Zurich, Switzerland, 1995. Dept. of Computer Science, University of Zurich, CH-8057. [6] W. T. Freeman and C. Weissman. Television control by hand gestures. In M. Bichsel, editor, Intl. Workshop on automatic face and gesture recognition, Zurich, Switzerland, 1995. Dept. of Computer Science, University of Zurich, CH-8057.

  12. [7] B. K. P. Horn. Robot vision. MIT Press,1986. [8] M. Krueger. Articial Reality. Addison-Wesley, 1983. [9] K. Kyuma, E. Lange, J. Ohta, A. Hermanns,B. Banish, and M. Oita. Nature, 372(197),1994. [10] R. K. McConnell. Method of and apparatus for pattern recognition. U. S. Patent No.4,567,610, Jan. 1986.

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