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Presentation by Leif Arne Rønningen, Item, NTNU, Autumn 2008

TTM4142 Networked Multimedia Systems Stereo Analyses by Hybrid Recursive Matching (HRM) for Real-Time Immersive Video Conferencing. N Atzpadin, P Kauff, O Schreer IEEE Transactions on circuits and systems for video technology Vol. 14, no 3, March 2004.

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Presentation by Leif Arne Rønningen, Item, NTNU, Autumn 2008

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  1. TTM4142 Networked Multimedia SystemsStereo Analyses by Hybrid Recursive Matching (HRM) for Real-Time Immersive Video Conferencing N Atzpadin, P Kauff, O Schreer IEEE Transactions on circuits and systems for video technology Vol. 14, no 3, March 2004 Presentation by Leif Arne Rønningen, Item, NTNU, Autumn 2008

  2. Stereo correspondence search by HRM. The output disparity maps give the pixel correspondences of the left and right images

  3. Two-camera model, rectification

  4. The HRM algorithm - outline

  5. HRM outline behaviour • For the current image block posisiton do: • The selection-of-start-vector function evaluates three candidate vectors for the current block posisiton, and outputs the vector with the smallest DBD (displaced block difference) as the start vector for pixel-recursion • The pixel-recursion function outpus an update vector with the smallest DPD (displaced pixel difference) • The selection-of-final-vector function selects as the final vector the one with the smallest DBD

  6. Block Recursion • The three candidate vectors are calculated using information of both the previous image and the spatial neighborhood • Spatial candidate vectors are found using meander scan <link> • Spatial candidates in the right image are equally distributed around the considered pixel in the left and right image • A temporal candidate vector is taken from the same position in the previous frame <link>

  7. Meander scan for arbitrarily shaped video objects Even frames Odd frames

  8. Shape driven displaced block difference - DBD

  9. Pixel RecursionDisplaced pixel difference - DPD

  10. Outline of pixel recursion

  11. Mean absolute difference between candidate and final output vector

  12. Frequency of identity between candidat and final output vector

  13. Horizontal Vertical Spatial distribution of squared difference between final output vector and candidate

  14. Temporal Update Spatial distribution of squared difference between final output vector and candidate

  15. HRM applied to left and right images Left-to-right Right-to-left disparities disparities

  16. Result of the consistency check (unreliable disparities are marked as black).

  17. Synthesized views (a) without postprocessing, (b) with consistency check and simple interpolation, and (c) with segmentation based postprocessing.

  18. Zoom of the critical hand area in synthesized views from previous Figure (a) without postprocessing, (b) with consistency check and simple interpolation, and (c) with segmentation based postprocessing.

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