Deliverable 2.2: Demonstrator on Multiscale Motion Estimation Florian Becker, Jing Yuan, Christoph Schnörr CVGPR group, University of Mannheim.

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Presentation transcript:

Deliverable 2.2: Demonstrator on Multiscale Motion Estimation Florian Becker, Jing Yuan, Christoph Schnörr CVGPR group, University of Mannheim

Multiscale Filter Library Implementation:ANSI C Interface: C/C++ and Matlab (MEX) Lowpass-filter: binomial filters Resampling: spline interpolation (degree 2 to 5) Scaling factor: selectable

Multiscale Motion Estimation Demonstrator 2.2 Implementation: Matlab Multiscale framework for motion estimation: dyadic image pyramid → multiscale filter library underlying singlescale motion estimator → Lucas/Kanade image warping → spline interpolation warp rescaling → spline interpolation evaluation: synthetic PIV data

Spline Interpolation: 360° Rotation in 23 Steps original cubic spline bicubic bilinear

Spline Interpolation: 10 x Zoom bilinear cubic spline

↓↓ ↑ ↓↓ ↑ W W E+ scale down scale up warp image estimate warp join warps ← next coarser levelnext finer level →

Regularisation of Local Flow Estimation Replace data term in variational approaches M and d: from multiscale Lucas/Kanade estimator Definition: compact ASCII file format for data term Example: Horn/Schunck with replaced data term

PIV image New Data Term confidential measurement local estimation

Horn/Schunck with New Data Term λ=0 λ=10 -4 λ=10 -2