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Multiresolution stereo image matching using complex wavelets

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Multiresolution stereo image matching using complex wavelets. Julian Magarey CRC for Sensor Signal and Information Processing. Anthony Dick Dept of Computer Science University of Adelaide. Stereo Vision Problem. A Stereo Pair. AIM: To recover 3D shape from stereo pair. Stereo Matching.

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Multiresolution stereo image matching using complex wavelets

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  1. Multiresolution stereo image matching using complex wavelets Julian Magarey CRC for Sensor Signal and Information Processing Anthony Dick Dept of Computer Science University of Adelaide

  2. Stereo Vision Problem

  3. A Stereo Pair AIM: To recover 3D shape from stereo pair

  4. Stereo Matching • Find a point in each image which represents the same point in the scene • corresponding points corresponding points disparity

  5. Feature Based Matching • Detect and match distinctive features • Problems • featureless areas • occluded features • same feature may appear different

  6. Multiresolution Matching • Match points at several levels of detail Left Image MATCH MATCH MATCH FINE COARSE Right Image

  7. Wavelet Transform Original Image Resolution i,j Level 1 Res i/2, j/2 Level 2 Res i/4, j/4

  8. Multiresolution Matching • Now have multiresolution representation • Level m similarity distance measure: where x is a pixel in the level m representation of the left image x’ is a pixel in the level m representation of the right image

  9. Similarity distance surface • Can extrapolate similarity surface about x’ where the surface minimum, the location of the surface minimum, a 2x2 curvature matrix, are derived from

  10. Stereo Matching Algorithm • Now have basic matching algorithm • perform wavelet transform on images • minimise SD(x,x’) for all x at top level • use as starting point for finer level matching • What if top level match is wrong? • How do we interpolate matches to finer level?

  11. Coping with Mismatches • Find a field of disparity vectors which minimises where is a directed measure of the difference between {u} and the unsmoothed disparity field is a measure of the uniformity of {u} is a scalar controlling their relative influence

  12. Regularisation Features • Based on Anandan [IJCV, 1989] • Use curvature matrix κ to smooth more in directions of less certainty Smooth more in this direction Smooth less in this direction Similarity surface contours

  13. Coarse-to-fine interpolation • Robust disparity interpolation A B a b c d C D = level m pixel = level m+1 pixel D(a) = choice of {D(A), D(B), D(C), D(D)} which minimises similarity distance

  14. Results • Calibrated camera setup • Projective reconstruction • Form textured VRML surface 3D Surface C1 C2 Right Camera Left Camera

  15. Results

  16. Results

  17. Conclusion • Future work • lighting • geometric constraint incorporation • colour images • camera self-calibration • more than two images • Already, results are promising!

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