1 / 17

Multiresolution stereo image matching using complex wavelets

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.

kasimir-buckley
Download Presentation

Multiresolution stereo image matching using complex wavelets

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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!

More Related