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A R obust A lgorithm F or M easuring T ie P oints O n T he B lock O f A erial I mages

IX th International Scientific and Technical Conference From Imagery to Map: Digital Photogrammetric Technologies. A R obust A lgorithm F or M easuring T ie P oints O n T he B lock O f A erial I mages. Andrey Sechin Scientific Director RACURS Alexey Chernyavskiy

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A R obust A lgorithm F or M easuring T ie P oints O n T he B lock O f A erial I mages

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  1. IXth International Scientific and Technical Conference From Imagery to Map: Digital Photogrammetric Technologies A Robust Algorithm For Measuring Tie Points On The Block Of Aerial Images Andrey Sechin Scientific Director RACURS AlexeyChernyavskiy AlexanderVelizhev AntonYakubenko Graphics & Media Lab, MSU October 5–8, 2009, Attica, Greece

  2. Area based cross correlation

  3. Benefits and Drawbacks • + High subpixel accuracy - Needs a good initial guess • Works on smooth surfaces - Fails on periodic structures V.N. Adrov, A.D.Checkurin, A.Yu.Sechin, A.N.Smirnov, J-P. Adam-Guillaume, J-A. Qussette, Program PHOTOMOD: digital photogrammetry and stereoscopic images synthesis on a personal computer., Digital Photogrammetry and Remote Sensing ‘95, ISPRS Proceedings, Vol. 2646. Two formulae are equivalent Zheltov S. Y., Sibiryakov A. V. 1997. Adaptive Subpixel Cross-correlation in a Point Correspondence Problem. Optical 3-D Measurement Techniques IV, Wichmann Verlag, Heidelberg, pp.86-95.

  4. Segmentation/boundary correlation Segments matching similarity function W 2005 PHOTOMOD 4.0 M. Drakin, A.Elizarov, A. Sechin, A.Zelenskiy AUTOMATIC STEREO POINTS MEASUREMENTS USING TWO-DIMENSIONAL FEATURE EXTRACTION, Optical 3-D Measurement Techiques VIII, v I, p. 385-388, Zurich 2007. Correlation coefficientR -

  5. New approach – Detectors, Descriptors, RANSAC Introduction • N (N > 2) strips • Images are ordered inside strips • No information on strips ordering • The problem: find tie points with subpixel accuracy

  6. Universality • Algorithm should work with: • Any terrain type (buildings, fields, mountains, forests, …) • Digital, scanned, space imagenary • arbitrary overlap

  7. Detector • Reduce image resolution • Detector – find «corners» (1D features) on all images • We use classic corner detectors • We selectN (~1000) uniformly spaced best corners

  8. Descriptor(SIFT/SURF/DAISY….) • Calculate gradients in the neighborhood of 1D feature (corner) (gradients are invariant to lightness shift) • Select one (or a couple) of main gradient directions (invariance to rotations) • Calculate histograms of gradients (good neighbourhood desciption) • Normalize histograms (invarience to contast)

  9. Candidates for matching (1) • For all points A on the first image we select the nearest (with respect to descriptor) point A’ on the second image A’ A

  10. Candidates for matching (2) • For points A’on the second image we find the nearest point (with respect to descriptor) A” on the first image A’ A’’

  11. Candidates for matching (3) • If A and A’’coincide, the pair (A, A’) fits for the nest stage B A’ A, A’’ B’ B’’

  12. RANdom SAmpling Consensus (RANSAC, PROSAC,…) • N (iterations number) times repeate • Randomly select pairs. The number of seleted paires must be enough for model calculation (homography, fundamental matrix, relative orientation • Calculate the model for selected matches • Calculate errors for all possible pairs for the found model • Ellimination of “bad”matches (outliers)– that do not fit the threashould • Calculate the number of “good” matches (inliers) and RMS • Select the best model from all iterations • Refinу the model using inliers

  13. Example of found matches

  14. Speedup/Reliability increase Distance filtering (desriptors) Metric filtering Topological filtering Reinforcement matching

  15. Approximate overlap definition Consider all candidate pairs with approximately the same distance on both images. Angle voting. Shift voting with respect tox,y.

  16. Finding matches on many images 1 2 2 1 4 4 3 3 CRalgorithm (conflict resolution) Resolving conflicts and adding new matches

  17. Finding “features” on images with initial resolution/subpixel refinement Several “features” should be found in the neighborhood. Repeat algorithm on initial resolution, take into account all found restrictions

  18. Conclusion The algorithm is fast and reliable on reduced resolutions Calculation of detectors/discriptors/image overlap/RANSAC needs only several seconds of CPU time per image. CR algorithm needs some speedup (the solution is to split block into sub-blocks). To DO CR algorithm speedup. Speedup of the initial resolution part of the algorithm. Subpixel detectors (experiments to be performed).

  19. Thank you for attention!

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