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Chapter 8

Chapter 8. Documented applications of TRS and affine moment invariants. Character/digit/symbol recognition Recognition of aircraft and ship silhouettes (also from non-perpendicular views) Recognition of components on an assembly belt

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Chapter 8

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  1. Chapter 8

  2. Documented applications of TRS and affine moment invariants • Character/digit/symbol recognition • Recognition of aircraft and ship silhouettes (also from non-perpendicular views) • Recognition of components on an assembly belt • Recognition of biological shapes – algae, fishes, whales, ... • Landmark recognition in robotics • Image registration (medical, satellite, aerial, ...) • Normalization of database images, retriaval • Motion flow estimation • Digital watermarking

  3. Recognition of circular landmarks Measurement of scoliosis progress during pregnancy

  4. The goal: to detect the landmark centers The method: template matching by invariants

  5. Recognition of distorted landmarks

  6. Landmark clusters in the space of the AMI’s

  7. Satellite image registration Landsat TM SPOT

  8. Registration algorithm • Independent segmentation of both images • Extraction of salient regions • Calculating AMI’s • Finding three most stable pairs in the AMI space • Calculating the primal affine transform parameters • Transforming the SPOT regions over the Landsat • Finding matching regions by minimum distance in the image plane (10 found altogether). Region centroids serve as final control points • Calculating the final affine transform parameters by a least-square fit • Resampling and transformation of the SPOT image

  9. Segmentation

  10. Selected regions

  11. Matched region pairs

  12. Matched region pairs • Three most stable pairs found in the AMI space (the labels in circles) • The other matching regions found by minimum distance in the image plane

  13. Registered and superimposed images

  14. Optical flow estimation Traditional method A method based on Zernike moments.Note fewer artifacts.

  15. Image retrieval Moment invariants can be used as features for content-based image retrieval, particularly in case of simple 2D objects.

  16. Digital watermarking by moments The image with an invisible watermark based on rotation invariants. The host image

  17. Documented applications of convolution and combined invariants • Character/digit/symbol recognition in the presence of camera shake or other blurs • Robust image registration (medical, satellite) • Camera position estimation through registration • Multichannel deconvolution and superresolution • Detection of image forgeries • Focus/blur measurement

  18. Camera position estimation through registration Photo at the initial position (sharp) Photo at the current position, unknown shift and rotation (blurred background because of the object in the foreground)

  19. Position estimation algorithm • Independent corner detection in both images • Extraction of salient corner points • Calculating blur-rotation invariants of a circular neighborhood of each extracted corner • Matching corners by the invariants (14 matches found) • Estimating the relative between-image shift and rotation by a least-square fit

  20. Matched corners

  21. Multichannel blind deconvolution For MBD, robust registration of the input blurred frames is required.

  22. MBD of long-exposure images The Poor Fisherman, Paul Gauguin, 1896

  23. Detecting image forgeries • Copy-move forgery (clone of a region from the same image) • The cloned region is often intentionally blurred to make its detection difficult • Dividing the image into blocks, calculating blur invariants and looking for blocks having the same invariants • Presence of identical blocks indicates cloning forgery. “Blind” detection without having the original.

  24. Detecting image forgeries original fake duplicated regions

  25. Recent world-famous photo of Iranian missiles

  26. Duplicated regions indicate that the picture was manipulated

  27. Moment-based focus measure • Odd-order moments blur invariants • Even-order moments blur/focus measure If M(g1) > M(g2)  g2 is less blurred (more focused)

  28. Usage of a focus measure • Global measurement – ordering a set of images, which differ from each other by a degree of blur, according to their quality. Typically in astronomy.

  29. Images of different level of blur

  30. Sunspots – blurring by atmospheric turbulence

  31. Saturn images – intentional out-of-focus blur

  32. Usage of a focus measure • Global measurement – ordering a set of images, which differ from each other by a degree of blur, according to their quality. Typically in astronomy. • The moments perform very well in the above cases because of their robustness to noise.

  33. Usage of a focus measure • Local measurement – selecting the frame in which a certain small region is sharp/least defocussed. Typically in multifocal image fusion.

  34. Multifocus fusion based on a localblur measurement

  35. Usage of a focus measure • Local measurement – selecting the frame in which a certain small region is sharp/least defocussed. Typically in multifocal image fusion. • The moments are worse than wavelets and Laplacian because of their global character.

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