Assessment of interest points detection algorithms in OTB

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Assessment of interest points detection algorithms in OTB
Otmane Lahlou; CS
Julien Michel; CS
Damien Pichard; CS
Jordi Inglada; CNES

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  • 1. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Assessment of interest point detection algorithms in OTB Otmane Lahlou1 , Julien Michel1 , Damien Pichard1 , Jordi Inglada2 1 C OMMUNICATIONS & S YSTÈMES 2 C ENTRE NATIONAL D ’ ÉTUDES SPATIALES IGARSS, July 12-17, 2009
  • 2. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Introduction Finding correspondences between images The dense approach: expensive but exhaustive The sparse approach: cheap, might be sufficient Interest points Characteristic locations with highly discriminant keys Robust: illumination, affine transform, noise . . . In Orfeo Toolbox Mainly SIFT and SURF Perfect framework for a validation and comparison chain IGARSS, July 12-17, 2009
  • 3. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Outline of the presentation Detectors in Orfeo Toolbox Validation chain Evaluation results Scene classification IGARSS, July 12-17, 2009
  • 4. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Outline Detectors in Orfeo Toolbox Validation chain Evaluation results Scene classification IGARSS, July 12-17, 2009
  • 5. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Keypoints detectors in OTB Scale Invariant Feature Transform (SIFT) Location: local extrema in scale space using DoG pyramids Key (128 values): local orientation histograms Implementation in OTB: Home-brewed version (not efficient) Wrapping of SiftFast (very fast and accurate) Speed-Up Robust Feature (SURF, variant of SIFT) Location: Laplacian approximation instead of DoG Key (64 values): local Haar wavelet response Implementation in OTB: contributed by CS IGARSS, July 12-17, 2009
  • 6. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Example of application using OTB Disparity map estimation based on sift matching IGARSS, July 12-17, 2009
  • 7. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Outline Detectors in Orfeo Toolbox Validation chain Evaluation results Scene classification IGARSS, July 12-17, 2009
  • 8. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Overall Scheme Smoothing Key point detection Input Key point Image Matching Affine Key point Smoothing Warping detection Standard interface: Detector can be either SIFT or SURF IGARSS, July 12-17, 2009
  • 9. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Parameters Translation, rotation and scale factor Image feature: band, intensity, NDVI, NDWI Amount of smoothing (anisotropic diffusion) Number of scales Matching distance Back-matching Tolerance for match validation IGARSS, July 12-17, 2009
  • 10. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion SIFT Matching 134/269 good matches, 0 bad match IGARSS, July 12-17, 2009
  • 11. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion SURF Matching 61/282 good matches, 1 bad match IGARSS, July 12-17, 2009
  • 12. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Outline Detectors in Orfeo Toolbox Validation chain Evaluation results Scene classification IGARSS, July 12-17, 2009
  • 13. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion SIFT scaling sensitivity Amplitude channel, rotation: 0, translation: (0,0), smoothing: no 1.6 SIFT 1 SIFT 2 True Matches False Matches 1.4 1.2 Scaling 1 IGARSS, July 12-17, 2009
  • 14. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion SURF scaling sensitivity SURF is more sensitive to scaling than SIFT 1.6 SURF 1 SURF 2 True Matches False Matches 1.4 1.2 Scaling 1 IGARSS, July 12-17, 2009
  • 15. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion SIFT rotation sensitivity Amplitude channel, scale: 1., translation: (0,0), smoothing: no 100 80 SIFT 1 SIFT 2 True Matches False Matches 60 40 20 Angle 0 IGARSS, July 12-17, 2009
  • 16. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion SURF rotation sensitivity SURF is highly sensitive to rotation (implementation ?) 100 80 SURF 1 SURF 2 True Matches False Matches 60 40 20 Angle 0 IGARSS, July 12-17, 2009
  • 17. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion SIFT smoothing sensitivity Amplitude channel, rotation: 5◦ , translation: (5,3.3), scaling: 0.9 14 SIFT 1 SIFT 2 True Matches False Matches 12 10 smoothing iterations 8 IGARSS, July 12-17, 2009
  • 18. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion SURF smoothing sensitivity SURF is less sensitive to smoothing than SIFT 14 SURF 1 SURF 2 True Matches False Matches 12 10 smoothing iterations 8 IGARSS, July 12-17, 2009
  • 19. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion SIFT input type sensitivity rotation: 5◦ , translation: (5,3.3), scaling: 0.9, smoothing: 5 Ndwi SIFT 1 SIFT 2 Good matches Bad matches Ndvi hannel4 Amplitude IGARSS, July 12-17, 2009
  • 20. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion SURF input type sensitivity SURF is less sensitive to the input type Ndwi SURF 1 SURF 2 Good matches Bad matches Ndvi hannel4 Amplitude IGARSS, July 12-17, 2009
  • 21. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion SIFT vs. SURF in OTB pros cons SIFT Fast implementation Sensitive to input type (SiftFast) Sensitive to smoothing Robust high rate matching SURF Robust wrt input types Poor matching rates Robust wrt smoothing Highly sensitive (implementation ?) Slower than SiftFast IGARSS, July 12-17, 2009
  • 22. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Outline Detectors in Orfeo Toolbox Validation chain Evaluation results Scene classification IGARSS, July 12-17, 2009
  • 23. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Scene classification (1) Principles Keypoints spatial density: discriminant for classification ? Data: BD Orfeo (patches of pan-sharpened Quickbird) Examples of densities SIFTDENSITYAMPLITUDE5A01 SIFTDENSITYAMPLITUDE5C01 SIFTDENSITYAMPLITUDE5D01 0.2 0.3 0.1 "histo_AMPLITUDE_5_A01.dat" "histo_AMPLITUDE_5_C01.dat" "histo_AMPLITUDE_5_D01.dat" 0.18 0.09 0.25 0.16 0.08 0.14 0.07 0.2 0.12 0.06 Histogram Histogram Histogram 0.1 0.15 0.05 0.08 0.04 0.1 0.06 0.03 0.04 0.02 0.05 0.02 0.01 0 0 0 0 0.01 0.02 0.03 0.04 0.05 0.06 0 0.01 0.02 0.03 0.04 0.05 0.06 0 0.01 0.02 0.03 0.04 0.05 0.06 SIFT Density SIFT Density SIFT Density (a) Urban areas (b) Agricultural areas (c) Woods IGARSS, July 12-17, 2009
  • 24. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Scene Classification (2) Decision Rule Maximum a posteriori Results This simple example shows promising results: Urban Agricultural Woods Urban 20 4 0 Agricultural 13 81 1 Woods 3 3 114 IGARSS, July 12-17, 2009
  • 25. Introduction Detectors in OTB Validation chain Evaluation results Scene classification Conclusion Conclusion Summary OTB is an efficient framework for algorithm validation chain Behavior of SIFT/SURF wrt various parameters Soundness of detectors for registration, but also classification Perspectives Exploit keys for object recognition tasks (work in progress) Out-of core Sift/Surf extraction IGARSS, July 12-17, 2009