Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion




        Reference algorithm ...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Introduction

      OTB provid...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Outline of the presentation


...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Outline


      SIFT: known to...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


The Scale Invariant Feature Tr...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Algorithm main steps



      ...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Implementation

      Main dif...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Results




                  ...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Outline


      SIFT: known to...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


The Mean-Shift algorithm

    ...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Implementation


      Based o...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Results




       Figure: Mea...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Outline


      SIFT: known to...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


The Bayesian fusion algorithm
...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Implementation


      Inputs ...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Results




      Figure: Usin...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Outline


      SIFT: known to...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


The road extraction algorithm
...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Implementation


      Researc...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Results




           Figure:...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Outline


      SIFT: known to...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Trends for operational process...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Streaming and multi-threading
...
Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion


Conclusion

      We saw
     ...
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Reference algorithm implementations in OTB: textbook cases

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Reference algorithm implementations in OTB: textbook cases
Julien Michel; CS
Thomas Feuvrier; CS
Jordi Inglada; CNES

Published in: Technology, Education
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Reference algorithm implementations in OTB: textbook cases

  1. 1. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Reference algorithm implementations in OTB: Textbook Cases Julien Michel1 , Jordi Inglada2 1 C OMMUNICATIONS & S YSTÈMES 2 C ENTRE NATIONAL D ’ ÉTUDES SPATIALES IGARSS, July 12-17, 2009
  2. 2. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Introduction OTB provides bleeding-edge algorithms to end-users. But what about: Correctness, performances, complexity? Parameters? Scalability to real market data? This talk: Feedback from OTB development team Emphasizes the need for open source reference implementation Shows that OTB is a perfect framework for this IGARSS, July 12-17, 2009
  3. 3. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Outline of the presentation SIFT: known to be good, hard to code Mean Shift: One step forward Bayesian Fusion: Almost happy Road Extraction: Reproducible extraction From reference implementations to operational processing chains IGARSS, July 12-17, 2009
  4. 4. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Outline SIFT: known to be good, hard to code Mean Shift: One step forward Bayesian Fusion: Almost happy Road Extraction: Reproducible extraction From reference implementations to operational processing chains IGARSS, July 12-17, 2009
  5. 5. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion The Scale Invariant Feature Transform A robust keypoints detector Keypoints: local extrema of difference of Gaussian in scale space Descriptors: histogram of local orientation Applications Registration Object recognition Panorama stitching . . . IGARSS, July 12-17, 2009
  6. 6. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Algorithm main steps 1. Build the DoG pyramid 2. Detect extrema within 8 neighbors 3. Refine location by quadric fitting 4. Discard low contrast points 5. Discard edge response 6. Assign one or more orientations per point 7. Compute the descriptor IGARSS, July 12-17, 2009
  7. 7. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Implementation Main difficulties No reference implementation available Initialization differs between papers Steps 3, 4 and 5 did not appear in some papers Parameters for some steps are not detailed (kernel radius, thresholds . . . ) Consequences Implementation fails to achieve expected performances OTB Sift based on the wrapping of SiftFast open source library IGARSS, July 12-17, 2009
  8. 8. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Results Figure: SIFT detection (5 scales) IGARSS, July 12-17, 2009
  9. 9. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Outline SIFT: known to be good, hard to code Mean Shift: One step forward Bayesian Fusion: Almost happy Road Extraction: Reproducible extraction From reference implementations to operational processing chains IGARSS, July 12-17, 2009
  10. 10. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion The Mean-Shift algorithm Overall scheme Iterative non-parametric feature space analysis technique Applied to the joint spatial-range domain Smoothing 1. Iterate the mean-shift procedure until convergence 2. Output spectral value is the spectral part of the local mode Clustering 1. Filter the whole image to get the local modes for each pixel, 2. Cluster pixels with close local modes in the joint domain IGARSS, July 12-17, 2009
  11. 11. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Implementation Based on Edison A small open source library Developed by the authors Performing smoothing and clustering Embedded in OTB Clean OTB compliant wrapping classes Supporting multi-threading (smoothing part) and streaming At the expense of tolerance on results IGARSS, July 12-17, 2009
  12. 12. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Results Figure: Mean Shift clustering (spatial radius: 10, range radius: 40) IGARSS, July 12-17, 2009
  13. 13. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Outline SIFT: known to be good, hard to code Mean Shift: One step forward Bayesian Fusion: Almost happy Road Extraction: Reproducible extraction From reference implementations to operational processing chains IGARSS, July 12-17, 2009
  14. 14. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion The Bayesian fusion algorithm Description Pan-sharpening technique Based on statistical relationships between P and XS Allows to weight P and XS information Promising for multi-modalities fusion Authors wanted to Provide reproducible evidences Advertise for a reference implementation in their papers They proposed to contribute their algorithm to OTB IGARSS, July 12-17, 2009
  15. 15. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Implementation Inputs from the authors Matlab source code of the algorithm Input data, output data and parameters for validation Work done by the OTB team Transpose the Matlab code to OTB compliant C++ code Validate the implementation performances and correctness Feed the input/output data to the daily testing framework Was available in OTB release when published IGARSS, July 12-17, 2009
  16. 16. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Results Figure: Using the Orfeo ToolBox Bayesian Fusion implementation on Ikonos data c European Space Imaging) IGARSS, July 12-17, 2009
  17. 17. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Outline SIFT: known to be good, hard to code Mean Shift: One step forward Bayesian Fusion: Almost happy Road Extraction: Reproducible extraction From reference implementations to operational processing chains IGARSS, July 12-17, 2009
  18. 18. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion The road extraction algorithm Description Path extraction from the gradient of a likelihood map Path filtering according to a priori knowledge Likelihood map = spectral angle wrt a road pixel Advantages Light supervised coarse extraction of the network Fast algorithm (can be further refined) Likelihood map can be changed IGARSS, July 12-17, 2009
  19. 19. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Implementation Research using OTB Existing filters were used Missing one were developed Light integration cost Code reviews Testing and validation Parts of the processing chain can be used individually OTB: an efficient tool for fast prototyping during research IGARSS, July 12-17, 2009
  20. 20. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Results Figure: Using the Orfeo ToolBox Road Extraction framework IGARSS, July 12-17, 2009
  21. 21. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Outline SIFT: known to be good, hard to code Mean Shift: One step forward Bayesian Fusion: Almost happy Road Extraction: Reproducible extraction From reference implementations to operational processing chains IGARSS, July 12-17, 2009
  22. 22. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Trends for operational processing chains Efficiency Achieve best possible performances and correctness Scale to real market data: time and memory consumption Interoperability Similar techniques should be switchable Consecutive techniques should be pluggable Standard interfaces are mandatory But also: progress reporting, error management, common I/O . . . IGARSS, July 12-17, 2009
  23. 23. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Streaming and multi-threading HR imagery means huge data volumes Streaming and multi-threading: These are techniques allowing to overcome computation time and memory capability limitations They need to split the data in order to process them Algorithms which can not process data by chunks are not scalable Scalability of algorithms is crucial for end-user applications IGARSS, July 12-17, 2009
  24. 24. Introduction SIFT MeanShift Bayesian Fusion Road Extraction Operational chains Conclusion Conclusion We saw Usefulness of open source reference implementations Advantages of interoperable implementations From toy reproducible research to operational applications: scalability OTB is perfect for the job Fulfills the requirements above You can help us improve the library Any contribution is useful: algorithm description + input/output data, Matlab/IDL/... code, OTB program, etc. IGARSS, July 12-17, 2009
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