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Panorama Tools

Panorama Tools. Capstone Project. H2T2 Group. H2T2 Group. TungNS00457 - Project Manager. HoaHM00556 - Designer. HuongP00282 - Developer. ThoND00288 - Tester. Content. Overview PMS Project Requirements Software Process Model Architecture Design Algorithm Test Demo Q&A.

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Panorama Tools

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  1. Panorama Tools Capstone Project H2T2 Group

  2. H2T2 Group TungNS00457 - Project Manager HoaHM00556 - Designer HuongP00282 - Developer ThoND00288 - Tester

  3. Content • Overview • PMS Project • Requirements • Software Process Model • Architecture Design • Algorithm • Test • Demo • Q&A

  4. Overview • What is Panorama?

  5. Overview • How to make a Panorama?

  6. Some type of panoramic images • Planar of Flat • Cube • Cylinder • Sphere

  7. Some existing methods and solutions • Kolor Autopano

  8. Some existing methods and solutions • Microsoft’s Image Composite Editor

  9. Our idea • Free and open source software. • High quality. • Powerful.

  10. PMS Project

  11. Requirement • User Requirement Specification

  12. Requirement • System Requirement Specification

  13. Requirement • System features • Use case 1

  14. Requirement • System features • Use case 2

  15. Requirement • System features • Use case 3

  16. Requirement • System features • Use case 4

  17. Requirement • System features • Use case 5

  18. Requirement • System features • Use case 6

  19. Software Process Model WHY CHOOSE? • PMS team members experience • PMS project characteristic OUR CHOSE Iterative and incremental development

  20. Architecture Design Application Core-PMS.dll GUI • Choice of System Architecture WPF OpenCV2.2 NET Framework 4.0 The basic of system architecture to build the application “Panorama Tool”

  21. Architecture Design • Component Diagram

  22. Architecture Design • Core Package • GUI Package

  23. Architecture Design • Sequence Diagram

  24. Architecture Design • User Interface Design

  25. Architecture Design • Data Structure: *.PMS file

  26. Algorithm • Image Stitching algorithm flow: • Reference: • [1] Jubiao Li and Junping Du • Study on Panoramic Image Stitching Algorithm, 2010 PACCS

  27. Algorithm • Feature Extraction: Harris Corner Detection, SIFT, SUFT, etc • Feature Matching: Neighbor Matching, SIFT descriptors, SUFT descriptor, etc • Mismatch Removal & Image Registration: RANSAC • Image Fusion: Using result of Image Registration to stitch images

  28. Algorithm • Feature Extraction: Harris Corner Detection • Simple example with function E() = Sum(all pixel in small window) • Window around flat: E do not change • Window around edge: E change in some directions, do not change along edge • Window around corner: E change in all directions

  29. Algorithm • Feature Extraction: Harris Corner Detection

  30. Algorithm • Feature Extraction: Harris Corner Detection •  window size = 3, threshold = 0.1 • Reference: • [2] C. Harris and M.J. Stephens. A combined corner and edge detector. In • Alvey Vision Conference, pages 147–152, 1988.

  31. Algorithm • Feature Matching: Neighbor Matching • Area to compare two features from two images Distance(X,Y) = SUM (Xi * Yi) / SQRT (Yi * Yi) window size = 51 pixel; adaptive threshold

  32. Algorithm • Mismatch Removal & Image Registration: RANSAC • RANSAC is an abbreviation for "RANdomSAmple Consensus" Reference: [3] Ondrej Chum (2005) - "Two-View Geometry Estimation by Random Sample and Consensus"

  33. Algorithm • Mismatch Removal & Image Registration: RANSAC • We consider a couple matching key features from two image is one point in previous sample of RANSAC, we have to find the model to fit the maximum number of coupe matching key features.  Model to use RANSAC: Reference: [1] Jubiao Li and Junping Du Study on Panoramic Image Stitching Algorithm, 2010 PACCS

  34. Algorithm • Mismatch Removal & Image Registration: RANSAC • Sample result of using RANSAC:

  35. Algorithm • Image Fusion: Using result of Image Registration (matrix M) to stitch images The first image The second image

  36. Algorithm • Image Fusion: Using result of Image Registration (matrix M) to stitch images • Result with no blending • Result with blending

  37. Test The V-Model

  38. Test • Test Approach • Unit testing • Integration testing • System testing • Acceptance testing

  39. Test • Test cases • PCL (Program Check List) test cases • Why PCL? • Ensure quality of application. • Easy to detect defects and issues. • Reduce effort.

  40. Test • Defect logs tracking system

  41. Result

  42. Compare with Autopano Giga 2.5

  43. Assessment

  44. Q&A

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