1. Panorama Tools H2T2 Group Capstone Project

2. H2T2 Group TungNS00457 - Project Manager HoaHM00556 - DesignerHuongP00282 - DeveloperThoND00288 - Tester

3. Content I.Overview II.PMS Project 1.Requirements 2.Software Process Model 3.Architecture Design 4.Algorithm 5.Test III.Demo IV.Q&A

4. Overview What is Panorama?

5. Overview How to make a Panorama?

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

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 Functional RequirementNon-functional Requirement Create panorama automatically Create PMS file project Display image Edit image Friendly interface Easy to use

12. Requirement System Requirement Specification Hardware InterfacesSoftware Interfaces 2 GHz processor 32-bit (x86) 2 GB RAM or higher. 128 MB Graphic Card or higher. Microsoft Visual Studio 2010 OpenCV 2.2 Library.NET Framework 4.0

13. Requirement o System features Use case 1

14. Requirement o System features Use case 2

15. Requirement o System features Use case 3

16. Requirement o System features Use case 4

17. Requirement o System features Use case 5

18. Requirement o System features Use case 6

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

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

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. 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 Algorithm

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  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 "RANdom SAmple 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 imageThe 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 CasesTime 02 images (1280x800)~ 1.5s 03 images (1280x800)~ 3.5s 06 images (1280x800)~ 10s 08 images (1280x800)~ 15s

43. Compare with Autopano Giga 2.5

44. Assessment

45. Q&A