1. Color a* b* Brightness L* Texture Original Image Features Feature combination E D 22 Boundary Processing Textons A B C A B C 22 Region Processing

2. RegionTexture : Histogram of oriented edge response Boundary and Edge: Edge detection-> lines Interests points: Corner detection

3. Texture Feature (preview) Texture Gradient TG(x,y,r,  )  2 difference of texton histograms Textons are vector-quantized filter outputs Texton Map

4. Texture boundary Canny2MMUsHumanImage

5. Texture boundary Canny2MMUsHumanImage

6. Texture boundary Canny2MMUsHumanImage

7. Corner Detections (preview)

8. Matching with Features Detect feature points in both images Find corresponding pairs

9. Matching with Features Detect feature points in both images Find corresponding pairs Use these pairs to align images

10. Boundary and Edge: Edge detection-> lines

11. An example: S.F. in fogS.F. in Canny

12. An example: S.F. in fogS.F. with Hough lines

13. Hough Transform image edges needs to be grouped into lines and junctions Hough transform: Detect lines in an edge image

14. Line Representation is the distance from the origin to the line is the norm direction of the line Image space : Hough space : point in image space ==> a curve in hough space

15. Line Representation is the distance from the origin to the line is the norm direction of the line Image space : Hough space : point in image space ==> a curve in hough space For every theta, set:

16. Hough Space point in hough space ==> line in image space

17. Intersection of the curves Each pixel in the image => One curve in Hough space What is the intersection of the curves?

18. Hough Transform Points in the line : In hough space, all the curves pass: So the intersection of the curves is the parameters of the line! Next question: How to find the intersection ?

19. Voting Scheme Each edge pixel in the image votes in Hough space for a series of Choose the of maximum votes

20. Basic Hough Transform

21. Example

23. Extension Choose the sampling of Use gradient of the image voting for specific Iteratively find the maximum votes and remove corresponding edge pixels Suppress edge pixels close to the detected lines

24. Example of Using Estimated Edge Orientation+Iterative line removal