1. Image Features, Hough Transform Image Pyramid CSE399b, Spring 06 Computer Vision Lecture 10 http://www.quicktopic.com/35/H/NHVD8SZQQJHZ

2. Boundary and Edge: Edge detection-> lines

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

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

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

6. 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

7. 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:

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

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

10. 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 ?

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

12. Basic Hough Transform

13. Example

15. 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

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

18. A detour through scale space

19. Image encoding-decoding 1) Image statistics: pixel in neighborhood are correlated, encode per pixel value is redundant 2) Predictive Coding:use raster scan, predict based on pass value, and store only the error in prediction. Simple and fast 10 202224 10 15212324 signal prediction 005110Error-encoded

20. Non-causal prediction non-causal involves typically transform, or solution to a large sets of equations. Encode block by block. Bigger compression but slower. 10 202224 131017222324 signal prediction 203010Error-encoded

21. Gaussian Pyramid for encoding 1)Prediction using weighted local Gaussian average 2)Encode the difference as the Laplacian 3)Both Laplacian and the Averaged image is easy to encode [Burt & Adelson, 1983]

22. Gaussian pyramid

23. Choice in weighting function Gaussian

24. Image Expansion

27. + - Laplaican Image

28. Gaussian pyramid is smooth=> can be subsampled Laplacian pyramid has narrow band of frequency=> compressed

29. Ln = Gn