1. Lecture 6-1CS251: Intro to AI/Lisp II I can see clearly now May 4th, 1999

2. Lecture 6-1CS251: Intro to AI/Lisp II Announcements Assignment 4 –Canny detector Next assignment Project Next topic?

3. Lecture 6-1CS251: Intro to AI/Lisp II What’s the plan? Build on a set of primitives –Edges –Lines –Segments –Regions –Depth –Objects Construct a representation of a 3D scene

4. Lecture 6-1CS251: Intro to AI/Lisp II

5. Lecture 6-1CS251: Intro to AI/Lisp II Threshold

6. Lecture 6-1CS251: Intro to AI/Lisp II

7. Lecture 6-1CS251: Intro to AI/Lisp II Another Edge Finder

8. Lecture 6-1CS251: Intro to AI/Lisp II Wagon Wheel (From UBC)

9. Lecture 6-1CS251: Intro to AI/Lisp II

10. Lecture 6-1CS251: Intro to AI/Lisp II Why bother? Split into signal (time) and frequency domains –Operations performed in one domain have corresponding operations in the other –Convolution operation in time is multiplication in the frequency domain:

11. Lecture 6-1CS251: Intro to AI/Lisp II Back to Discrete Edges Use discrete approximations

12. Lecture 6-1CS251: Intro to AI/Lisp II We have Edges -- Now What? Recognize objects in images Correlation –How closely related are two functions (usually R.V.’s)? –Varies from -1 (perfect negative) through 0 (no correlation) to 1 A & B must be the same size

13. Lecture 6-1CS251: Intro to AI/Lisp II Using Correlation

14. Lecture 6-1CS251: Intro to AI/Lisp II Hough Transform Parameterize –What’s a line? Use parameterization to look for curves/shapes

15. Lecture 6-1CS251: Intro to AI/Lisp II Hough Transform Example Suppose we are looking for straight lines in an image. Start with a point (x',y') in the image, all lines which pass through that pixel have the form:

16. Lecture 6-1CS251: Intro to AI/Lisp II Hough Transform Example Treat x’ and y’ as constants (fix the point under consideration) Each line through the point ( x’, y’ ) is a point on the (m, c) line

17. Lecture 6-1CS251: Intro to AI/Lisp II Hough Transform Example Back to the image: Consider two pixels p and q that lie on the same line in the image p q x y p q m c For each pixel, all of the possible lines through it are represented by a single line in (m,c) space. Single line in (x,y) space which goes through both pixels lies on the intersection of the two lines representing p and q in (m,c) space

18. Lecture 6-1CS251: Intro to AI/Lisp II Hough Conclusion All pixels which lie on the same line in (x,y) space are represented by lines which all pass through a single point in (m,c) space –Single point through which they all pass gives the values of m and c in the equation of the line y=mx+c.

19. Lecture 6-1CS251: Intro to AI/Lisp II Properties of the Hough Transform What kinds of lines can be detected? What’s the range of m? Number of unknown parameters increases the amount of processing increases exponentially.

20. Lecture 6-1CS251: Intro to AI/Lisp II Image Segmentation Break an image into homogeneous segments What’s a segment? –No abrupt changes in intensity across interior –Edge detection can do this, but doesn’t have to

21. Lecture 6-1CS251: Intro to AI/Lisp II Segmentation Techniques Pixel classification Splitting and merging Relaxation

22. Lecture 6-1CS251: Intro to AI/Lisp II Pixel Classification Partition the gray scale into intervals –0 to 31, 32 to 63, 64 to 95, 96 to … –Each interval maps to a class Quick and dirty, but: –Ignores pixel location in image –Works better for color

23. Lecture 6-1CS251: Intro to AI/Lisp II Splitting & Merging Start with an image, I, and split into subsections, S i, where i = 1.. M Define a predicate, H –Checks whether a subsection is homogeneous Four conditions: 1) 3) 2) 4)

24. Lecture 6-1CS251: Intro to AI/Lisp II Split, Merge or Both Merge: Start with condition 3 and begin merging –Individual pixels satisfy 3 Split: Start with condition 4 and begin splitting –Whole image satisfies 4

25. Lecture 6-1CS251: Intro to AI/Lisp II Relaxation Iterative process of labeling Reinforce or inhibit confidence of labeling Keep doing it Convergence?