1. Interactive Heuristic Edge Detection Douglas A. Lyon, Ph.D. Chair, Computer Engineering Dept. Fairfield University, CT, USA Lyon@DocJava.com, http://www.DocJava.comhttp://www.DocJava.com Copyright 2002 © DocJava, Inc.

2. Background It is easy to find a bad edge! We know a good edge when we see it!

3. The Problem Given an expert + an image. The expert places markers on a good edge. Find a way to connect the markers.

4. Motivation Experts find/define good edges Knowledge is hard to encode.

5. Approach Mark an important edge Pixels=graph nodes Search in pixel space using a heuristic A* is faster than DP

6. Assumptions User is a domain expert Knowledge rep=heuristics+markers

7. Applications Photo interpretation Path planning (source+destination) Medical imaging

8. Photo Interpretation

9. Echocardiogram 3D-multi-scale analysis

10. Path Plans, the idea

11. Path Planning-pre proc. hist+thresh Dil+Skel

12. Path Planning - Search

13. The Idea The mind selects from filter banks The mind tunes the filters

14. Gabor filter w/threshold The Strong edge is the wrong edge!

15. Sub bands for 3x3 Robinson

16. Sub Bands 7x7 Gabor

17. Gabor-biologically motivated

18. Log filters=prefilter+laplacian

19. Mexican Hat (LoG Kernel)

20. The Log kernel

21. Oriented Filters are steerable

22. Changing Scale at 0 Degrees

23. Changing Phase at 0 degrees

24. Summary Heuristics+markers= knowledge Low-level image processing still needed Global optimization is not appropriate for all applications Sometimes we only want a single, good edge

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