1. Inferring Edges by Using Belief Propagation
Jue Wang and Jiun-Hung Chen
CSE/EE 576 Spring 2004

2. Outline Formulation Implementation Issues Results Discussion
A proposal for a “better” approach

3. Formulation Two dimensional observation: Magnitude of gradient
Direction of gradient
Five states for hidden nodes:
State 0: Not on edge
State 1: On horizontal edge
State 2: On vertical edge
State 3: On “/” edge
State 4: On “\” edge

4. Formulation
Probabilities:

5. Preprocessing Gaussian Smoothing Differentiation
Non-maximum suppression
Magnitudes, directions of gradient, state priors

6. Implementation Issues
Zero Message Problem
Happens with extremely skew distribution
1 iter.
2 iter.
15 iter.

7. Results
Original image
BP edges
Canny edges

8. Results
Original BP
Canny

9. Results Canny Original BP BP tends to merge very close edges together
Directed message passing? Different message for different edges?

10. Discussion Do loops matter? Yes!!! Sometimes will not converge
Increasing the number of loops is not a good idea

11. Discussion The algorithm is VERY sensitive to and
Solution: learning distributions from examples?
Another issue: too slow!

12. Example-Based Edge Detection*
Input patch
Closest image patches from database Y
Corresponding edge patches from database X
*Example-Based Super-Resolution (Freeman, Jones and Pasztor, IEEE CG&A 2002)