Inferring Edges by Using Belief Propagation

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Presentation transcript:

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

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

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

Formulation Probabilities:

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

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

Results Original image BP edges Canny edges

Results Original BP Canny

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

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

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

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)