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Inferring Edges by Using Belief Propagation

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.

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Inferring Edges by Using Belief Propagation

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  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 15 iter. 1 iter. 2 iter.

  7. Results Original image Canny edges BP 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)

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