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Level Set Segmentation with Shape Priors

Level Set Segmentation with Shape Priors. Jue Wang and Jiun-Hung Chen CSE/EE 577 Spring 2004. Notes on Level Set. Steps for implementation Define your own energy functional Initialize the curve and surface Evolve according to the numerical solution:

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Level Set Segmentation with Shape Priors

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  1. Level Set Segmentation with Shape Priors Jue Wang and Jiun-Hung Chen CSE/EE 577 Spring 2004

  2. Notes on Level Set • Steps for implementation • Define your own energy functional • Initialize the curve and surface • Evolve according to the numerical solution: • Continue until stopping criteria is reached

  3. Notes on Level Set • So the key is • Defining a good energy function according to your application • For basic image segmentation, the energy functional is usually defined as:

  4. Notes on Level Set • Numerical Implementation • Not easy to figure out but they’re in books, for example • Numerical stability should be also considered when you design your own energy function

  5. Segmentation with Shape Prior Reference shape Level set seg. w/o shape prior Level set seg. w/ shape prior Input image D. Cremers and S. Soatto. A pseudo-distance for shape priors in level set segmentation. IEEE Workshop on Variational, Geometric and Leve Set Methods in Computer Vision, 2003.

  6. Segmentation with Shape Prior • Basic Idea • Shape prior can be represented as another implicit function • The evolution of current is influenced by the distance between and . • Problem: how to define the distance

  7. Segmentation with Shape Prior • A Pseudo-distance • Shape energy: • Evolution function:

  8. Segmentation with Shape Prior • Pose parameters • Numerical solution help needed for this 

  9. Results • An image’s worth of thousands of words • An EXE is worth more

  10. Future Work • How to incorporate statistical shape models? • Have a bunch of reference shapes instead of one • X.M. et al. Integrating prior shape models into level-set approaches. Pattern Recognition Letters, April 2004.

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