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Enabling Users to Guide the Design of Robust Model Fitting Algorithms

Matthias Wimmer, Freek Stulp and Bernd Radig matthias.wimmer@cs.tum.edu. Technische Universität München. Enabling Users to Guide the Design of Robust Model Fitting Algorithms. Outline. Model-based image interpretation Model fitting, objective function Designing objective functions

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Enabling Users to Guide the Design of Robust Model Fitting Algorithms

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  1. Matthias Wimmer, Freek Stulp and Bernd Radig matthias.wimmer@cs.tum.edu Technische Universität München Enabling Users to Guide the Design of Robust Model Fitting Algorithms

  2. Outline • Model-based image interpretation • Model fitting, objective function • Designing objective functions • Our 5-step approach • Learning objective functions • Partly automated • Evaluation • Accuracy • Runtime

  3. Model-based Image Interpretation • The model The model contains a parameter vector that represents the model’s configuration. video Dvideo U

  4. Model Fitting • Objective functionCalculates a value that indicates how accurately a parameterized model matches an image. • Fitting algorithmSearches for the modelparameters that describe the image best,i.e it minimizes the objective function.

  5. Introducing Objective Functions

  6. Ideal Objective Functions P1: Correctness property:The global minimum corresponds to the best model fit. P2: Uni-modality property:The objective function has no local extrema. ¬ P1 P1 ¬P2 P2

  7. Design Approach Shortcomings: • Many manual steps • Requires domain knowledge • Time-consuming (because of loop) • Low accuracy

  8. Our Approach bases on Machine Learning • Ideal objective function necessary • Distance between current and correct location of contour point • Provides training data • Machine Learning yields calculation rules • Guided by human experience (widely automated) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

  9. Step 1: Manually Annotate Images

  10. Step 2: Generate Further Annotations ……...............………………………….. function value = 0.3 function value = 0 function value = 0.2

  11. Step 3: Specify Image Features Styles (6): Sizes (3): Locations (5x5): Number of features: 6 styles · 3 sizes · 25 locations = 450

  12. Step 4: Generate Training Data • Mapping of feature values to the expected function value.

  13. Step 5: Apply Machine Learning Machine learning technique: Model Trees • Select the most relevant features • High runtime performance

  14. Benefits • Locally customized calculation rules • Automatic selection of relevant features • Generalization from many images

  15. Evaluation 1: Fitting Accuracy on BioID

  16. Evaluation 2: Runtime Characteristics statistics-based objective function f m learned objective function f l A: 45.1 ms B: 1360 ms C: 8.12 ms D: 9.75 ms • f m considers all features provided. • f l selects the most appropriate features. • Note: C and D are as accurate as B.

  17. Ongoing Research and Outlook • Integration of further image features • Compute the image features on the fly • Learning objective functions for 3D models • Application to different scenario • Medical scenario • Robot scenario: • Model of indoor environment • Self localization

  18. Thank you! ありがとう Online-Demonstration: http://www9.cs.tum.edu/people/wimmerm

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