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Matthias Wimmer, Freek Stulp and Bernd Radig [email protected]. 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 - PowerPoint PPT Presentation
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Enabling Users to Guide the Design of Robust Model Fitting Algorithms
Matthias Wimmer, Freek Stulp and Bernd Radig
TechnischeUniversitätMünchen
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
slide 2/18
Technische Universität MünchenMatthias Wimmer
Outline Model-based image interpretation
Model fitting, objective function Designing objective functions
Our 5-step approach Learning objective functions Partly automated
Evaluation Accuracy Runtime
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
slide 3/18
Technische Universität MünchenMatthias Wimmer
Model-based Image Interpretation
The model The model contains a parameter vector that represents the model’s configuration. video D video U
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
slide 4/18
Technische Universität MünchenMatthias Wimmer
Model Fitting Objective function
Calculates a value that indicates how accurately a parameterized model matches an image.
Fitting algorithm Searches for the modelparameters that describe the image best,i.e it minimizes the objective function.
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
slide 5/18
Technische Universität MünchenMatthias Wimmer
Introducing Objective Functions
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
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Technische Universität MünchenMatthias Wimmer
Ideal Objective FunctionsP1: 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
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
slide 7/18
Technische Universität MünchenMatthias Wimmer
Design Approach
Shortcomings: Many manual steps Requires domain knowledge Time-consuming (because of loop) Low accuracy
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
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Technische Universität MünchenMatthias Wimmer
Our Approach bases on Machine Learning
x x x xx
xxxx
xx
x xx
xx
x x
xx
x x
xx x
xx
xxx
x x
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)
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
slide 9/18
Technische Universität MünchenMatthias Wimmer
Step 1: Manually Annotate Images
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
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Technische Universität MünchenMatthias Wimmer
……...............…………………………..
Step 2: Generate Further Annotations
function value = 0function value = 0.3 function value = 0.2
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
slide 11/18
Technische Universität MünchenMatthias Wimmer
Step 3: Specify Image Features
Number of features: 6 styles · 3 sizes · 25 locations = 450
Styles (6): Sizes (3): Locations (5x5):
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
slide 12/18
Technische Universität MünchenMatthias Wimmer
Step 4: Generate Training Data
Mapping of feature values to the expected function value.
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
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Technische Universität MünchenMatthias Wimmer
Step 5: Apply Machine Learning
Machine learning technique: Model Trees Select the most relevant features High runtime performance
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
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Technische Universität MünchenMatthias Wimmer
Benefits
1. Locally customized calculation rules
2. Automatic selection of relevant features
3. Generalization from many images
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
slide 15/18
Technische Universität MünchenMatthias Wimmer
Evaluation 1: Fitting Accuracy on BioID
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
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Technische Universität MünchenMatthias Wimmer
Evaluation 2: Runtime Characteristicsstatistics-based objective function f m learned objective function f l
C: 8.12 ms
D: 9.75 ms
A: 45.1 ms
B:1360 ms
f m considers all features provided. f l selects the most appropriate features.
Note: C and D are as accurate as B.
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
slide 17/18
Technische Universität MünchenMatthias Wimmer
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
Interactive Computer Vision,
Rio de Janeiro2007, October 15th
slide 18/18
Technische Universität MünchenMatthias Wimmer
Thank you!
ありがとうOnline-Demonstration: http://www9.cs.tum.edu/people/wimmerm