enabling users to guide the design of robust model fitting algorithms

Enabling Users to Guide the Design of Robust Model Fitting Algorithms

Matthias Wimmer, Freek Stulp and Bernd Radig

[email protected]

TechnischeUniversitätMünchen

Interactive Computer Vision,

Rio de Janeiro2007, October 15th

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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



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Model-based Image Interpretation

The model The model contains a parameter vector that represents the model’s configuration. video D video U



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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.



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Introducing Objective Functions



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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



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Design Approach

Shortcomings: Many manual steps Requires domain knowledge Time-consuming (because of loop) Low accuracy



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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)



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Step 1: Manually Annotate Images



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……...............…………………………..

Step 2: Generate Further Annotations

function value = 0function value = 0.3 function value = 0.2



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Step 3: Specify Image Features

Number of features: 6 styles · 3 sizes · 25 locations = 450

Styles (6): Sizes (3): Locations (5x5):



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Step 4: Generate Training Data

Mapping of feature values to the expected function value.



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Step 5: Apply Machine Learning

Machine learning technique: Model Trees Select the most relevant features High runtime performance



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Benefits

1. Locally customized calculation rules

2. Automatic selection of relevant features

3. Generalization from many images



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Evaluation 1: Fitting Accuracy on BioID



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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.



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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



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Thank you!

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

enabling users to guide the design of robust model fitting algorithms

Documents

expected function value

model treesselect

model parameters

parameterized model

image best

best model fit

annotationsfunction

fitting algorithm searches