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/16 Technische Universität München Sylvia Pietzsch Model-based Image Interpretation Model Contains a parameter vector p that represents the model‘s configurations. Objective Function Calculates how well a parameterized model fits to an image. Fitting Algorithm Searches for the model that fits the image best by minimizing the objective function.
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Sylvia Pietzsch
Chair for Image UnderstandingComputer Science
Technische Universität München
Learning Generic and Person Specific Objective Functions
Diplomarbeit
07.02.2007 2/16Technische Universität MünchenSylvia Pietzsch
Overview Model-based Image Interpretation
Generic Objective Functions Traditional Approach Ideal Objective Functions Learning Objective Functions Experimental Evaluation
Person-specific Objective Functions Experimental Evaluation
07.02.2007 3/16Technische Universität MünchenSylvia Pietzsch
Model-based Image Interpretation Model
Contains a parameter vector p that represents the model‘s configurations.
Objective Function Calculates how well a parameterized model fits to an image.
Fitting Algorithm Searches for the model that fits the image best by minimizing the objective function.
07.02.2007 4/16Technische Universität MünchenSylvia Pietzsch
Traditional Approach
Designer selects salient features from the image and composes them.
Based on designer‘s intuition and implicit knowledge of the domain.
shortcomings: time-consuming resulting objective function is not ideal
07.02.2007 5/16Technische Universität MünchenSylvia Pietzsch
Ideal Objective FunctionsP1: Correctness Property:
The global minimum of the objective function corresponds to the best model fit.
P2: Uni-Modality Property:The objective function has no local extrema or saddle points.
07.02.2007 6/16Technische Universität MünchenSylvia Pietzsch
Example: Comparing Objective Functions
a) image b) along perpendicular c) edge values d) designed objective function e) ideal objective function f) training samples g) learned objective function
07.02.2007 7/16Technische Universität MünchenSylvia Pietzsch
Learning the Objective Function (1)
07.02.2007 8/16Technische Universität MünchenSylvia Pietzsch
Learning the Objective Function (2)
07.02.2007 9/16Technische Universität MünchenSylvia Pietzsch
Learning the Objective Function (3)
6 styles · 3 sizes · (5 · 5) locations = 450 features
07.02.2007 10/16Technische Universität MünchenSylvia Pietzsch
Evaluation 1: Used Features Model trees tend to select the most relevant
features. Edge-based features are hardly used at all.
07.02.2007 11/16Technische Universität MünchenSylvia Pietzsch
Evaluation 2: RobustnessIndicators measure the fulfillment of P1 and P2:I1: Correctness Indicator
Distance between the ideal position of the contour point and the global minimum of the objective function
I2: Uni-Modality Indicator Total number of local minima divided by the size of the considered region
07.02.2007 12/16Technische Universität MünchenSylvia Pietzsch
Evaluation 3: Learning Distance
07.02.2007 13/16Technische Universität MünchenSylvia Pietzsch
Person Specific Objective Functions Single Images
The objective function has to take any appearance of a human face into consideration.
➱ moderate accuracy
Image SequenceThe appearance of a person‘s face only changes slightly. Consider particular characteristics of the visible person,
e.g. beard, glasses, bald head,... ➱ increase of accuracy
Challenges: Learn specific objective functions for groups of persons offline. Detect the correct group online.
07.02.2007 14/16Technische Universität MünchenSylvia Pietzsch
Evaluation: Fitting Results45 persons from news broadcasts on TV
07.02.2007 15/16Technische Universität MünchenSylvia Pietzsch
Outlook Learning objective functions for 3D-Models
Integration of further image features
Compute the image features on the fly
Automatic detection of the visible person:e.g. via AAM parameters
07.02.2007 16/16Technische Universität MünchenSylvia Pietzsch
The End
