a. opelt, m. fussenegger, a. pinz, p. auer
DESCRIPTION
A. Opelt, M. Fussenegger, A. Pinz, P. Auer. Weak Hypotheses and Boosting for Generic Object Detection and Recognition. Agenda. The Basic Idea Our Framework for generic Object Recognition The techniques used The Learning Model Our Model The Weak Hypotheses Finder Experiments - PowerPoint PPT PresentationTRANSCRIPT
Andreas Opelt (Graz University of Technology and University of Leoben)
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A. Opelt, M. Fussenegger, A. Pinz, P. Auer
Weak Hypotheses
and Boosting
for Generic Object Detection
and
Recognition
Andreas Opelt (Graz University of Technology and University of Leoben)
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Agenda
• The Basic Idea• Our Framework for generic Object Recognition • The techniques used• The Learning Model
• Our Model• The Weak Hypotheses Finder
• Experiments• Discussion / Outlook
Andreas Opelt (Graz University of Technology and University of Leoben)
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The Basic Idea 1/2
We want to go towards ‘real’ Generic Object Recognition!
No pre-selection of the object !
Arbitrary view of the object!
Any instance of the object category!
Any background clutter!
Object is located anywhere in the
image!
Objects shown in any arbitrary scale!
Not only for a special category of objects!
Not special images for learning!
Problems ?
Andreas Opelt (Graz University of Technology and University of Leoben)
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Agarwal and Roth, ECCV 2002,
Cars side database
The Basic Idea 2/2
We want to go towards ‘real’ Generic Object Recognition!We want to go towards ‘real’ Generic Object Recognition!
Oxford database; (Fergus, Perona and Zisserman, CVPR 2003)
Graz database; Bikes, Persons, Background
Andreas Opelt (Graz University of Technology and University of Leoben)
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The Framework
Andreas Opelt (Graz University of Technology and University of Leoben)
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Region Extraction 1/2
[Mikolajczyk/Schmid 2001]
Data reduction: Threshold
[Mikolajczyk/Schmid 2001]
Data reduction: Threshold
[Lowe 1999] (Diff. of Gaussian)
Data reduction: Clustering
Andreas Opelt (Graz University of Technology and University of Leoben)
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Region Extraction 2/2
Andreas Opelt (Graz University of Technology and University of Leoben)
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Region Normalization
• Homomorphic Filtering [Gonzales and Woods, C. 4.5.]
• Size Normalization
Andreas Opelt (Graz University of Technology and University of Leoben)
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The Framework
Andreas Opelt (Graz University of Technology and University of Leoben)
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Local Descriptors
Subsampled Grayvalues
Basic Moments (Dim=10)
[L. Van Gool 1996]
Dim=9
[D. Lowe 1999]
Dim=128 (3 orient. planes, 8x8px)
Andreas Opelt (Graz University of Technology and University of Leoben)
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The Framework
Andreas Opelt (Graz University of Technology and University of Leoben)
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The Learning Model 1/3
Input:
Output:
Weak Hypotheses:
Threshold, Weight
Andreas Opelt (Graz University of Technology and University of Leoben)
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The Learning Model 1/3
Select best Weak Hypothesis
Calculate Threshold
Andreas Opelt (Graz University of Technology and University of Leoben)
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The Learning Model 3/3
Andreas Opelt (Graz University of Technology and University of Leoben)
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Experiments 1/6
Category: Bikes some Weak Hypotheses
Andreas Opelt (Graz University of Technology and University of Leoben)
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Experiments 2/6
Testing
BIKE !
Andreas Opelt (Graz University of Technology and University of Leoben)
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Experiments 3/6
Testing
BIKE ! BIKE !
Andreas Opelt (Graz University of Technology and University of Leoben)
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Experiments 4/6
Testing
NO BIKE ! NO BIKE !
Andreas Opelt (Graz University of Technology and University of Leoben)
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Experiments 5/6
Testing
NO BIKE !
Andreas Opelt (Graz University of Technology and University of Leoben)
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Experiments 6/6
Facts:
Andreas Opelt (Graz University of Technology and University of Leoben)
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Discussion / Outlook
• Further Experimental Evaluation
• Multiclass Categorisation
• Combination with other Types of Regions
Andreas Opelt (Graz University of Technology and University of Leoben)
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Conclusion
• Generic object recognition
• A new Framework
• A new Learning Model
• Good Results
Andreas Opelt (Graz University of Technology and University of Leoben)
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Thank you !
Generic object recognition; not an easy task!
Thanks to the Lava Project and the FWF Project – FSP Cognitive Vision