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Designing Descriptors
16-385 Computer Vision (Kris Kitani)
Carnegie Mellon University
Feature matching is important for tasks like…
Designing Descriptors 16-385 Computer Vision (Kris Kitani) Carnegie - - PowerPoint PPT Presentation
Designing Descriptors 16-385 Computer Vision (Kris Kitani) Carnegie - - PowerPoint PPT Presentation
Designing Descriptors 16-385 Computer Vision (Kris Kitani) Carnegie Mellon University Feature matching is important for tasks like Object instance recognition Schmid and Mohr 1997 Sivic and Zisserman, 2003 Rothganger et al. 2003 Lowe 2002
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Object instance recognition
Rothganger et al. 2003 Lowe 2002 Schmid and Mohr 1997 Sivic and Zisserman, 2003
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Image mosaicing
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If we know where the good features are, how do we match them?
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How do we describe an image patch?
Patches with similar content should have similar descriptors.
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Challenges of designing a feature descriptor
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Photometric transformations
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Geometric transformations
- bjects will appear at different scales,
translation and rotation
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Designing a feature descriptor
(the search for image invariants)
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What is the best descriptor for an image feature?
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Image patch
Just use the pixel values of the patch Perfectly fine if geometry and appearance is unchanged
(a.k.a. template matching)
What are the problems?
( )
1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9
vector of intensity values
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Image patch
Just use the pixel values of the patch Perfectly fine if geometry and appearance is unchanged
(a.k.a. template matching)
What are the problems? How can you be less sensitive to absolute intensity values?
( )
1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9
vector of intensity values
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Image gradients
Use pixel differences
( )
1 2 3 4 5 6 7 8 9
- +
+
- +
vector of x derivatives
What are the problems?
Feature is invariant to absolute intensity values
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Image gradients
Use pixel differences
( )
1 2 3 4 5 6 7 8 9
- +
+
- +
vector of x derivatives
What are the problems? How can you be less sensitive to deformations?
Feature is invariant to absolute intensity values
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Color histogram
Invariant to changes in scale and rotation
What are the problems?
colors
Count the colors in the image using a histogram
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Color histogram
Invariant to changes in scale and rotation
What are the problems?
colors
Count the colors in the image using a histogram
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Color histogram
Invariant to changes in scale and rotation
What are the problems? How can you be more sensitive to spatial layout?
colors
Count the colors in the image using a histogram
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Spatial histograms
What are the problems?
Compute histograms over spatial ‘cells’ Retains rough spatial layout Some invariance to deformations
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Spatial histograms
What are the problems? How can you be completely invariant to rotation?
Compute histograms over spatial ‘cells’ Retains rough spatial layout Some invariance to deformations
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Orientation normalization
Use the dominant image gradient direction to normalize the orientation of the patch
What are the problems? save the orientation angle along with
θ
(x, y, s)