Image matching Notes | EduRev

: Image matching Notes | EduRev

 Page 1


Image matching
by Diva Sian
by swashford
Harder case
by Diva Sian by scgbt
Even harder case
“How the Afghan Girl was Identified by Her Iris Patterns”  Read the story 
Harder still?
NASA Mars Rover images
Page 2


Image matching
by Diva Sian
by swashford
Harder case
by Diva Sian by scgbt
Even harder case
“How the Afghan Girl was Identified by Her Iris Patterns”  Read the story 
Harder still?
NASA Mars Rover images
NASA Mars Rover images
with SIFT feature matches
Figure by Noah Snavely
Answer below (look for tiny colored squares…) Features
All is Vanity, by C. Allan Gilbert, 1873-1929
Reading
- M. Brown et al. Multi-Image Matching using Multi-Scale 
Oriented Patches, CVPR 2005
Image Matching Image Matching
Page 3


Image matching
by Diva Sian
by swashford
Harder case
by Diva Sian by scgbt
Even harder case
“How the Afghan Girl was Identified by Her Iris Patterns”  Read the story 
Harder still?
NASA Mars Rover images
NASA Mars Rover images
with SIFT feature matches
Figure by Noah Snavely
Answer below (look for tiny colored squares…) Features
All is Vanity, by C. Allan Gilbert, 1873-1929
Reading
- M. Brown et al. Multi-Image Matching using Multi-Scale 
Oriented Patches, CVPR 2005
Image Matching Image Matching
Invariant local features
Find features that are invariant to transformations
• geometric invariance:  translation, rotation, scale
• photometric invariance:  brightness, exposure, …
Feature Descriptors
Advantages of local features
Locality 
• features are local, so robust to occlusion and clutter
Distinctiveness: 
• can differentiate a large database of objects
Quantity
• hundreds or thousands in a single image
Efficiency
• real-time performance achievable
More motivation…  
Feature points are used for:
• Image alignment (e.g., panoramas)
• 3D reconstruction
• Motion tracking
• Object recognition
• Indexing and database retrieval
• Robot navigation
• … many others
What makes a good feature?
 Snoop demo
Page 4


Image matching
by Diva Sian
by swashford
Harder case
by Diva Sian by scgbt
Even harder case
“How the Afghan Girl was Identified by Her Iris Patterns”  Read the story 
Harder still?
NASA Mars Rover images
NASA Mars Rover images
with SIFT feature matches
Figure by Noah Snavely
Answer below (look for tiny colored squares…) Features
All is Vanity, by C. Allan Gilbert, 1873-1929
Reading
- M. Brown et al. Multi-Image Matching using Multi-Scale 
Oriented Patches, CVPR 2005
Image Matching Image Matching
Invariant local features
Find features that are invariant to transformations
• geometric invariance:  translation, rotation, scale
• photometric invariance:  brightness, exposure, …
Feature Descriptors
Advantages of local features
Locality 
• features are local, so robust to occlusion and clutter
Distinctiveness: 
• can differentiate a large database of objects
Quantity
• hundreds or thousands in a single image
Efficiency
• real-time performance achievable
More motivation…  
Feature points are used for:
• Image alignment (e.g., panoramas)
• 3D reconstruction
• Motion tracking
• Object recognition
• Indexing and database retrieval
• Robot navigation
• … many others
What makes a good feature?
 Snoop demo
Want uniqueness
Look for image regions that are unusual
• Lead to unambiguous matches in other images
How to define “unusual”?
Local measures of uniqueness
Suppose we only consider a small window of pixels
• What defines whether a feature is a good or bad candidate?
Slide adapted from Darya Frolova, Denis Simakov, Weizmann Institute.
High level idea is that corners are good.  You want to find windows that contain strong gradients AND gradients oriented in more than one direction
Feature detection
“flat” region:
no change in all 
directions
“edge”:  
no change along 
the edge direction
“corner”:
significant change 
in all directions
Local measure of feature uniqueness
• How does the window change when you shift by a small amount?
Slide adapted from Darya Frolova, Denis Simakov, Weizmann Institute.
Feature detection
Define
E(u,v) = amount of change when you shift the window by (u,v)
E(u,v) is small
for all shifts
E(u,v) is small
for some shifts
E(u,v) is small 
for no shifts
We want                      to be ______
Page 5


Image matching
by Diva Sian
by swashford
Harder case
by Diva Sian by scgbt
Even harder case
“How the Afghan Girl was Identified by Her Iris Patterns”  Read the story 
Harder still?
NASA Mars Rover images
NASA Mars Rover images
with SIFT feature matches
Figure by Noah Snavely
Answer below (look for tiny colored squares…) Features
All is Vanity, by C. Allan Gilbert, 1873-1929
Reading
- M. Brown et al. Multi-Image Matching using Multi-Scale 
Oriented Patches, CVPR 2005
Image Matching Image Matching
Invariant local features
Find features that are invariant to transformations
• geometric invariance:  translation, rotation, scale
• photometric invariance:  brightness, exposure, …
Feature Descriptors
Advantages of local features
Locality 
• features are local, so robust to occlusion and clutter
Distinctiveness: 
• can differentiate a large database of objects
Quantity
• hundreds or thousands in a single image
Efficiency
• real-time performance achievable
More motivation…  
Feature points are used for:
• Image alignment (e.g., panoramas)
• 3D reconstruction
• Motion tracking
• Object recognition
• Indexing and database retrieval
• Robot navigation
• … many others
What makes a good feature?
 Snoop demo
Want uniqueness
Look for image regions that are unusual
• Lead to unambiguous matches in other images
How to define “unusual”?
Local measures of uniqueness
Suppose we only consider a small window of pixels
• What defines whether a feature is a good or bad candidate?
Slide adapted from Darya Frolova, Denis Simakov, Weizmann Institute.
High level idea is that corners are good.  You want to find windows that contain strong gradients AND gradients oriented in more than one direction
Feature detection
“flat” region:
no change in all 
directions
“edge”:  
no change along 
the edge direction
“corner”:
significant change 
in all directions
Local measure of feature uniqueness
• How does the window change when you shift by a small amount?
Slide adapted from Darya Frolova, Denis Simakov, Weizmann Institute.
Feature detection
Define
E(u,v) = amount of change when you shift the window by (u,v)
E(u,v) is small
for all shifts
E(u,v) is small
for some shifts
E(u,v) is small 
for no shifts
We want                      to be ______
Consider shifting the window W by (u,v)
• how do the pixels in W change?
• compare each pixel before and after by
Sum of the Squared Differences (SSD)
• this defines an SSD “error” E(u,v):
Feature detection:  the math
W
Taylor Series expansion of I:
If the motion (u,v) is small, then first order approx is good
Plugging this into the formula on the previous slide…
Small motion assumption
Consider shifting the window W by (u,v)
• how do the pixels in W change?
• compare each pixel before and after by
summing up the squared differences
• this defines an “error” of E(u,v):
Feature detection:  the math
W
Feature detection:  the math
This can be rewritten:
For the example above
• You can move the center of the green window to anywhere on the 
blue unit circle
• Which directions will result in the largest and smallest E values?
• We can find these directions by looking at the eigenvectors of H
Read More
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