Tài liệu SIFT: SCALE INVARIANT FEATURE TRANSFORM BY DAVID LOWE doc

Constructing Scale Space
Construct Scale Space
Take Difference of
Gaussians
Locate DoG Extrema
Sub Pixel Locate
Potential Feature
Points
Build Keypoint
Descriptors
Assign Keypoints
Orientations
Filter Edge and Low
Contrast Responses
Go Play with Your
Features!!
Scale Space
Constructing Scale Space
 Gaussian kernel used to create scale space
 Only possible scale space kernel (Lindberg „94)
where
Laplacian of Gaussians
 LoG - σ
2
∆
2
G
 Extrema Useful
 Found to be stable features
 Gives Excellent notion of scale
 Calculation costly so instead….
Take DoG
Construct Scale Space
Take Difference of
Gaussians
Locate DoG Extrema
Sub Pixel Locate
Potential Feature
Points
Build Keypoint
Descriptors
Assign Keypoints
Orientations
Filter Edge and Low
Contrast Responses
Go Play with Your
Features!!
Difference of Gaussian
 Approximation of Laplacian of Gaussians
DoG Pyramid
DoG Extrema
Construct Scale Space
Take Difference of
Gaussians
Locate DoG Extrema
Sub Pixel Locate
Potential Feature
Points
Build Keypoint
Descriptors
Assign Keypoints
Orientations
Filter Edge and Low
Contrast Responses
Go Play with Your
Features!!
Locate the Extrema of the DoG
 Scan each DOG image
 Look at all neighboring points
(including scale)
 Identify Min and Max
 26 Comparisons
Sub pixel Localization
Construct Scale Space
Take Difference of
Gaussians
Locate DoG Extrema
Sub Pixel Locate
Potential Feature
Points
Build Keypoint
Descriptors
Assign Keypoints
Orientations
Filter Edge and Low
Contrast Responses
Go Play with Your
Features!!

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