1. Benefits of Feature Detection and matching in images
2. Characteristics of Good Feature
3. Corners are Good Features
4. Harris Corner Detector Algorithm
5. Stages of a SIFT detector
Image matching
translation
rotation
affine
perspective
scale
x,y,Θ
Finding Features
-goal -find points in an image that can be:
found in other images
found precisely well located
found reliably
Repeatability/precision
Saliency/matchability
compactness and efficiency
locality
Corner Detection: Mathematics
E(u,v)=Σw(x,y)[I(x+y,y+v)-I(x,y)]^2
w(x,y) {box function, a Gaussian
The quadratic approximation, following Taylor Expansion, simplifies to
E(u,v) = [u, v]M[u, v]
Scale Invariant Detectors
-sift(Lowe, 2004)
-Find local maximum of
different of Gaussians in space and scale
DoG in simply a pyramid of the difference of Gaussians within each octave
