Intro to Sift
Yu Liu 4.17.2012
Why? What? How?
USCT, Dept. EEIS, Yu Liu
3
1. Image Matching in a common and important problem in computer vision. 2. Application in:
◦ ◦ ◦ ◦ ◦ Object or scene recognition 3D reconstruction Stereo correspondence Motion tracking Image Searching
USCT, Dept. EEIS, Yu Liu
4
3. Traditional method: simple corner detectors is not stable when you have images of different scales and rotations.
4. We need a method can solve:
◦ ◦ ◦ ◦ ◦ ◦ Different Scale Rotation Different Illumination Changed Viewpoint Affine Distortion Addition of noise
USCT, Dept. EEIS, Yu Liu 5
SITF is a method for extracting distinctive invariant features, providing robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination.
Raised by David G. Lowe in Distinctive Image Features from Scale-Invariant Keypoints in 2004.
David G. Lowe Computer Science Department 2366 Main Mall University of British Columbia Vancouver, B.C., V6T 1Z4, Canada E-mail: lowe@cs.ubc.ca
USCT, Dept. EEIS, Yu Liu
6
Main idea: Extract a set of features, which are invariant to image scaling and rotation, change in illumination and 3D viewpoint.
1. Create Scale space:
◦ Create DoG Pyramid of Images.
Octave 5 Octave 4 Octave 3
…
…
8
…
4
2
…
Octave 2
…
Octave 1
USCT, Dept. EEIS, Yu Liu
7
2. Detect Keypoint:
◦ ◦ ◦ ◦ (1) (2) (3) (4) Scale-space extrema detection Keypoint localization Select optimal keypoints Orientation assignment of Keypoint
D 2 D 1 X ( 2) X X
T
Tr H r 1 Det H r
2
2
USCT, Dept. EEIS, Yu Liu
8
3. Keypoint descriptor: features
◦ ◦ ◦ ◦ Sample region- Around keypoint Rotation by angle of keypoint’s orientation Orientation histogtram entry- descriptor: 4*4*8 Normalization x x y y
3 oct 3 oct 3 oct 3 oct
x
y
USCT, Dept. EEIS, Yu Liu
Why? What? How?
USCT, Dept. EEIS, Yu Liu
3
1. Image Matching in a common and important problem in computer vision. 2. Application in:
◦ ◦ ◦ ◦ ◦ Object or scene recognition 3D reconstruction Stereo correspondence Motion tracking Image Searching
USCT, Dept. EEIS, Yu Liu
4
3. Traditional method: simple corner detectors is not stable when you have images of different scales and rotations.
4. We need a method can solve:
◦ ◦ ◦ ◦ ◦ ◦ Different Scale Rotation Different Illumination Changed Viewpoint Affine Distortion Addition of noise
USCT, Dept. EEIS, Yu Liu 5
SITF is a method for extracting distinctive invariant features, providing robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination.
Raised by David G. Lowe in Distinctive Image Features from Scale-Invariant Keypoints in 2004.
David G. Lowe Computer Science Department 2366 Main Mall University of British Columbia Vancouver, B.C., V6T 1Z4, Canada E-mail: lowe@cs.ubc.ca
USCT, Dept. EEIS, Yu Liu
6
Main idea: Extract a set of features, which are invariant to image scaling and rotation, change in illumination and 3D viewpoint.
1. Create Scale space:
◦ Create DoG Pyramid of Images.
Octave 5 Octave 4 Octave 3
…
…
8
…
4
2
…
Octave 2
…
Octave 1
USCT, Dept. EEIS, Yu Liu
7
2. Detect Keypoint:
◦ ◦ ◦ ◦ (1) (2) (3) (4) Scale-space extrema detection Keypoint localization Select optimal keypoints Orientation assignment of Keypoint
D 2 D 1 X ( 2) X X
T
Tr H r 1 Det H r
2
2
USCT, Dept. EEIS, Yu Liu
8
3. Keypoint descriptor: features
◦ ◦ ◦ ◦ Sample region- Around keypoint Rotation by angle of keypoint’s orientation Orientation histogtram entry- descriptor: 4*4*8 Normalization x x y y
3 oct 3 oct 3 oct 3 oct
x
y
USCT, Dept. EEIS, Yu Liu