SIFT - Distinctive Image Features from Scale-Invariant Keypoints
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Transcript of SIFT - Distinctive Image Features from Scale-Invariant Keypoints
SIFT - Distinctive Image Features from Scale-Invariant
Keypointsby David G. Lowe
presented by David Störmer
Table of contents
Feature Generation● detection of scale-space
extrema● local extrema detection● maxima/minima filtering
with thresholds● subpixel extrema
detection
Classification Generation● gradient
direction/magnitude diagramm on key-point
● Histogram of directions ● generate SIFT feature
vector
Detection of scale-space extrema
Local extrema detection
● search for maxima/minima within a 3x3x3 region
● performed on results of difference of Gaussian algorithm
Maxima/Minima filtering with thresholds
● in local area there a lots of values
● local extrema can have very small values
● double thresholding helps to remove "unimportant" points that were created from small difference in source image, such as noise
Subpixel extrema detection
● the given data does not represent a signal exactly, because it is sampled
● position of extrema can be extrapolated by using Tylor expression and setting it to zero
● the extrema can also get another value
● this subpixel extrema is done for direction x, y and can also be done for z (between DoG)
Gradient direction/magnitude
● Calculate gradient and magnitude of a 16x16 region around key-point
● create a kind of characteristic of keypoint in location
Histogram of directions
● directions are separated into 36 bins with each 10 degrees● magnitude of each direction is added on certain bin● each bar with a peak over 80% is converted into
a separate key-point with its own direction
Generate SIFT feature vector - 1
Aim of this step: generate 128 dimensional feature vector of 16x16 window around key-point
Generate SIFT feature vector - 2
● in 4x4 Window gradient an magnitude is calculated● foreach, the key-point orientation is subtracted● orientation were put in 8 bin histogramm
Generate SIFT feature vector - 2
● depending on their distance to key-point, a gaussian weighting function is applied
● the result: 8 bins with directions● this is done for alle 4x4 regions within 16x16 region = 128
directions
Results in my thesis
Results in my thesis
Results in my thesis
Thanks
Sources
● http://www.cs.ubc.ca/~lowe/keypoints/ ● http://en.wikipedia.org/wiki/Scale-
invariant_feature_transform ● http://www.aishack.in/2010/05/sift-scale-invariant-feature-
transform/