Scale Invariant Feature Transform
Scale Invariant Feature Transform
Scale Invariant Feature Transform
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Rithwik Mutyala<br />
322552<br />
mrithwik@gmail.com
Key Steps of SIFT<br />
� <strong>Scale</strong>-space extrema detection<br />
� Keypoint detection, scale invariance<br />
� Keypoint Localization<br />
� Stable keypoint selection<br />
� Orientation Assignment<br />
� Orientation invariance and calculation of local image<br />
gradient directions<br />
� Keypoint Descriptor<br />
� Variations: Local shape distortion and change in<br />
illumination
Orientation Assignment<br />
Image from: Lowe 2001
Orientation Assignment<br />
� Selection of same scale as keypoint for Gaussian<br />
smoothed Image L � <strong>Scale</strong> Invariance<br />
� m(x,y) � Gradient Magnitude<br />
Θ(x,y) � Orientation<br />
�L( x �1, y) � L( x �1,<br />
y)<br />
�<br />
GradientVector � �<br />
L( x, y �1) � L( x, y �1)<br />
�<br />
� �
Orientation Assignment<br />
Image from: Jonas Hurrelmann
Image from: Jonas Hurrelmann
� Dimensions of X-Axis (Bin Number)<br />
Image from: Jonas Hurrelmann
Orientation Assignment<br />
� Orientation histogram<br />
� Gradient orientations of sample points within a region<br />
around the keypoint<br />
� Each sample is weighted by 1) Gradient Magnitude<br />
2) Guassian Circular<br />
Window; Smoothing<br />
� Multiple keypoints<br />
� The highest peak is detected (Magnitude H)<br />
� Peaks with 80% of H are used to create multiple vectors<br />
for the same keypoint
Local Image/Region Descriptor<br />
� Next Step is to compute a descriptor for the local image<br />
region<br />
� Similar mechanism to Inferior Temporal Cortex in primates<br />
Image from: Jonas Hurrelmann
Local Image Descriptor<br />
� Orientation histograms allow for significant shift in<br />
Gradient position (and thus provide 3D view variations<br />
with different viewpoints)<br />
� 4x4x8=128 feature vector for each keypoint; this then is<br />
a unique descriptor for the keypoint.<br />
� Illumination Invariance: Normalization of Orientation<br />
vectors to 1
QUESTIONS<br />
Image from: T.Tuytelaars ECCV 2006 Tutorial
Thank You For your Patience
References<br />
� Object Invarience from Local-<strong>Scale</strong> <strong>Invariant</strong><br />
<strong>Feature</strong>s;Lowe 1999<br />
� Disntinctive Image features from <strong>Scale</strong>-<strong>Invariant</strong><br />
Keypoints; Lowe 2004<br />
� Local <strong>Feature</strong> View Clustering for 3D object<br />
recognition; Lowe 2001
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