Gaussian and non-Gaussian processes of zero power variation, and ...
Scale Invariant Feature Transform · Fundamental theory 6 DOG (edge) Scale-space axioms. ......
Transcript of Scale Invariant Feature Transform · Fundamental theory 6 DOG (edge) Scale-space axioms. ......
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2019-04-10
Scale Invariant Feature Transform
Han Sol Kang
ISL Lab Seminar
: SIFT
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Contents
2
Fundamental theory
SIFT
Introduction
Example
Summary
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Introduction
3
illumination illumination + Scale
illumination + Scale + Rotation illumination + Scale Rotation + Affine
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Introduction
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David G Lowe
[2] Lowe, David G. "Distinctive image features from scale-invariant keypoints." International journal of computer vision 60.2 (2004): 91-110.
[1] Lowe, David G. "Object recognition from local scale-invariant features." Computer vision, 1999. The proceedings of the seventh IEEE international conference on. Vol. 2. Ieee, 1999.
A senior research scientist at Google (Seattle) in the Machine Intelligence Group.
99: Object recognition from local scale-invariant features [1]
04: Distinctive Image Features from Scale-Invariant Keypoints [2]
Autostich
: Atuomated paranoma creation
SIFT
: Matching with local invariant features
Augmented reality in natural scenes
[Overview of Research Projects]
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Introduction
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Scale-Space Extrema Detection
Accurate KeypointLocalization
Orientation Assignment
KeypointDescription
Search over multiple scales and Image locations.
Select keypoints based on a measure of stability.
Compute best orientation(s) for each keypoint region.
Use local image gradients at selected scale and rotation to describe each keypoint region.
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Fundamental theory
6
DOG (edge)
Scale-space axioms
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Fundamental theory
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LOG (blob)
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Fundamental theory
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Normalized LOG
Normalization : LOG
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Fundamental theory
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DOG & LOG
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Fundamental theory
10
Gaussian Pyramid
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Difference of Gaussian(DOG)Gaussian
Convolution with
Gaussian
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SIFT
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Detection of Scale-Space Extrema
Extrema : maxima & minima
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SIFT
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Detection of Scale-Space Extrema
Scale
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aerial photographs, industrial images)
SIFT
Detection of Scale-Space Extrema
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SIFT
Accurate Keypoint Localization (low contrast)
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SIFT
Accurate Keypoint Localization (edge)
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SIFT
Accurate Keypoint Localization
(a) 233x189 pixel original image
(d) 536 keypoints location
(threshold on ratio of principal curvatures)
(c) 729 keypoints location (threshold on minimum contrast)
(b) 832 keypoints location
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SIFT
Orientation Assignment
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SIFT
Orientation Assignment
Histogram : Using 36bins
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SIFT
The Local Images Descriptor
illumination : normalization vector
(Feature vector < 0.2)
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SIFT
The Local Images Descriptor
r : the number of orientations
n : the width
The size of the resulting
descriptor vector is 2rn
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SIFT
Keypoint Matching
Object model
(train image)Test image
DB
offline online
1) Nearest-neighbor search
2) Cluster identification by Hough transform
voting
3) Model verification by linear least squares
4) Outlier detection
: Euclidean distance, K-D tree, BBF(Best-Bin-First)
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SIFT
Keypoint Matching
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Example
Recognition
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Example
Recognition
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Q & A
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