CS654: Digital Image Analysis Lecture 30: Color Model Conversion.
CS654: Digital Image Analysis Lecture 36: Feature Extraction and Analysis.
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Transcript of CS654: Digital Image Analysis Lecture 36: Feature Extraction and Analysis.
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CS654: Digital Image Analysis
Lecture 36: Feature Extraction and Analysis
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Recap of Lecture 35
• JPEG
• DCT
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Outline of Lecture 36
• Feature representation
• Shape feature
• Curvature
• Curvature Scale Space
• Other shape features
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Introduction
• The goal in digital image analysis is to extract useful information for solving application-based problems.
• The first step to this is to reduce the amount of image data using methods that we have discussed before:
• Image segmentation
• Filtering in frequency domain
• Morphology,
• …
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What next ??
• The next step would be to extract features that are useful in solving computer imaging problems.
• What features to be extracted are application dependent.
• After the features have been extracted, then analysis can be done.
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Shape features
Shape
Contour Region
Structural
• Syntactic• Graph• Tree• Model-driven• Data-driven• Shape context
• Perimeter• Compactness• Eccentricity• Fourier Descriptors• Wavelet Descriptors• Curvature Scale Space• Shape Signature• Chain Code• Hausdorff Distance• Elastic Matching
Non-Structural • Area• Euler Number• Eccentricity• Geometric Moments• Zernike Moments• Pseudo-Zernike Mmts• Legendre Moments• Grid Method
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Planer curves
Parameterized curve 𝐶 (𝑝 )={𝑥 (𝑝 ) , 𝑦 (𝑝 ) } 𝑝∈ [0,1 ]
y
x
𝐶 (0.1 )
𝐶 (0.5 )𝐶 (0.8 )
Closed curve:
y
x
𝐶 (𝑥 )=¿{𝑥 ,𝐹 (𝑥 ) }
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Planer curves: tangent, curvatureParameterized curve
𝐶 (𝑝 )={𝑥 (𝑝 ) , 𝑦 (𝑝 ) } 𝑝∈ [0,1 ]𝐶 (0.1 )
𝐶 (0.5 )𝐶 (0.8 )
Tangent 𝑡=𝐶𝑝
¿𝐶𝑝∨¿¿
𝐶𝑝=𝜕𝐶𝜕𝑝
¿ [𝑥𝑝 , 𝑦 𝑝]
¿𝐶 𝑠
is a parameterization to get tangent of unit length
magnitude of the derivative is unity
⟨𝐶 𝑠 ,𝐶𝑠 ⟩=1 𝜕𝜕𝑠 ⟨𝐶𝑠 ,𝐶 𝑠 ⟩= 𝜕
𝜕 𝑠1 ⇒ 2 ⟨𝐶 𝑠 ,𝐶𝑠𝑠 ⟩=0⇒ ⟨𝑪𝒔 ,𝑪𝒔𝒔 ⟩=𝟎
𝑪𝒔𝒔
Curvature: Magnitude of the second derivative,
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Curvature
• Magnitude of the second derivative curvature
• Change in the tangent between two successive point is more
• Curvature is more curve is curving a lot
𝜅=0𝜅=
1𝑟
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Linear Transformation
Affine transformation:
𝐴= [𝑢1 ,𝑢2 ] , h𝑤 𝑒𝑟𝑒 ⟨𝑢1 ,𝑢2 ⟩=0 𝑎𝑛𝑑 ⟨𝑢𝑖 ,𝑢𝑖 ⟩=1Euclidean transformation:
Euclidean
Affine Affine
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Linear Transformation
Equi- Affine transformation:
Euclidean
Equi-Affine
det ( 𝐴)=1
Equi- Affine
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Differential Signature
Euclidean invariant signature {𝑠 ,𝜅 (𝑠 ) }
𝑠
𝜅
Starting point?
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Affine transformation
𝐼 2 (𝑥 , 𝑦 )=𝐼 1(𝑇 1 (𝑥 , 𝑦 ) ,𝑇 2 (𝑥 , 𝑦 ))
[𝑇 1 (𝑥 , 𝑦 )𝑇 2 (𝑥 , 𝑦 )]=[𝑎 𝑏
𝑐 𝑑 ][𝑥𝑦 ]+[𝑒𝑓 ]Equi-affine:
What would be the definition of arc length and curvature in case of affine transformation?
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Re-parameterization
• The same curve C, can be parameterized with two different parameters
•
• Magnitude of the derivative changes, not the curve
• Geometric measurement should be invariant to parameterization
• Invariant under group of transformation
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Euclidean arc-length
• Only allows for rotation and translation
• Length is preserved
𝑑𝑥
𝑑𝑦𝑑𝑠
𝑑𝑠=√𝑑𝑥2+𝑑𝑦 2
¿𝑑𝑝𝑑𝑝
√𝑑𝑥2+𝑑 𝑦2
¿𝑑𝑝√( 𝑑𝑥𝑑𝑝 )2
+( 𝑑𝑦𝑑𝑝 )2
=¿𝐶𝑝∨𝑑𝑝
𝑠=∫ ¿𝐶𝑝∨𝑑𝑝 𝐿=∫0
𝐿
𝑑𝑠
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Equi-affine arclength
• Length is not preserved any more, however area is preserved
𝐶𝑣
𝐶𝑣𝑣
= parameterization for affine transform
|𝐶𝑣 ,𝐶𝑣𝑣|=1
𝑣=∫|𝐶𝑝 ,𝐶𝑝𝑝|13 𝑑𝑝
𝑣=∫|𝑪𝒔 ,𝑪𝒔𝒔|13 𝑑𝑠1
𝜅 𝑣=∫ 𝜅13 𝑑𝑠 𝑑𝑣=𝜅
13 𝑑𝑠
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Equi-affine curvature
|𝐶𝑣 ,𝐶𝑣𝑣|=1 ⇒𝜕𝜕𝑣 |𝐶𝑣 ,𝐶𝑣𝑣|=
𝜕𝜕 𝑣1
⇒|𝐶𝑣𝑣 ,𝐶𝑣𝑣|+|𝐶𝑣 ,𝐶𝑣𝑣 𝑣|=0
⇒|𝐶𝑣 ,𝐶𝑣𝑣𝑣|=0
⇒𝐶𝑣∥𝐶𝑣𝑣𝑣
⇒𝐶𝑣𝑣𝑣=𝜇𝐶𝑣
Affine invariant curvature
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Differential signature
Affine invariant signature {𝑣 ,𝜇 (𝑣 )}
𝑠
𝜅
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Curvature Scale Space
• Defined a unique way of observing and studying 2-D closed shapes
• Trace the outer most closed curve of the object and thus proceed
• Mapping to a space which represents each point as a curvature w.r.t. the arc length.
• Matching is performed using CSS image
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CSS Image
• It is a multi-scale organization of the inflection points (zero crossing points) of an evolving contour
• Curvature is a local measure of how fast a planar contour is turning
•
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Process
1. This method convolutes a path (arc length ) based parametric representation of a planar curve with a Gaussian function
2. As the Gaussian width varies from a small to a large value.
3. Plot the curvature vs. the normalized arc length of the planar curve.
4. As the Gaussian width is increased, • the scale of the image increases or• the image evolves and thus the amount of noise is reduced and the
curve distortions smoothened.
• The benefits of this representation are that it is invariant under rotation ,uniform scaling and translation of the curve.
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Illustration
arclength parameter on the original contour
sta
ndar
d de
viat
ion
of t
he
Gau
ssia
n fil
ter
CSS ImageCurvature zero-crossing segments
Input image
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Matching
• Let be the maxima of the query
Where Arc length parameter
= scale parameter, such that
Let be the maxima of the database shape Where Arc length parameter = scale parameter, such that
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Illustration
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Binary Object Features – Area
• The area of the ith object is defined as follows:
• The area Ai is measured in pixels and indicates the relative size of the object.
1
0
1
0
),(width
ci
height
ri crIA
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Binary Object Features – Center of Area
• The center of area is defined as follows:
• These correspond to the row and column coordinate of the center of the i-th object.
1
0
1
0
1
0
1
0
),(1
),(1
width
ci
height
rii
width
ci
height
rii
crcIA
c
crrIA
r
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Binary Object Features – Axis of Least Second Moment
• The Axis of Least Second Moment is expressed as - the angle of the axis relatives to the vertical axis.
1
0
21
0
1
0
21
0
1
0
1
01
),()(),()(
),())((2tan2
1width
ci
height
r
width
ci
height
r
width
ci
height
ri
crIcccrIrr
crIccrr
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Binary Object Features – Axis of Least Second Moment
• This assumes that the origin is as the center of area.
• This feature provides information about the object’s orientation.
• This axis corresponds to the line about which it takes the least amount of energy to spin an object.
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Binary Object Features – Aspect Ratio
• The equation for aspect ratio is as follows:
• reveals how the object spread in both vertical and horizontal direction.
• High aspect ratio indicates the object spread more towards horizontal direction.
1
1
minmax
minmax
rr
cc
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