basic binary segmentation

Report
Tutorial on Medical Image Segmentation: Beyond Level-Sets
MICCAI, 2014
Yuri Boykov, UWO
Western University
Canada
www.csd.uwo.ca/faculty/yuri/miccai14_MIS
1: Basics of optimization-based segmentation
- continuous and discrete approaches
2 : Exact and approximate techniques
- non-submodular and high-order problems
3: Multi-region segmentation (Milan)
- high-dimensional applications
Yuri Boykov, UWO
Introduction to Image Segmentation
Part 1

implicit/explicit representation of boundaries
• active contours, level-sets, graph cut, etc.

Basic low-order objective functions (energies)
• physics, geometry, statistics, information theory
Part 2

Set functions, submodularity
• Exact methods

Approximation methods
• Higher-order and non-submodular objectives
• Comparison to gradient descent (level-sets)
Yuri Boykov, UWO
Thresholding
T
Yuri Boykov, UWO
Thresholding
T
S={ p : Ip < T }
Yuri Boykov, UWO
Thresholding
-
=
Background
I= Iobj - Ibkg
Subtraction
?
Threshold intensities above
T
better segmentation?
Yuri Boykov, UWO
Good segmentation S ?

Objective function must be specified
Quality function
Cost function
Loss function
E(S) : 2P  
“Energy”
Regularization functional
Segmentation becomes an optimization problem:
S = arg min E(S)
Yuri Boykov, UWO
Good segmentation S ?

Objective function must be specified
Quality function
Cost function
Loss function
E(S) = E1(S)+…+ En(S)
“Energy”
Regularization functional
combining different constraints
e.g. on region and boundary
Segmentation becomes an optimization problem:
S = arg min E(S)
Yuri Boykov, UWO
Beyond linear combination of terms

Ratios are also used
•
•
•
•
E1( S )
E( S ) 
E2 ( S )
Normalized cuts [Shi, Malik, 2000]
Minimum Ratio cycles [Jarmin Ishkawa, 2001]
Ratio regions [Cox et al, 1996]
Parametric max-flow applications [Kolmogorov et al 2007]
Not in this tutorial
Yuri Boykov, UWO
Segmentation principles
interactive

Boundary seeds
vs.

• Livewire (intelligent scissors) 

Region seeds

• Graph cuts (intelligent paint)

• Distance (Voronoi-like cells)

Bounding box
• Grabcut [Rother et al]

Center seeds
• Star shape [Veksler]

Many other options…

unsupervised
Normalized cuts [Shi Malik]
Mean-shift [Comaniciu]
MDL [Zhu&Yuille]
Entropy of appearance
Add enough constraints:
 Saliency
 Shape
 Known appearance
 Texture
Yuri Boykov, UWO
1. We won’t cover everything…
2. We will not emphasize the differences between interactive and unsupervised
(too easy to convert one into the other)
interactive

Boundary seeds
vs.

• Livewire (intelligent scissors) 

Region seeds

• Graph cuts (intelligent paint)

• Distance (Voronoi-like cells)

Bounding box
• Grabcut [Rother et al]

Center seeds
• Star shape [Veksler]

Many other options…

unsupervised
Normalized cuts [Shi Malik]
Mean-shift [Comaniciu]
MDL [Zhu&Yuille]
Entropy of appearance
Add enough constraints:
 Saliency
 Shape
 Known appearance
 Texture
Yuri Boykov, UWO
Common segmentation techniques
boundary-based
region-growing
intelligent scissors
(live-wire)
region-based
thresholding
both region & boundary
geodesic
active contours
(e.g. level-sets)
MRF
active contours
(snakes)
(e.g. graph-cuts)
watersheds
random walker
optimization-based
Yuri Boykov, UWO
Common surface representations
continuous
optimization
sp 
mesh
level-sets
combinatorial
optimization
mixed
optimization
s p {0,1}
sp  Z  p 
graph
labeling
point cloud
labeling
on complex
on grid
Yuri Boykov, UWO
Active contours (e.g. snakes)
[Kass, Witkin, Terzopoulos 1987]
Given: initial contour (model) near desirable object
Goal: evolve the contour to fit exact object boundary
Yuri Boykov, UWO
Tracking via active contours
Tracking Heart Ventricles
5-14
Yuri Boykov, UWO
Active contours - snakes
Parametric Curve Representation (continuous case)
A curve can be represented by 2 functions

C  {ν(s) | s [0,1]}  
ν(s)  ( x(s), y(s))
open curve
parameter
0  s 1
closed curve
5-15
Yuri Boykov, UWO
Snake Energy
E( C )  Ein( C )  Eex( C )
internal energy encourages
smoothness or any particular shape
external energy encourages curve onto
image structures (e.g. image edges)
5-16
Yuri Boykov, UWO
Active contours - snakes
(continuous case)

internal energy (physics of elastic band)
1
1
2
2
Ein ( C )     dν ds     d 2 ν ds
ds
d s
0
0
elasticity / stretching

2
stiffness / bending
external energy (from image)
1
Eex ( C )    | I (v( s)) |2 ds
0
proximity to image edges
5-17
Yuri Boykov, UWO
Active contours – snakes
(discrete case)
elastic energy
(elasticity)
d vi 1  i 1

ds
2
v3
C  (ν0 , ν1 , ν 2 ,....,ν n1 )  2n
v4
v5
νi  ( xi , yi )
v2
v1
v6
v7
v 10
v9
v8
bending energy
(stiffness)
d 2
 (  i 1   i )  (  i   i 1 )   i 1  2 i   i 1
2
ds
Yuri Boykov, UWO
Basic Elastic Snake
1
1
dv 2
2
case
E     | | ds   | I ( v( s ))| ds Ccontinuous
 { ν( s ) | s  [ 0 ,1 ]}
ds
0
0
n 1
E    | vi 1  vi | 
2
i 0
elastic smoothness term
(interior energy)
n 1
| I ( vi ) |
2
discrete case
C  { νi | 0  i  n }
i 0
image data term
(exterior energy)
5-19
Yuri Boykov, UWO
Snakes - gradient descent
C
n 1
simple elastic snake energy
E( x0 , , xn1 , y0 , , yn1 )   | I x ( xi , yi ) |2  | I y ( xi , yi ) |2
here, energy is a function of 2n variables
i 0
n 1
    ( xi 1  xi ) 2  ( yi 1  yi ) 2
i 0
update equation for the whole snake
C'  C  E  t
C
E
 x'0   x0   x0 

 

E

y
'
y

y0 
 0   0 
 ...    ...    ...   t

 
  E 
x
'
x
 n 1   n 1   xn1 
 y' n 1   yn 1   ynE1 
5-20
Yuri Boykov, UWO
Snakes - gradient descent
C
n 1
simple elastic snake energy
E( x0 , , xn1 , y0 , , yn1 )   | I x ( xi , yi ) |2  | I y ( xi , yi ) |2
here, energy is a function of 2n variables
i 0
n 1
    ( xi 1  xi ) 2  ( yi 1  yi ) 2
i 0

ν'i  νi  Fi  t
update equation for each node

Fi
C
E
 x'0   x0   x0 

 

E

y
'
y

y0 
 0   0 
 ...    ...    ...   t

 
  E 
x
'
x
 n 1   n 1   xn1 
 y' n 1   yn 1   ynE1 

 xEi 
Fi    E 
 yi 
5-21
Yuri Boykov, UWO
Snakes - gradient descent
E(C)
energy function E(C) for contours C
C  2 n
cˆ c2
local minima
for E(C)
step size
could be tricky
c1
c0
gradient descent steps
Ci 1

Ci  t  E
second derivative of
image intensities
5-22
Yuri Boykov, UWO
Implicit (region-based) surface
representation via level-sets
z  u ( x, y )
C  {( x, y) : u( x, y)  0}
(implicit contour representation)
[Dervieux, Thomasset, 79, 81] [Osher, Sethian, 89]
Yuri Boykov, UWO
Implicit (region-based) surface
representation via level-sets


dC    N
Normal contour motion can be represented by evolution of level-set function u
du   | u|
[Dervieux, Thomasset, 79, 81] [Osher, Sethian, 89]
u (x)
The scaling by  | u | is easily
verified in one dimension
du   | u | dC
dC
x
p
Note 1: - commonly used for gradient descent evolution
dC   E  dt
Note 2: - level sets can not represent tangential motion of contour points ???
Yuri Boykov, UWO
Tangential vs. normal motion
of contour points
- normal motion of a contour point visibly changes shape (geometry)
- tangential motion generates no “visible” shape change
A simple example of
tangential motion
of contour points
(rotation)
A simple example of
normal motion
of contour points
(expansion)
Comments: - geometric “energy” of a contour measures “visible” shape properties
(length, curvature, area, e.t.c.). Thus, gradient descent w.r.t. geometric
objective generates only ”visible” (normal) motion.

dC  dt  E
- level sets can represent contour gradient descent evolution
only for geometric “energies” E(C) (s.t. E is collinear with contour normal N )
Level sets
(implicit contour representation)
Geodesic active contours
Yuri Boykov, UWO
Tangential vs. normal motion
of contour points
- normal motion of a contour point visibly changes shape (geometry)
- tangential motion generates no “visible” shape change

Np
E p
Q: in what medical applications
tangential motion of
segment boundary matters?
Comments: - gradient descent for physics-based “energy” of a contour (e.g. elasticity)
may produce geometrically “invisible” tangential motion of contour points
- physics-based energy of a contour depends on its parameterization,
while geometrically it could be the same contour (compare two shapes above)
Parametric contours
(explicit contour representation)
Physics-based active contours
Yuri Boykov, UWO
Physics vs. Geometry
continuous optimization
mesh
snakes, balloons, active contours
•
•
•
•
explicit or parametric contour representation
physics-based objectives (typically)
gradient descent
could use dynamic programming in 2D
[Amini, Weymouth, Jain, 1990]
level-sets
geodesic active contours
•
•
•
•
implicit or non-parametric representation
geometry-based objectives
gradient descent
can use convex formulations (TV-based)
[Chan, Esidoglu, Nikolova 2006]
Yuri Boykov, UWO
Most common geometric functionals
for segmentation with level-sets

dC    N
Functional E( C )
weighted length
(boundary alignment to intensity edges)
E (C )   g  ds
E (C )  
(oriented boundary alignment)

 ~ g    g , N
 f
da
 ~ f
int(C )
(region alignment to appearance model)
flux
du     | u |
C
E (C ) 
weighted area
or
C
 
v, N ds

 ~ div(v)
Yuri Boykov, UWO
Most common geometric functionals
for segmentation with level-sets

dC    N
Functional E( C )
E(C)  | S |g
weighted length
or
du     | u |

 ~ g    g , N
(boundary alignment to intensity edges)
E (C ) 
weighted area
E (C )  
(oriented boundary alignment)
da
 ~ f
int(C )
(region alignment to appearance model)
flux
 f
C
 
v, N ds

 ~ div(v)
Yuri Boykov, UWO
Towards discrete geometry:
weighted boundary length on a graph
[Barrett and Mortensen 1996]
“Live wire” or “intelligent scissors”
w(| I |)
| I |
pixels
| I|
image-based edge weights
B
A
shortest path algorithm (Dijkstra)
Yuri Boykov, UWO
shortest path on a 2D graph  graph cut
Example:
find the shortest
closed contour in a given
domain of a graph
Shortest paths
approach
Graph Cuts
approach
p
Compute the shortest path
p ->p for a point p.
Repeat for all points on the
gray line. Then choose the
optimal contour.
Compute the
minimum cut that
separates red region
from blue region
Yuri Boykov, UWO
graph cuts vs. shortest paths

On 2D grids graph cuts and shortest paths give optimal 1D contours.
B
A
A Path connects points
A Cut separates regions

Shortest paths still give optimal 1-D contours on N-D grids

Min-cuts give optimal hyper-surfaces on N-D grids
Yuri Boykov, UWO
Graph cut
[Boykov and Jolly 2001]
hard
constraint
t
n-links
a cut
hard
constraint
s
Minimum cost cut can be
computed in polynomial time
(max-flow/min-cut algorithms)
 I pq 
wpq  exp  2 
 2 

I pq
Yuri Boykov, UWO
Minimum s-t cuts algorithms

Augmenting paths [Ford & Fulkerson, 1962]
- heuristically tuned to grids [Boykov&Kolmogorov 2003]

Push-relabel [Goldberg-Tarjan, 1986]
- good choice for denser grids, e.g. in 3D

Preflow [Hochbaum, 2003]
- also competitive
Yuri Boykov, UWO
Optimal boundary in 2D
“max-flow = min-cut”
Yuri Boykov, UWO
Optimal boundary in 3D
3D bone segmentation (real time screen capture, year 2000)
Yuri Boykov, UWO
‘Smoothness’ of segmentation boundary
- snakes (physics-based contours)
- geodesic contours (geometry)
- graph cuts (discrete geometry)
NOTE: many distance-to-seed methods optimize segmentation boundary only indirectly,
they compute some analogue of optimum Voronoi cells
[fuzzy connectivity, random walker, geodesic Voronoi cells, etc.]
Yuri Boykov, UWO
Discrete vs. continuous boundary cost
Geodesic contours
E ( S )   ws ds
S
Graph cuts
E(S )   wpq  [S p  Sq ]
p ,q
S p {0,1}
C
[Caselles, Kimmel, Sapiro, 1997] (level-sets)
[Chan, Esidoglu, Nikolova, 2006] (convex)
[Boykov and Jolly 2001]
[Boykov and Kolmogorov 2003]
Both incorporate
segmentation boundary smoothness and
alignment to image edges
Yuri Boykov, UWO
Graph cuts on a grid and boundary of S
B( S ) 
S p {0,1}

 |e|
eC
Severed n-links can approximate geometric length of contour C
[Boykov&Kolmogorov, ICCV 2003]

This result fundamentally relies on ideas of Integral Geometry (also
known as Probabilistic Geometry) originally developed in 1930’s.
• e.g. Blaschke, Santalo, Gelfand
Yuri Boykov, UWO
Integral geometry approach to length
2
C
a subset of lines L
intersecting contour C

LC
a set of all
lines L


0
probability that a “randomly drown” line intersects C
Euclidean length of C :
|| C ||

1
2
n

d


d

L

Cauchy-Crofton formula
the number of times
line L intersects C
Yuri Boykov, UWO
Graph cuts and integral geometry
Graph nodes are imbedded
in R2 in a grid-like fashion
C
Edges of any regular
neighborhood system
generate families of lines
{ , , , }
|| C || 
Euclidean
length
1
2
n
k
 k  k
k
the number of edges of
family k intersecting C
 || C ||gc
graph cut cost
for edge weights:
k  k
wk 
2
Length can be estimated without computing any derivatives
Yuri Boykov, UWO
Metrication errors
Euclidean
metric
“standard”
4-neighborhoods
(Manhattan metric)
Riemannian
metric
8-neighborhoods
larger-neighborhoods
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Metrication errors
4-neighborhood
8-neighborhood
Yuri Boykov, UWO
Differential
geometry
Differential vs. integral approach to
geometric boundary length
|| C || 
|| C || 
1

|

u
|
dx

0
Ct  dt
Parametric
(explicit)
contour
representation
Level-set
function
representation
Integral
geometry

|| C || 
1
2
n
L
 d  d
Cauchy-Crofton formula
implicit (region-based) representation of contours
Yuri Boykov, UWO
Implicit (region-based) surface
representation via level-sets
• Level set function u(p) is normally stored on image pixels
• Values of u(p) can be interpreted as distances or heights of image pixels
-1.7
-0.8
-0.8
0.2
A contour may be
approximated from
u(x,y) with subpixel accuracy
u( p)  u( x , y)
-0.6
-0.4
-0.5
0.5
C
-0.2
0.6
0.3
0.7
Yuri Boykov, UWO
Implicit (region-based) surface
representation via graph-cuts
• Graph cuts represent surfaces via binary labeling Sp of each graph node
• Binary values of Sp indicate interior or exterior points (e.g. pixel centers)
0
0
0
1
There are many
contours satisfying
interior/exterior
labeling of points
S p  S ( x , y)
0
0
0
1
Question: Is this a contour
to be reconstructed from
binary labeling Sp ?
C
Answer: NO
0
1
1
1
Yuri Boykov, UWO
Contour/surface representations
(summary)
Implicit (area-based)
Level sets
(geodesic active contours)
Graph cuts
(minimum cost cuts)
What else besides
boundary length |∂S| ?
Explicit (boundary-based)
Snakes
(physics-based band model)
Live-wire
(shortest paths on graphs)
Yuri Boykov, UWO
From seeds
to more general region constraints
[Boykov and Jolly 2001]
Dp (s)
t
n-links
a cut
S
w pq
S
Dp (t )
segmentation
s
assume I and I are known
“expected” intensities
of object and background
s
t
Dp (s)  | I p  I s |
Dp (t )  | I p  I t |
cost of severed t-links
E(S) =
s
|
I

I
 p |
pS
t
|
I

I
 p | +
pS
| S |
cost of severed n-links
Yuri Boykov, UWO
From seeds
to more general region constraints
[Boykov and Jolly 2001]
t
n-links
a cut
Dp (s)
S
w pq
S
segmentation
re-estimate
Dp (s)  | I p  I s |
unknown intensities
of object and background
Chan-Vese model
s
I s and I t
I s and I t could be
Block-Coord.Descent
Dp (t )
Dp (t )  | I p  I t |
cost of severed t-links
E(S, Is,It) = | I p  I s | 
pS
t
|
I

I
 p |
pS
+
| S |
cost of severed n-links
Yuri Boykov, UWO
Block-coordinate descent for

E(S , I 0 , I 1 )
0
Minimize over labeling S for fixed I , I
E (S , I 0 , I 1 ) 
 (I
0
p:S p  0
 I p )2 
 (I
1
p:S p 1
 I p )2 
w
{ pq}N
pq
1
 [S p  Sq ]
optimal L can be computed using graph cuts

0
1
Minimize over I , I for fixed labeling S
E( S, I , I ) 
0
1
 (I
p:S p  0
0
 Ip) 
2
 (I
p:S p 1
1
 Ip)
fixed for
2
S=const
  wpq  [S p  Sq ]
{ pq}N
optimal I 1, I 0 can be computed by minimizing
squared errors inside object and background segments
Iˆ 0 
1
|S |

I
p:S p  0
p
Iˆ1 
1
|S |

I
p :S p 1
p
Yuri Boykov, UWO
Chan-Vese segmentation
(binary case S p {0,1} )
E (S , I 0 , I 1 ) 
0
2
(
I

I
)


p
p :S p  0

w
{ pq}N
pq
1
2
(
I

I
)


p
p :S p 1
 [S p  Sq ]
Yuri Boykov, UWO
Chan-Vese segmentation
(could be used for more than 2 labels S p {0,1,2,...} )
can be used for segmentation, to reduce color-depth,
or to create a cartoon
E ( S , I 0 , I 1 ,...) 
0
2
(
I

I
)


p
p :S p  0

w
{ pq}N
pq
1
2
(
I

I
)
...

p
p :S p 1
 [S p  Sq ]
Yuri Boykov, UWO
Chan-Vese segmentation
(could be used for more than 2 labels S p {0,1,2,...} )
can be used for segmentation, to reduce color-depth,
or to create a cartoon
E ( S , I 0 , I 1 ,...) 
joint optimization
over S and I0, I1,…
is NP-hard
0
2
(
I

I
)


p
p :S p  0
1
2
(
I

I
)
...

p
p :S p 1
without the smoothing term, this is like
“K-means” clustering in the color space
Yuri Boykov, UWO
From fixed intensity segments
to general intensity distributions
[Boykov and Jolly 2001]
t
n-links
a cut
Dp (s)
S
w pq
S
Dp (t )
segmentation
s
Appearance models can be
given by intensity distributions
of object and background
Dp (s)   ln Pr( I p | s)
Dp (t )   ln Pr(I p | t )
cost of severed t-links
E(S) =
  ln Pr(I
pS
p
| s) 
  ln Pr(I
pS
p
| t ) + | S |
cost of severed n-links
Yuri Boykov, UWO
Graph cut (region + boundary)
Yuri Boykov, UWO
Graph cut as energy optimization for S
[Boykov and Jolly 2001]
t
n-links
a cut
Dp (s)
S
w pq
S
Dp (t )
segmentation  cut
s
S p {0,1}
E(S) =
cost(cut)
(1)D ( S)D (0)
D 
pS
p
p
p
p
pS
p
+
w
pqN
pq
 [ S p  Sq ]
cost of severed t-links
cost of severed n-links
unary terms
pair-wise terms
regional properties of
S
boundary smoothness for
S
Yuri Boykov, UWO
Unary potentials as linear term wrt. S p {0,1}
unary terms
D
p
( S p )   Dp (1)   Dp (0)
pS
p
pS
  Dp (1)  S p  Dp (0)  (1- Sp )
p
 const   Dp (1)  Dp (0) S p
p
f ( p)
f
p
 s p  f, S
p
Linear region term
analogous to
geodesic active contours
E ( S )   f
S
Yuri Boykov, UWO
Unary potentials as linear term wrt.
f,S 
f
p
 sp
p
unary terms
(linear)
Examples of potential functions f
• Log-likelihoods
f p  ln Pr ( I p )
• Chan-Vese
f p  ( I p  c)2
• Volume Ballooning f p  1
• Attention
f p  filter responseat p
Yuri Boykov, UWO
In general,…
k-arity potentials are k-th order polynomial
pair-wise terms
w
pqN
pq
 [ S p  Sq ] 
 w  S
pqN
pq
p
 (1  Sq )  (1  Sp )  Sq 
quadratic polynomial wrt.S p
Quadratic term analogous
to boundary length in
geodesic active contours
E ( S ) | S |w   ws  ds
S
Yuri Boykov, UWO
Basic (quadratic) boundary regularization
| S |w 
w
pqN
pq
 [s p  sq ]
s p {0,1}
second-order terms
(quadratic)
Examples of discontinuity penalties w
• Euclidean
boundary length
1
w pq ~
| pq|
[Boykov&Kolmogorov, 2003], via integral geometry
• contrast-weighted
boundary length
[Boykov&Jolly, 2001]
wpq ~ exp ( I p  I q )2
Yuri Boykov, UWO
Basic second-order segmentation energy
includes linear and quadratic terms
E(S)  f, S  | S |w
f
pS
p
w
pqN
pq
 [ S p  Sq ]
Yuri Boykov, UWO
Basic second-order segmentation energy
includes linear and quadratic terms
E(S)  f, S  | S |w
MAIN ADVANTAGE:
guaranteed global optimum
(t.e. best segmentation w.r.t. objective)
 (discrete case) via graph cuts
[Boykov&Jolly’01; Boykov&Kolmogorov’03]
public code [BK’2004], fast on CPU
 (continuous case) via convex TV formulations
[Chen,Esidoglu,Nikolova’06; Chambolle,Pock,Cremers’08]
public code [C. Nieuwenhuis’2014], comparably fast on GPU
NOTE: this formulation is different from basic level-sets [Osher&Sethian’89]
Yuri Boykov, UWO
Optimization vs Thresholding
E(S)
f, Sf p  B(S)
E(S)  
pS
S
thresholding
Pr(I | Fg)
e.g. graph cut [BJ, 2001]
Pr(I | Bg)
 Pr(I(p)| fg) 

f(p)  ln
 Pr(I(p)| bg) 
I
Yuri Boykov, UWO
Other examples of useful
globally optimizable segmentation objectives
Flux [Boykov&Kolmogorov 2005]
 Color consistency [“One Cut”, Tang et al. 2014]
 Distance ||S-S0|| from template shape

• Hamming, L2,… [e.g. Boykov,Cremers,Kolmogorov, 2006]

Star-shape prior [Veksler 2008]
NOT ALLOWED
Yuri Boykov, UWO
Many more example of useful
hard-to-optimize segmentation objectives
Typical Problems:

Continuous case
• Non-convexity

Typical Solutions:
gradient descent
(linearization)
+ level sets
Discrete case
• Non-submodularity (more later)
• High-order
• Density (too many terms)
to be discussed
Yuri Boykov, UWO
Examples of useful higher-order energies

Cardinality potentials
E(S) 
(constraints on segment size)
V0 | S|
E(S)

V0   s p
can not be represented as a sum of
simpler (unary or quadratic) terms
high-order
potential
|S|
V0
Yuri Boykov, UWO
Examples of useful higher-order energies

Cardinality potentials
E(S) 
(constraints on segment size)
 V0 | S| 
E(S)
2

V  s 
0
2
p
can be represented as a sum of
unary and quadratic terms
NOTE: 2nd-order
potential
|S|
V0
still difficult to optimize
(completely connected graph)
Yuri Boykov, UWO
Examples of useful higher-order energies

Cardinality potentials
E(S) 
(constraints on segment size)
 V0 | S| 
E(S)
m

V  s 
0
m
p
can not be represented as a sum of
simpler (unary or quadratic) terms
high-order
potential
|S|
V0
Yuri Boykov, UWO
Examples of useful higher-order energies
Cardinality potentials (constraints on segment size)
 Curvature of the boundary
 Shape convexity
 Segment connectivity
 Appearance entropy, color consistency
 Distribution consistency
 High-order shape moments
…

Yuri Boykov, UWO
Summary
Not-Covered:
Covered basics of:




Thresholding, region growing
Snakes, active contours
Geodesic contours
Graph cuts (binary labeling, MRF)
Implicit surface representation
Global optimization is possible
To be covered later:

High-order models





Ratio functionals
Normalized cuts
Watersheds
Random walker
Many others…

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