Segmentation and Perceptual Grouping Kaniza (Introduction to Computer Vision, 11.1.04)
-
date post
21-Dec-2015 -
Category
Documents
-
view
297 -
download
0
Transcript of Segmentation and Perceptual Grouping Kaniza (Introduction to Computer Vision, 11.1.04)
Segmentation and Perceptual Grouping
Kaniza
(Introduction to Computer Vision, 11.1.04)
The image of this cube contradicts the optical image
Perceptual Organization
• Atomism, reductionism:– Perception is a process of decomposing an
image into its parts.– The whole is equal to the sum of its parts.
• Gestalt (Wertheimer, Köhler, Koffka 1912)– The whole is larger than the sum of its parts.
Gestalt: apparent motion
Gestalt: apparent motion
Gestalt Principles
• Proximity
Gestalt Principles
• Proximity• Proximity• Similarity
Gestalt Principles
• Proximity• Similarity
• Proximity• Similarity• Continuity
Gestalt Principles
• Closure• Proximity• Similarity• Continuity
Gestalt Principles
• Proximity• Similarity• Continuity
• Closure• Closure• Common Fate
Gestalt Principles
• Proximity• Similarity• Continuity
• Closure• Common Fate• Simplicity
• Closure• Common Fate
Mona Lisa
Mona Lisa
Smooth Completion
• Isotropic
• Smoothness
• Minimal curvature
• Extensibility
Elastica:
2min ( )k s ds
Elastica
• Scale invariant (Weiss, Bruckstein & Netravali)
• Approximation (Sharon, Brandt & Basri)
2 21 2 1 24( )
2min ( )l k s ds
(Sharon, Brandt & Basri)
Hough Transform
Hough Transform
Curve Salience
Saliency Network
Encourage
• Length
• Low curvature
• Closure
(Shashua & Ullman)
Saliency Network(Shashua & Ullman)
Tensor Voting
• Every edge element votes to all its circular edge completions
• Vote attenuates with distance: e-αd
• Vote attenuates with curvature: e-βk
• Determine salience at every point using principal moments
(Guy & Medioni)
Tensor Voting(Guy & Medioni)
Stochastic Completion Field
• Random walk:
• In addition, a particle may die with probability:
2
cos
sin
(0, )
x
y
N
1/ re
(Mumford; Williams & Jacobs)
Stochastic Completion Fields
• Most probable path:
with
2
2
( )
1
21
log( 2 )
k s ds ds
r
(Mumford; Williams & Jacobs)
Stochastic Completion Fields(Mumford; Williams & Jacobs)
Stochastic Completion Fields(Mumford; Williams & Jacobs)
Stochastic Completion Fields(Mumford; Williams & Jacobs)
Shortest Path(Hu, Sakoda & Pavlidis)
Snakes
• Given a curve Г(s)=(x(s),y(s)), define:1
0
int
22 2
int 2
( ( ))
( ( ))
( , )
( ) ( )
image ext
image
E s ds
E s E E E
E I x y
E s ss s
(Kass, Witkin & Terzopolous)
Snakes: Curve Evolution
Snakes: Curve Evolution
Thresholding
Histogram
0 50 100 150 200 250
0
200
400
600
800
1000
1200
Thresholding
Thresholding
125
15699
Image Segmentation
Camouflage
Minimum Cut(Wu & Leahy)
Texture Examples
Filter Bank(Malik & Perona)
Normalized Cuts(Malik et al.)
Segmentation by Weighted Aggregation
A multiscale algorithm:• Optimizes a global measure• Returns a full hierarchy of segments• Linear complexity• Combines multiscale measurements:
– Texture– Boundary integrity
(Galun, Sharon, Brandt & Basri)
Segmentation by Weighted Aggregation(Galun, Sharon, Brandt & Basri)
Leopards
And More…
Malik’s Ncuts