[] Genetic Algorithms and Image Understanding

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Genetic Algorithms

and Image Understanding

Sam Clanton

Computer Integrated Surgery II

March 14, 2001

Resources


Bhanu, Bir and Lee, Sunkee.
Genetic Learning for
Adaptive Image Segmentation.

Kluwer Academic
Publishers, 1994


Goldberg, David.
Genetic Algorithms in Search,
Optimization, and Machine Learning.

Addison Wesley
Longman, 1989.

Genetic Algorithms


Optimization Problems


Adaptive Systems


Speed
-
Critical Applications


Are Useful For…

General Problem to be Solved


The k
-
armed bandit problem

Picture: Goldberg

How do we maximize our
winnings?


GA’s are good for multiple,

many
-
armed bandits.

What Is a Genetic Algorithm?


Operates on principle of


survival of the fittest


“Population Pool” of Parameters


Genetic Operators
-

Reproduction,
Crossover, and Mutation


Survival Of the Fittest


Analogous to survival in biological system


Fitness Function


Optimization == Finding most fit parameter
set for a particular problem


S
elk
(an elk)

~ Ability to run away (elk, lions, tigers)


Ability to run away (herd, lions, tigers)

Spset(a pset) ~ Ability to perform task(pset, input)


Ability to perform task(population, input)

Population Pool

24, 32, 76, 1

34, 43, 6, 17



“Surviving” parameter sets are kept around




Individuals are extracted and applied when input resembles


past input for that individual.



Genetic operators add


new individuals to pool




Individuals can be


dropped when they


appear useless

Genetic Operators


Affect survival of particular schema




Schema
-

string representation of a feature



Reproduction f(H) / f
avg


Crossover 1
-

p
c

* L(H) / L(total)


Mutation 1
-

L(H) * p
m

Feature Preservation


Overall Equation


m(H, t+1) = m(H, t) *

F(H)/f
avg

Reproduction

* (1
-

p
c

L(H) / L(tot)

Crossover

-

L(H) * p
m

)

Mutation

An Example
-

Reproduction

String

Initial
Pop

X val

F(x) = x
* x

Pselect
(fitness/
total
fitness)

Exp.
Count

(fitness /
avg
fitness)

Actual
Count
(roulette)

1

01101

13

169

.14

.58

1

2

11000

24

576

.49

1.97

2

3

01000

8

64

.06

.22

0

4

10011

19

361

.31

1.23

1

Sum

1170

Avg

293

Max

576

An Example
-

Crossover

String

Pop. Pool
(w/

Crossover)

Mate

Crossover
Site

New Pop.
Pool

X value

F(x)

1

0110|1

2

4

01100

12

144

2

01100|0

1

4

11001

25

625

3

11|000

4

2

11011

27

729

4

10|011

3

2

10000

16

256

Sum

1754

Avg

439

Max

739

GA’s in Image Segmentation


Optimization problem


“Twiddling Knobs” Approach


Relationship to “Many k
-
armed bandit” problem





Figure: Bhanu, Lee

GA method for image
segmentation

Figure: Bhanu, Lee

Images differ in
characteristics such as
brightness, saturation,
skewness, entropy, etc.

Use these values as inputs
to genetic algorithm

Figure: Bhanu, Lee

Image Analysis

Evolution of Segmentation
System

Figure: Bhanu, Lee

`

The project: Implementation in
DSP / FPGA

Image


Capture

Image

Processor

Genetic

optimizer


Collator

Memory

Output

Edge Detectors

FPGA

DSP