# [] Genetic Algorithms and Image Understanding

Τεχνίτη Νοημοσύνη και Ρομποτική

23 Οκτ 2013 (πριν από 4 χρόνια και 6 μήνες)

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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

Publishers, 1994

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

Longman, 1989.

Genetic Algorithms

Optimization Problems

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)

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.

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