ROOF CONTOURS RECOGNITION USING LIDAR DATA AND MARKOV RANDOM FIELD MODEL ON GRAPH THEORY

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18 Οκτ 2013 (πριν από 3 χρόνια και 11 μήνες)

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UNIVERSIDADE ESTADUAL DE MATO GROSSO

FACULDADE DE CIÊNCIAS EXATAS

CAMPUS DE
BARRA

DO
BUGRES

ROOF CONTOURS RECOGNITION USING
LIDAR DATA AND MARKOV RANDOM FIELD
MODEL ON GRAPH THEORY

Author

Edinéia Aparecida dos Santos Galvanin

Aluir Porfírio Dal Poz


LiDAR

technology

has

become

common

in

recent

years,

allowing

rapid

and

efficient

acquisition

of

the

Digital

Elevation

Models



Motivation


Methodology

Results

Conclusion


Object

segmentation

in

urban

areas,

due

to

scene

complexity,

requires

the

development

of

specific

methods

that

integrate

the

neighborhood

information

and

the

domain

knowledge

of

characteristics

of

the

interest

objects


Introduction

Methodology

Results

Conclusion


Object

extraction

and

generation

of

Digital

Terrain

Model



Introduction

Methodology

Results

Conclusion


This

paper

proposes

a

methodology

for

automatic

extraction

of

building

roof

contours

using

a

graph
-
based

MRF
.


Introduction

Methodology

Results

Conclusion


Main

advantage

is

to

provide

a

general

and

natural

model

for

the

interaction

among

spatially

related

random

variables

in

the

image
.

The

proposed

methodology

comprises

the

following

preprocessing

steps
:



Introduction

Methodology

Results

Conclusion


High regions

roof properties definition

Energy function

Minimization

Stability

Roof contours

yes

not

Recursive

splitting

region

segmentation,

region

merging

,

vectorization

and

polygonization


Introduction

Methodology

Results

Conclusion



Introduction

Methodology

Results

Conclusion


High regions

roof properties definition

Energy function

Minimization

Stability

Roof contours

yes

not


Introduction

Methodology

Results

Conclusion


Using

the

available

contours,

a

region

adjacency

graph

(RAG)

is

constructed

The

neighbourhood

,regions

neighboring

is

defined

as,



Introduction

Methodology

Results

Conclusion


The

construction

of

the

energy

function

depends

on

a

prior

knowledge

of

the

properties

of

the

object

´
roof
´
.

The

features

for

the

first

order

clique

used

is

the

area

and

rectangularity
.

The

area

feature

allows

small

object

in

relation

to

roofs,

can

be

discarded
.


Introduction

Methodology

Results

Conclusion


The

third

attribute

allows

the

verification

of

parallelism

or

perpendicularity

between

objects

Because

if

either

(objects

with

parallel

main

axes)

or

if

(objects

with

perpendicular

main

axes)
.


Introduction

Methodology

Results

Conclusion


High regions

roof properties definition

Energy function

Minimization

Stability

Roof contours

yes

not


Introduction

Methodology

Results

Conclusion


weights that gives relative importance to each term
of energy

rectangularity parameter of the object
.

the area of object

angle between the main axes of objects


Introduction

Methodology

Results

Conclusion


High regions

roof properties definition

Energy function

Minimization

Stability

Roof contours

yes

not


Introduction

Methodology

Results

Conclusion


three
-
dimensional visualization of the DEM used in
the test


Introduction

Methodology

Results

Conclusion


The

extracted

polygons

are

overlaid

in

red

on

the

intensity

image
.

This

figure

also

shows

the

reference

polygons

(in

blue)

and

a

false

negative

(in

green)
.




Introduction

Methodology

Results

Conclusion


The

choice

of

test

area

took

into

account

the

complexity

of

the

configurations

of

objects

in

the

scene

In

general,

a

good

indication

of

robustness

of

the

proposed

methodology

was

the

lack

of

false

positives

and

the

verification

of

few

false

negatives
.

The

completeness

parameters

showed

that

the

extracted

polygons

generally

have

high

superposition

with

their

reference

polygons
.


REFERENCES


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,

R
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C
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,

Jain,

A
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K
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,

1989
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Random

Field

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Image

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Journal

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Statistics,

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16
,

n
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2
,

pp
.

131

164
.


Haala,

N
.
,

Brenner,

C
.
,

1999
.

Extraction

of

buildings

and

trees

in

urban

environments
.

ISPRS

Journal

of

Photogrammetry

e

Remote

Sensing,

v
.
54
,

pp
.
130
-
137
.


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R
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Kasturi
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R

&

Schunck
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B
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G
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1995
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Machine

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MIT

Press

and

McGraw
-
Hill,

Inc

New

York
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Kinderman
,

R
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,

Snell,

J
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L
.

1980
.

Markov

Random

Fields

and

their

applications
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Providence,

R
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I
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American

Mathematical

Society
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Kirkpatrick,

S
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,

Gelatt
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C
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D
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,

Vecchi
,

M
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P
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1983
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by

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Science,

pp
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Kopparapu,

S
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K
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,

Desai,

U
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B
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approach

to

image

interpretation
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127
p
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THANKS FOR ATTENTION