AUTOMATIC FACADE IMAGE RECTIFICATION AND

EXTRACTION USING LINE SEGMENT FEATURES

Chun Liu

INRIA Rocquencourt, Domaine de Voluceau, 78153 Le Chesnay, France

Keywords:

Perspective rectiﬁcation, Line segment detection, Image segmantation, Markov random ﬁeld, Symmetry de-

tection.

Abstract:

Recently image based facade modeling has attracted signiﬁcant attention for 3D urban reconstruction because

of low cost on data acquisition and large amount of available image processing tools. In image based facade

modeling, it normally requests a rectiﬁed and segmented image input covering only the facade region. Yet this

requirement involves heavy manual work on the perspective rectiﬁcation and facade region extraction. In this

paper, we propose an automatic rectiﬁcation and segmentation process using line segment features. The raw

input image is ﬁrstly rectiﬁed with the help of two vanishing points estimated from line segment in the image.

Then based on the line segment spatial distribution and the luminance feature, the facade region is extracted

from the sky, the road and the near-by buildings. The experiments show this method successfully work on

Paris urban buildings.

1 INTRODUCTION

Urban modelingis becoming an intense research topic

because of practical interests and usages in several ar-

eas such as tourism, environment, military, economy,

etc. Among all methodologies for obtaining 3D city

representations, image based approach is preferred

most because this approach offers low cost and has a

lot of ﬂexibility. This image based approach involves

detecting and recognizing the overall structure of a

building and the individual elements from 2D ground

images, which will be used later for 3D reconstruction

of the cities.

The ﬁrst step in image based modeling procedure

(see ﬁgure 1) is to rectify the raw image against the

perspective distortion and to extract region of interest.

Upon the completion of this process, a rectiﬁed and a

much cleaner image input with less distortion and in-

terference from unrelated image content is produced.

This way, the determination of building structure and

the recognition of various building elements become

much easier and more straightforward.

However less research work has been done on the

automation of this process. This is due to the fact

that the urban scenes have added many layers of com-

plexity into the process automation. And usually this

task is left to the manual processing in most interac-

tive modeling softwares. Consequently, in the large

Figure 1: Modeling Procedure. left to right, up to down,

original image, rectiﬁed image, segmented facade image,

image semantic segmentation, geometry detection, ﬁnal

model.

scale urban modeling with numerous facade images,

the manual rectiﬁcation and extraction process can be

heavy and painful for the human modelers.

This paper describes an unsupervised automatic

process for rectiﬁcation and extraction of complex fa-

cade images in Paris urban area. In this process, we

ﬁrst extract line segments from the image and then

estimate the horizontal and vertical vanishing points.

104

Liu C..

AUTOMATIC FACADE IMAGE RECTIFICATION AND EXTRACTION USING LINE SEGMENT FEATURES.

DOI: 10.5220/0003316401040111

In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP-2011), pages 104-111

ISBN: 978-989-8425-47-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

The line segment is deﬁned as the edge with a pre-

cise length. Using the two vanishing points, the ho-

mography matrix which deﬁnes the image projection

is estimated and the image is rectiﬁed. Next in the

rectiﬁed image, the unwanted regions such as the sky,

the road and the neighbourhood area are recognized

by using the line segment spatial distribution and the

luminance features. As a result, the region of inter-

est where the building facade locates is extracted for

further analysis tasks.

The assumption and the requirement for this

method is that the facade images need to be taken in

a cloudy morning, or evening, of later Spring or early

Autumn, when the building facades are relatively il-

luminated homogeneously and less occluded by veg-

etation. In addition, the image needs to be taken with

large angle objective so the facade will be contained

in one single image. These restrictive conditions en-

sure that the facade image will preserve maximum de-

tails and different parts of the facade can be relatively

easy to differentiate in terms of geometry, color and

texture.

1.1 Related Work

The perspective rectiﬁcation and image segmentation

for natural and urban scene interpretation have been

studied intensively in computer vision.

The general procedure for the interpretation in-

volves three steps, feature extraction, feature process-

ing based on mathematical modeling of the problem

and the ﬁnal inference. The selection of features are

crucial and problem-speciﬁc. The widely used fea-

tures include shape, color and textures. Those visual

appearances are extracted straightforwardly and com-

pared directly using various error metrics. However,

in complicated urban scenes, where human made ob-

jects could be highly textured and occluded, those

visual appearance features are not suitable for direct

comparison. For instance, a blazing window glass can

reﬂect any visual content from the sky or neighbour-

hood which is interfering and not related to the true

characteristics so that two identical windows may ap-

pear different visually.

The edges, which are the discontinuities over the

borders of objects on images, can provide strong clues

for object shape and location information. This is es-

pecially apparent in man-made objects where many

rectangular surfaces exist such that horizontal and

vertical edges are often seen. For complex shape de-

tection, one can take advantage of processing edges

elaborately. Similarly, proper clustering of the edges

can be utilized to analyze the overall typology of the

man made objects such as architectures where repe-

tition and symmetry of primitive geometries are the

typical composition characteristics. In addition, the

distribution of edges could reveal other useful infor-

mation such as the orientation of the scene.

Traditionally, the detection of line segment, is

based on edge detection and the Hough trans-

form (Matas et al., 1998). And the line segment hy-

pothesis is usually validated by hard decisions on the

gap and length thresholds. As a result, this detec-

tion often produces false line segments, which can be

harmful to further edge feature processing. Rafael et

al (Grompone von Gioi et al., 2010) proposed a fast

line segment detection algorithm which provides al-

most no false line and the computation time is linear.

This algorithm takes gradient as input and searches

for line region by considering less false detection. The

output from this algorithm is close to human percep-

tion such that in case of a noisy like texture, there will

be no line segment detected. With this line segment

detector, the line segment is robust and less biased for

computer vision problems.

In processing architectural photos, the successful

perspective rectiﬁcation depends on accurate estima-

tion of vanishing points, which are intersecting points

from line segments on images. In highly rectangular

textures (Liebowitz and Zisserman, 1998), lines can

be easily extracted and rectiﬁcation is completed by

combining afﬁne rectiﬁcation with vanishing points

and metric rectiﬁcation with other priors like known

angles between lines. However, generally, such prior

information can not be obtained automatically and

needs human interaction. In general case (Kalantaria

et al., 2008), the image is rectiﬁed by using two van-

ishing points and solving the homography matrix di-

rectly. The certainty of less interference from false

line detection is provided by using RANSAC algo-

rithm. However, the rectiﬁcation is less efﬁcient due

to abundant false line segments.

The segmentation of outdoor scene could also

beneﬁt from line segment detection. Derek Hoeim

(Hoiem et al., 2005) proposed a decision tree based

machine learning algorithm for outdoor scene seg-

mentation. In his method, the direction of edges

is considered as key feature and was shown to pro-

vide useful information in differentiating objects. De-

spite the method’s general application, the amount of

manual labeling work can be terrifying. Similarly in

the unsupervised approaches, the interpretation of the

sky can rely on edge analysis (Laungrungthip et al.,

2008). In this case, the sky can be thought of as

clean regions against the ground, different to previ-

ous assumption that the sky is more blue than the

ground (Laungrungthip, 2008) (Schmitt and Priese,

2009) (Zafarifar and de With, 2006).

AUTOMATIC FACADE IMAGE RECTIFICATION AND EXTRACTION USING LINE SEGMENT FEATURES

105

The removal of the ground from the outdoor scene

is also of interest in automobile navigation. Most lit-

erature in road detection have described using photos

taken from the air (Quartulli and Datcu, 2004) or from

road vehicles in which case the road is in the center of

the image. There are less works on road detection on

static building photos because the ground-level urban

scene is too complex to analyze. Therefore, the de-

tection of road will need more computation-intensive

detection of pedestrians and cars.

The subsequent sections are organized as follows.

Section 2 discusses the line segment features and

its advantage against the conventional edge features.

Section 3 describes the perspective rectiﬁcation pro-

cedure. Section 4 presents how the facade region

is extracted from backgrounds such as the sky, the

ground and the neighbourhood. Section 5 provides

the results and the conclusion.

2 LINE SEGMENT FEATURES

Line Segment features provide more precise informa-

tion of object geometries on images. In complex ur-

ban scene, where luminance and occlusion can not be

controlled, line segments can reveal the image discon-

tinuity over the object borders with strong conﬁdence

compared to other regional features such as color and

texture. Moreover,the use of speciﬁc line segment de-

tection (Grompone von Gioi et al., 2010), has allowed

the robust estimation of object geometry. Figure 2

shows the comparison of line segment detection with

other edge detection methods.

Line segments can also be used to discover the

high level structure information of an image. The spa-

tial distributions of line segments can indicate more

clues such as the existence or the discontinuity of rep-

etition and symmetry. Figure 2 clearly shows that, the

separation for ﬂoors, tiles and facades can be deter-

mined from horizontal and vertical line segment de-

tections. Furthermore, the orientation of the scene

can also be determined since all the horizontal lines

are converging to the left. This line segments conver-

gence will in turn indicate the location of the vanish-

ing point, which is useful for perspective rectiﬁcation.

3 PERSPECTIVE

RECTIFICATION

In computer vision, the mapping of object (building

facades) 3D coordinates onto the imager of the cam-

era is a planar homography. Denote the points from

(a) Original image (b) Sobel Detector

form

(e) Horizontal line segment de-

tection

(f) Vertical line segment detec-

tion

Figure 2: Edge Detection vs Line Segment Detection.

building facades in the real world as

, and pixels on

images as

, we have the homography as below:







 ~

(1)

where h

is set to 1.0 and

are in R

= [a

]

, a is scaling factor,

,1.0]

. The homography H has eight degrees

of freedom. Therefore, eight equations are needed to

determine all the coefﬁcients. Given four point cor-

VISAPP 2011 - International Conference on Computer Vision Theory and Applications

106

respondences, image points (x

). and points in

the real world (x

), k = 0,... ,3, H will be deter-

mined.

Provided the vanishing points which we assume

to consist of two points, one is for the horizontal van-

ishing point and the other is for the vertical vanishing

point, it is possible to construct four point correspon-

dences between the real world (the scaling factor a is

set to 1.0) and image.

3.1 Rectiﬁcation Process

Considering vanishing points conﬁguration as ﬁgure

4. VP0 is the horizontal vanishing point and VP1 is

the vertical vanishing point. Then we project lines

from the vanishing points to the image. Subsequently,

they will intersect at four points around the image, A,

B, C and D. In real world, horizontal lines AC and

BD should be parallel, and so do vertical lines AB and

CD.

As a result, if we can wrap the four lines AC, BD,

AB and CD back to the four lines deﬁning the image

frame rectangles, the image will be rectiﬁed such that

all lines pointing to the same vanishing points are con-

verted back to be parallel. By applying this 2D wrap-

ping (the Z coordinate in the real world is set to 1.0),

the image is rectiﬁed. Considering this wrapping,

the four points correspondences between A, B, C,

D and four image corners, upper left, upper right,

lower left, lower right can be used to determine the

homography matrix.

3.2 Vanishing Points Estimation

The procedure starts from line segment detections.

On the facade image, the horizontal and the vertical

line segments are picked up with proper angle quota

between the line and the image width axis. The hori-

zontal lines should have angles smaller than 45

◦

while

the vertical lines should have angles larger than 45

◦

After line segment extraction, the estimation of

vanishing points is done in horizontal and vertical

direction separately but follows the same procedure.

The initial vanishing point estimation is taken by cal-

culating the mean point of intersecting points from

every non-parallel line segments. Next, the Leven-

berg Marquardt optimization is performed to estimate

the optimal vanishing point location by checking how

the vanishing point ﬁts every line segment. However,

due to image distortion, image noise, and line seg-

ment perturbation not pointing to the vanishing points

from non rectangular objects on the facades, the opti-

mization result from line segments is biased. There-

fore, we need to use RANSAC (see Algorithm 1) to

(a) original image (b) rectiﬁcation result

liers

(e) vertical line segments (f) vertical line segment inliers

(g) horizontal lines projection

from vanishing points

(h) vertical lines projection

from vanishing points

Figure 3: Perspective rectiﬁcation process.

AUTOMATIC FACADE IMAGE RECTIFICATION AND EXTRACTION USING LINE SEGMENT FEATURES

107

V P 0

V P 1

Figure 4: One conﬁguration on two vanishing points.

minimize the effect from imperfect line segment sam-

ples. From ﬁgure 3(c), 3(d), 3(e) and 3(f), it can be

seen that after RANSAC the number of line segments

is reduced (especially on the roof and the ground ﬂoor

near the car).

Algorithm 1: Line segment samples ﬁltering using

RANSAC.

Set number inliers=0, angle std=10e+5

Set angle threshold=10

◦

Set N=1000, p=0.99

for i = 0 to N do

Randomly select half of the line segments

Calculate vanishing point using LM optimiza-

tion

Construct auxiliary line segment from vanishing

point and middle point of the line segment

Calculate the angle between the line segment and

auxiliary line segment for all line segments

if angle<angle threshold then

set it as inlier

end if

Calculate angle standard deviation for all inliers

if (number of inliers > number inliers) k

(number of inliers==number inliers && in-

lier angle std < angle std) then

update inliers

update angle std

end if

Set e = 1− number inliers/total lines

Set N = 1og(1− p)/log(1 − (1− e)

)

end for

3.3 Region of Interest Extraction

The next step is to extract the region of interest from

the rectiﬁed image, which is the central region con-

taining only the facade. (See ﬁgure 5) The procedure

involves calculating new positions of four points of

original image corners (upper left, upper right, lower

left and lower right) in the rectiﬁed image. Connect-

ing those four points in the rectiﬁed image will deﬁne

a viewable quadrilateral region which is seen from the

Figure 5: Region of interest extraction.

camera with no black pixel. From those four points,

we can obtain a maximum rectangle inside the quadri-

lateral which is the region of interest.

4 FACADE EXTRACTION

The facade extraction process consists of several

steps. In this process, the sky above, the road under-

neath, and the neighbourhoodbuilding facades will be

removed.

4.1 Sky Removal

4.1.1 Luminance Model

We assume the image can be divided into two parts

based on the luminance distribution. One is bright as

the sky and the other is dark as the facade beneath.

Based on the large number law, the luminance dis-

tribution on the sky area and on the facade area will

follow Gaussian distribution. It could be considered

that every facade has its own unique facade wall dec-

oration scheme, which deﬁnes consistent material use

and color painting. Hence, the amount of light re-

ﬂected from the facade wall should be around a mean

value and perturbed with a certain deviation because

the surface of the facade is not ﬂat. Moreover, the sky

could be considered as white regions with deviation

from the soft cloud shades. Under extreme case, the

sky is totally white which will have a mean value of

maximum luminance and zero variance.

The problem of estimating the parameters of ﬁnite

Gaussian mixture models has been extensively stud-

ied. We use the Expectation-Maximization method

(Martinez, 2001) for estimating the means and vari-

ations, which is run iteratively. In each iteration, the

mixing coefﬁcients, the means and variances are com-

puted. The iteration stops when it reaches the maxi-

VISAPP 2011 - International Conference on Computer Vision Theory and Applications

108

mum iteration or the error metrics reach the speciﬁca-

tion.

4.1.2 Image Segmentation using MRF

After obtaining two groups of Gaussian parame-

ters, the next step is to segment the image into sky

and non-sky regions. This can be considered as

a binary labeling problem. We deﬁne the labeling

cost as using Gaussian distribution density functions

with obtained Gaussian parameters. When we de-

note the sky luminance Gaussian distribution den-

sity function as N(µ

sky

,σ

sky

), and the facade region

luminance Gaussian distribution density function as

N(µ

facade

,σ

facade

), for a pixel with pixel value v, the

cost of labeling it as sky is N(v− µ

sky

,σ

sky

) and the

cost of labeling it as facade is N(v− µ

facade

,σ

facade

the optimal labeling is achieved by minimizing the

overall cost over all pixels by using Belief Propaga-

tion (Szeliski et al., 2008) by considering of the spa-

tial consistency in four clique connections (up, down,

left and right).

4.1.3 Skyline Extraction

After getting the binary segmented image, the sky line

needs to be extracted. The ﬁrst step is to apply mor-

phology operation on the binary image to ﬁll in some

holes. Then the border curve between the sky and

facade is extracted. The ﬁnal position is ﬁxed by av-

eraging y coordinate of the border curve.

4.2 Road Removal

The road area is usually painted in dark gray, blue, or

red. And it is less textured and decorated with white

lanes. From ﬁgure 6, it can be seen that the line seg-

ments on the roads are more likely horizontal while

on the building facades, line segments are both hori-

zontal and vertical. Therefore, it is possible to use the

direction of line segments to detect the road area.

4.2.1 Line Segment Model

We consider that below the road line position, there

are less vertical line segments on the image. To deﬁne

horizontal line segment density function along Y axis,

the number of horizontal line segments below each

vertical position is being counted and being divided

by the total horizontal line segments quantity. The

vertical line segment density function is also deﬁned

in this way. Then an object function for ﬁnding the

road line can be deﬁned as the ratio of two density

functions in energy form as the shown below.

Figure 6: Horizontal and vertical line segments distribution

for road line hypothesis.

E(y) = E

(y)/E

(y) (2)

where E

(y) is deﬁned as the ratio of the number of

horizontal line segments n

(y) below y to the total

number of horizontal line segments N

, and E

(y) is

deﬁned the ratio of the number of vertical line seg-

ments n

(y) below y to the total number of vertical

line segments N

(y) = e

(y)/N

(y) = e

(y)/N

(3)

4.2.2 Line Segment Proﬁling and Road Line

Extraction

The energy function is evaluated vertically from the

image top to the bottom to construct an energy pro-

ﬁle. Then the obtained energy proﬁle could be further

smoothed to remove the small noises. From the pro-

ﬁle (Figure 6, the most right image), the energy is

shown to be high on horizontal ledges and on road

line as expected.

The last local maximum on the energy proﬁle

should correspond to the road line because the road

is always on the bottom of the image. The ﬁnal posi-

tion for the road line is ﬁxed by averaging Y position

from all the horizontal line segments below last local

maximum.

AUTOMATIC FACADE IMAGE RECTIFICATION AND EXTRACTION USING LINE SEGMENT FEATURES

109

4.3 Neighbourhood Removal

The last step is to extract the facades from the neigh-

bourhood on its shoulder. On the image, the deﬁni-

tion of facade vertical border can be quite ambiguous.

Rain water pipe is a weak clue of the vertical separa-

tion under same cases. Here we take a strong assump-

tion on the building layout that horizontal consistency

breaks at building borders which works only in most

Parisian urban areas.

4.3.1 Horizontal Line Segment Distribution

Discontinuity as Symmetry Line and

Facade Border Clues

Windows on facades are repeated horizontally. Con-

sequently, the horizontal edges imposed by window

top borders are also repeated. In the gaps between

the windows horizontal edges, transitional symmetric

lines and facade vertical borders are located. On the

window transitional symmetric lines, the window ar-

rangements on both the left and the right are similar.

On the facade vertical borders, the windows arrange-

ment on the left and on the right are different (asym-

metric). (see Figure 7).

Figure 7: Horizontal line segments distribution.

We extract those gap positions as the hypothesis of

facade separation lines. In order to locate the gaps, the

horizontal line segment image is summed up along Y

axis such that the X proﬁle is obtained. After proper

smoothing on this horizontal line segment proﬁle, the

local minima which correspond to the gaps between

windows are extracted.

Afterwards, at each horizontal position, we mea-

sure the symmetry on its shoulders. We ﬁrst sum up

the horizontal line segments in a certain region on

each horizontal position’s shoulder along X axis, to

the left and to the right separately to get two Y pro-

ﬁles. From two proﬁles on the left (a) and on the right

(b), we can measure the symmetry. If the position is

considered as an asymmetric line, it will be accepted

as a facade border line. The symmetry measure is de-

ﬁned as follows.

Symmetry =

∑

δ(a

) ∗ |a

+ b

∑

(4)

∑

δ(a

) ∗ |a

+ b

| sums the line segments sym-

metric to the separation line hypothesis. If the proﬁles

are mostly different, there is less chance the line seg-

ments are aligned vertically from left and from right

of the separation line, so less is contributed to the nu-

merator of this symmetry measure. An empirical hard

threshold of 0.5 is used that value no larger than 0.5

will be considered as asymmetric.

4.3.2 Border Line Determination and Facade

Extraction

The borders between facades are extracted based on

the following procedure. If two distinct asymmetric

lines are found on the image border, they are treated

as the facade borders. If one of them is missing or no

asymmetric line is found, vertical line segment proﬁle

along the whole image is checked to pick up a position

where a strong vertical line exists that is longer than

one fourth of the image height. This vertical line is

usually located where the rain water pipe is. If no

strong lines are found, the image border is set to be

the facade border.

5 RESULTS AND CONCLUSIONS

We havesuccessfully applied our proposed method on

all of the Haussmannian facades on the Soufﬂot Street

in Paris.

We evaluate the rectiﬁcation and extraction sep-

arately. The rectiﬁcation is assessed by measuring

mean angle of horizontal line segments and mean ab-

solute difference between angle of vertical line seg-

ments and 90 degree in radians. And the extraction

is evaluated by measuring the ratio of the intersec-

tion area against the union area from automatically

extracted region and a ground truth region done man-

ually. Results on four buildings (No. 11, 12, 19 and

26) are shown in the below table and Figure 8. The

running time for rectiﬁcation and segmentation is re-

lated to the number of line segments detected in im-

ages and also the image size. For a typical facade im-

age in 518×778, it takes less than 2 minutes for rec-

tiﬁcation and extraction on a machine with 2.5GHz

Dual-core processer and 2G RAM.

The main contributions of this paper are twofolds.

One is we have proved that properly modeling of the

VISAPP 2011 - International Conference on Computer Vision Theory and Applications

110

Table 1: Error metrics.

b11 b12 b19 b26

Angle

0.045 0.046 0.037 0.044

Angle

0.035 0.037 0.041 0.048

Overlapping 85.43% 83.04% 92.78% 95.00%

Figure 8: Results on Soufﬂot Street Facades. Left to right,

original, rectiﬁed, cropped, ﬁnal and ground truth.

distribution of line segments will help the interpreta-

tion of the scene. The second contribution is that we

presented fast efﬁcient methods of rectiﬁcation and

segmentation process which require less human inter-

actions, and proved to be useful for many urban mod-

eling and urbanism applications as well.

One of the limitations of our current method, how-

ever, is that it is scale sensitive. Another limitation

is in the road detection which still depends on the as-

sumption that the road should be rather clean with less

occlusion from cars.

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