Abstract— The paper presents a Computer Vision System

based on texture segmentation and on a variation of the

Standard Hough Transform, in which the choice of the

parameters that determine the straight line that better

represents the image is based on the contour conditions of the

particular case of weld line detection on fuel storage tanks,

aiming to allow their correct detection by the computer vision

system even in the absence of the reinforcement structure,

usually found in those cases. The proposed vision system

provides the necessary information to keep a set of ultrasonic

sensors, used to inspect the weld line, in the necessary position

in order to improve the inspection reliability.

I. INTRODUCTION

he increasing search for security in the industrial sector,

together with the necessity of quality control, stimulates

the accomplishment of great investments in inspection. In

this context, automatic inspection systems had become a

reality in this sector. They made possible a more trustworthy

inspection, minimizing the human error and carrying out

such processes faster and with lower costs [1], [2].

This need is even more critical when considering the

inspection in storage tanks of harmful products to the health

and the environment. To guarantee the security of the

employees and to prevent aggressions to the environment,

without affecting the health of the population that inhabits

Manuscript received June 18, 2008. This work was supported in part by

CENPES/PETROBRAS, CAPES and CNPq. Lucas Molina is with the Electrical Engineering Nucleus of Federal

University of Sergipe – NEL/UFS, Av. Marechal Rondon, S/N, São

Cristóvão-SE, Brazil, 49100-000. He is also a Masters student at the COPPE of the Federal University of Rio de Janeiro – COPPE/UFRJ, Cidade

Universitária, Ilha do Fundão, Rio de Janeiro-RJ, Brazil, 21945-970 (e-

mail:lmolina@ufs.br). Elyson A. N. Carvalho is with the Electrical Engineering Nucleus of

Federal University of Sergipe – NEL/UFS, Av. Marechal Rondon, S/N, São

Cristóvão-SE, Brazil, 49100-000. He is also a PhD. student at the Electrical Engineering Departament of the Federal University of Campina Grande –

DEE/UFCG, Av. Aprígio Veloso, 882, Bodocongó, Campina Grande-PB,

Brazil, 58109-900 (e-mail: ecarvalho@ufs.br). Eduardo O. Freire is with the Electrical Engineering Nucleus of Federal

University of Sergipe – NEL/UFS, Av. Marechal Rondon, S/N, São

Cristóvão-SE, Brazil, 49100-000 (phone: +55-79-2105-6834; fax: +55-79-2105-6684; e-mail: efreire@ufs.br).

Jugurta R. Montalvão Filho is with the Electrical Engineering Nucleus of

Federal University of Sergipe – NEL/UFS, Av. Marechal Rondon, S/N, São Cristóvão-SE, Brazil, 49100-000 (phone: +55-79-2105-6834; fax: +55-79-

2105-6684; e-mail: jmontalvao@ufs.br).

Flávio de A. Chagas is with the Electrical Engineering Nucleus of Federal University of Sergipe – NEL/UFS, Av. Marechal Rondon, S/N, São

Cristóvão-SE, Brazil, 49100-000. He is an under-graduate student at the

NEL/UFS (e-mail: flavio.chagas@yahoo.com.br).

next to the industries, frequent inspections of such tanks are

carried out, over all in its weld lines, as it is the case of the

used spherical tanks in the gas storage.

The inspection of such tanks must to be made inside and

outside of them. When inspecting its interior, the

professional in charge for it is exposed to an even bigger

danger, as the environment may present traces of the

previously stored substance, thus the risk of explosion may

be imminent, and/or the environment atmosphere may be

toxic or with an insufficient concentration of oxygen to

support the human life.

The spherical tanks usually have big dimensions, forcing

the in-charged professional to work in high places, thus,

making the job even more dangerous and thus increasing the

costs to the company.

One way to carry out the weld line inspection is based on

the emission of high-frequency ultrasonic waves [3], [4].

The ultrasonic waves are emitted under different angles

through the reservoir structure, and propagate themselves

around it.

When air bubbles are inside the weld, the wave is

reflected, thus allowing for their detection. This kind of fail

detection is already being used and is not the focus of this

work. However, the reliability of this fail detection scheme

is hardly affected when the correct positioning of the set of

ultrasonic sensors is not guaranteed [3], [4]. This is the

reason that makes the correct detection of the weld line a

theme of major importance, and it is also the inspiration to

the realization of this work.

To carry out a reliable inspection it is necessary to exactly

identify the weld line, thus obtaining the necessary data to

position the inspection-sensor correctly.

The selection of the sensing methods to be used to

identify the weld line is of major importance to assure the

inspection reliability. In [5], an autonomous system to keep

the correct positioning of the inspection sensors with respect

to the weld line is proposed. The weld line detection, in this

case, is made based on the information provided by a linear

arrangement of distance infrared sensors. In the absence of

the reinforcement structure commonly found in weld line the

proposed sensing scheme is strongly affected, but the weld

line profile can still be correctly identified if visual

information is used instead of distance measurements.

In face of such necessity of the industrial sector, this

paper presents a sensing system based on computer vision to

allow the correct detection of the weld line even in the

absence of reinforcement structures.

A Robotic Vision System Using a Modified Hough Transform to

Perform Weld Line Detection on Storage Tanks

Lucas Molina, Elyson A. N. Carvalho, Eduardo O. Freire, Jugurta R. Montalvão-Filho, and Flávio de

A. Chagas

T

The paper is organized as follows: Section 2 is ab

classical method of straight line detection applied to images

of real weld lines; Section 3 is dedicated to the method

proposed in this paper, which takes into account the contour

conditions of the particular case of weld line detection.

analysis of the obtained results, conclusions and propositions

for the future are presented in Section 4.

II. STANDARD HOUGH TRANSFORM

The Standard Hough Transform (SHT)

method to detect curves in binary images, widely used and

studied by several authors. An overview about this subject is

found in [6].

The weld line may be considered as a straight line, which

can be equated in several ways. The most suitable one is the

parametric equation of the straight line:

θθρ sincos yx +=

Using this equation it is possible to write equations to

straight lines of any orientation, what is not possible when

using the general equation of the straight line:

baxy +=

To the straight lines parallel to the y axis,

is infinite and a mathematical description to them cannot be

obtained using the general equation. Therefore, the

parametric equation is commonly used in the

implementation of the SHT.

The input to the SHT is a binary image. A simple way

obtain it consists in the use of a thresholding algorithm, as

presented in [7]. To optimally determine such threshold,

according to the entropy criterion, will be used the method

proposed by [8], [9], based on the unidimensional entropy

maximization of the histogram of the gray level image.

gray level image of the weld line used to illustrate the results

of this work was captured using a VGA resolution camera

and is shown in Fig. 1.

The unimodal characteristic of the histogram of the gray

level image, presented in the Fig. 2.a, is common for images

of weld lines, where normally it has concentration of the

gray levels in a narrow band of values, making difficult the

segmentation, even using an optimal method for the choice

of a global threshold [8].

For the choice of the threshold, the method proposed by

Kapur [10] is applied to the histogram presented in Fig. 2a.

Firstly, the histogram is equalized according to the method

proposed in [11], in order to increase the contrast and to

facilitate the segmentation. If the gray level intensity of the

original image is treated as a random variable

probability of occurrence in the image of a given intensity

is given by:

The paper is organized as follows: Section 2 is about a

classical method of straight line detection applied to images

of real weld lines; Section 3 is dedicated to the method

proposed in this paper, which takes into account the contour

conditions of the particular case of weld line detection. The

of the obtained results, conclusions and propositions

RANSFORM – SHT

(SHT) is a classical

images, widely used and

authors. An overview about this subject is

The weld line may be considered as a straight line, which

equated in several ways. The most suitable one is the

(1)

Using this equation it is possible to write equations to

straight lines of any orientation, what is not possible when

using the general equation of the straight line:

(2)

, the coefficient a

is infinite and a mathematical description to them cannot be

obtained using the general equation. Therefore, the

parametric equation is commonly used in the

The input to the SHT is a binary image. A simple way to

obtain it consists in the use of a thresholding algorithm, as

. To optimally determine such threshold,

according to the entropy criterion, will be used the method

dimensional entropy

the histogram of the gray level image. The

gray level image of the weld line used to illustrate the results

of this work was captured using a VGA resolution camera

The unimodal characteristic of the histogram of the gray

2.a, is common for images

of weld lines, where normally it has concentration of the

gray levels in a narrow band of values, making difficult the

segmentation, even using an optimal method for the choice

For the choice of the threshold, the method proposed by

is applied to the histogram presented in Fig. 2a.

Firstly, the histogram is equalized according to the method

, in order to increase the contrast and to

ntation. If the gray level intensity of the

original image is treated as a random variable r, the

probability of occurrence in the image of a given intensity rk

( )n

nrp kkr =

where nk is the number of occurrences of the intensity

n is the total number of pixels of the image. Thus, a discrete

version of the Cumulative Distribution

given by:

( ) ( )∑ ===

k

j jrk rprFCDF0

And taking F(rk) as a function that maps each

into a new kr′ described by:

( ) ( ) ∑∑ =====

k

j

j

j

k

j rkkn

nrprFr

00'

where the new image formed by kr

image after the histogram equalization. The original

histogram, the equalized one and their respective CDFs are

presented in Fig. 2.

The resultant image after the histogram equalization,

considered by Kapur as necessary for a bette

threshold through unimodal histograms, is shown in Fig.

Fig. 2. a) Original image histogram; b) CDF

Equalized histogram; d) CDF of the image after the histogram

equalization.

Fig. 1. Example of a gray level image of a weld line.

(3)

is the number of occurrences of the intensity rk and

is the total number of pixels of the image. Thus, a discrete

Cumulative Distribution Function (CDF) is

(4)

) as a function that maps each rk of the image

(5)

kr′ represents the original image after the histogram equalization. The original

histogram, the equalized one and their respective CDFs are

the histogram equalization,

considered by Kapur as necessary for a better choice of the

threshold through unimodal histograms, is shown in Fig. 3.

CDF of the original image; c)

of the image after the histogram

Fig. 1. Example of a gray level image of a weld line.

The next step consists in binarize the image. Let

probability of occurrence of the gray level

maximization method is implemented through

search for the threshold T which maximizes the equation:

( ) ( ) ( ){ }THTHMAXT bp +=ψ

where:

( ) ∑ =

−=

T

i

iip

p

p

p

pTH

100

log

( ) ∑ +=

−=

G

Ti

iib

p

p

p

pTH

111

log

and

∑∑ +====

G

Ti i

T

i i pPpP1110 ,

11 1110 ==+=+ ∑ ∑∑ += ==

G

Ti

G

i ii

T

i i pppPP

where G is the number of distinct gray level intensities that

appear in the image, and Hp(T) and Hb(T

entropies of the sub-histograms generated by the threshold

The threshold T is tested to all possible values and for

each one a corresponding discrete function

The curve of ψ(T) can be seen in Fig. 4a, where it is possible

to notice the threshold T that maximizes the function (6) and

divides the histogram into two regions, binarizing the image

in an optimal way with respect to the entropy aspect and

considering the use of a single threshold

presented in Fig. 1 a threshold T=0.266 was found, and when

applied results in the binary image of Fig. 4b.

It is evident that for this kind of image a global threshold

is not the best solution, due to the unimodal nature of the

histogram and to its high sensitivity to illumination

problems. A search for multiple thresholds using more

advanced techniques of exploration of Histogram

those demonstrated by [12]-[14], which consider not only

the independent probability of each pixel, would produce

Fig. 3. Resultant image after the histogram equalization

The next step consists in binarize the image. Let pi the

level i, the entropy

rough an exhaustive

which maximizes the equation:

(6)

(7)

(8)

(9)

(10)

is the number of distinct gray level intensities that

T) are the partial

histograms generated by the threshold T.

is tested to all possible values and for

each one a corresponding discrete function ψ(T) is obtained.

, where it is possible

that maximizes the function (6) and

wo regions, binarizing the image

in an optimal way with respect to the entropy aspect and

considering the use of a single threshold. To the image

=0.266 was found, and when

applied results in the binary image of Fig. 4b.

is evident that for this kind of image a global threshold

is not the best solution, due to the unimodal nature of the

histogram and to its high sensitivity to illumination

A search for multiple thresholds using more

ation of Histograms 2D, as

which consider not only

the independent probability of each pixel, would produce

much superior results, but these algorithms are

computationally intensive and so

implemented and used on real-time applications, a very

important characteristic when considering the target

application of this work.

Finally, the SHT is applied [6] by

4b, as input, to produce the parametric space firstly

described by [15], in which another exhaustive search is

made. This time, to each white pixel of the image two

vectors of parameters ρ and θ are calculated. The values of

the former varies in the range between 0 and 180 degrees,

and to each value of θ is calculated a value

on the resolution adopted for θ, these calculus may demand a

high computational cost. To reduce the number of pixels to

be processed by the SHT, the contour extraction of the

binarized image is performed according to

resultant image is shown in Fig. 5a.

The parametric space in Fig. 5b is nothing more than a

bidimentional matrix of accumulators that indicate how

many votes each pair (θ,ρ) obtained in the process. The

accumulators are presented in Fig. 6 in the form of a 2D

histogram. As the objective is to find a unique straight line

that better represents the image, considering the classical

theory of the SHT, the parameters that determine this

straight line are the most voted ones. The resultant straight

line is shown in Fig. 7, plotted over the original image.

Fig. 6. Accumulators matrix presented like a 2D histogram.

Fig. 5. a) Contour extraction of the binarized image

Fig. 4. a) Function ψ(T) evidencing the threshold that maximizes the entropy of the histogram; b) Binarized image using

threshold T=0.266.

the histogram equalization.

much superior results, but these algorithms are

they are difficult to be

time applications, a very

important characteristic when considering the target

by using the image in Fig.

to produce the parametric space firstly

, in which another exhaustive search is

made. This time, to each white pixel of the image two

are calculated. The values of

the former varies in the range between 0 and 180 degrees,

is calculated a value for ρ. Depending

, these calculus may demand a

high computational cost. To reduce the number of pixels to

be processed by the SHT, the contour extraction of the

binarized image is performed according to [11]. The

The parametric space in Fig. 5b is nothing more than a

bidimentional matrix of accumulators that indicate how

obtained in the process. The

accumulators are presented in Fig. 6 in the form of a 2D

ram. As the objective is to find a unique straight line

that better represents the image, considering the classical

theory of the SHT, the parameters that determine this

straight line are the most voted ones. The resultant straight

over the original image.

Fig. 6. Accumulators matrix presented like a 2D histogram.

Contour extraction of the binarized image; b) Hough space.

the threshold that maximizes the

) Binarized image using the global

III. THE PROPOSED METHOD: A MODIFIED HOUGH

TRANSFORM

Despite of the efficiency of the method presented in the

previous section, based on the SHF, to detect straight lines in

images, it is very sensible to noises and illumination

problems, and thus, the result is strongly dependent of the

segmentation step.

The proposed method, to be presented in this section,

takes into account two intrinsic characteristics of images

from weld lines, its thickness and differentiated texture of

the weld line with respect to the image background.

The proposed segmentation is oriented to detect a kind of

texture which characterizes the weld line in the image,

producing an output image in the form of a unique elongated

object, with approximately the same thickness of the weld

line. In this object, a modified version of the Hough

transform, introduced in this paper, is applied, whose

parameters selected to represent the resultant straight line are

not the most voted ones in the generated parametric space.

Instead of this, the parameters are chosen searching for a

group of accumulators with similar number of votes to a

same θ, and selecting the θ with the smallest variance of ρ, thus allowing to search only for straight lines with a

minimum thickness, and discarding those ones whose

thickness is not compatible with thickness of a regular weld

line. This approach contributes to reduce the sensibility of

the Hough transform to noise and to improve the final result

without additional computational cost.

A. The Proposed Segmentation Method

The segmentation method here proposed is based on texture

information acquired through the second central moment

[11]. In a similar way that in the Section 2, the entropy

maximization of a unidimensional histogram is used, but this

time, instead of the gray level histogram of the image, a

vector representing a sampling of standard deviations of

non-overlapping regions, which cover the entire original

image. Such vector is named vσ. Each sub-region is defined by:

=

+++

+

sjsijsi

sjiji

k

pp

pp

SubR

,,

,,

L

MOM

L

(11)

where the kth sub-region has as first element, the pixel pi,j of

the image and the square dimension s. Considering SubRk as

an independent matrix of s dimension and elements denoted

by SRi,j, the kth element of vector vσ is given by:

( )∑ ∑= =−=

s

i

s

j kjik SRSRv1 1 ,σ (12)

where kSR is the mean of the elements of SubRk.

The distribution of vσ vs. the length of vσ, obtained from

the image shown in Fig. 1 is presented in Fig. 8, and its non-

equalized histogram in Fig. 9.

The probability of occurrence of a certain vσk is given by:

( )n

nvp k

kv =σσ (13)

where nk is the number of occurrences of the element vσk in

vσ and n is the total number of elements of vσ. From the non-equalized histogram shown in Fig. 9, and

using (6), (7) and (8), with the probability pvσ in the place of

pi, P0 and P1 are now calculated by:

Fig. 9. Non-equalized histogram of vσ.

Fig. 8. Distribution of vσ vs. the length of vσ.

Fig. 7. The resultant straight line obtained using the SHT plotted over

the original image.

∑ ==

T

i vpP10 σ

and ∑ +==

G

Ti vpP11 σ

where G is the number of columns of the histogram of

The threshold that maximizes the discrete

calculated in a similar way as presented in the previous

section and the due to this, detailed procedure will be

omitted. The discrete function ψ(T) obtained is presented in Fig. 10. As can be noticed looking at the figure, the optimal

threshold to vector vσ is T≅ 0.021. The application of such threshold to the image

similar way as that carried out to calculate vector

and (12), but this time, the sub-regions overlapping is

necessary to rebuild the image without losing informatio

To the kth pixel of the image, pi,j, (12) will be applied,

considering the sub-region SubRk as the region of interest,

with dimension s, and the kth pixel as its central element.

This sub-region may be described by:

=

++−+

+−−−

2,

22,

2

,

2,

22,

2

sj

si

sj

si

ji

sj

si

sj

si

k

pp

p

pp

SubR

L

MM

L

The value of vσi,j, calculated for a given pixel

(15) and (12), is then compared with the threshold

new is attributed to the image according with the rule:

Fig. 11. Binarized image resultant of the application of the

segmentation procedure here proposed.

Fig. 10. Function ψ(T) evidencing the threshold that maximizes the

entropy of the histogram of vσ.

(14)

is the number of columns of the histogram of vσ. e function ψ(T) is

calculated in a similar way as presented in the previous

section and the due to this, detailed procedure will be

) obtained is presented in

Fig. 10. As can be noticed looking at the figure, the optimal

The application of such threshold to the image is done in a

similar way as that carried out to calculate vector vσ in (11) regions overlapping is

necessary to rebuild the image without losing information.

, (12) will be applied,

as the region of interest,

pixel as its central element.

(15)

, calculated for a given pixel pi,j from

(15) and (12), is then compared with the threshold T, and a

is attributed to the image according with the rule:

<

>=

Tvif

Tvifp

ji

ji

ji

,

,

, ,0

,1'

σσ

The resultant image of this binarization process is shown in

Fig. 11, where it is possible to notice an elongated object

whose thickness is similar to the one of the weld line shown

in the image, as expected, showing that the proposed method

is capable to attain the aimed objectives.

B. The Modified Hough Transform

The Hough transform implemented in this work is a bit

different of its classical version due to some modifications

that were made taking into account the contour conditions

associated with the task of detecting weld lines in storage

tanks. Such modifications were implemented in a SHT, but

they could be also used in faster implementations of the

Hough transform, like the FHT (Fast Hough Transform)

since the modifications here proposed do not concern the

parametric space generation, but in the selection of the pair

of parameters (θ,ρ) which define a straight line in the image.

The same procedure described in Section

using as input the image presented in Fig. 11 (obtained using

the segmentation method proposed in this paper), is then

used to obtain the parametric space of Hough shown in

Fig.12. In this case, not just one accumulator, but a set of

them are evidenced.

Each vector θi (composed by each column of the

accumulators matrix), independently,

process with a window function, searching for

amplitude window which represents it, and adopting as a

minimal width to the window ¾ of the estimated width of

the weld line, thus neglecting the isolated stra

whose thickness is incompatible with the weld line

dimensions. This procedure reduces the sensitivity to biased

noises, and it is illustrated in Fig. 13 for two distinct vectors

θi and θj.

Fig. 13. The fitting process with a window function applied on two

distinct vectors θi and θj.

Fig. 12. The parametric space of Hough.

Fig. 11. Binarized image resultant of the application of the

the threshold that maximizes the

(16)

resultant image of this binarization process is shown in

Fig. 11, where it is possible to notice an elongated object

whose thickness is similar to the one of the weld line shown

in the image, as expected, showing that the proposed method

ain the aimed objectives.

The Modified Hough Transform

The Hough transform implemented in this work is a bit

different of its classical version due to some modifications

that were made taking into account the contour conditions

detecting weld lines in storage

tanks. Such modifications were implemented in a SHT, but

they could be also used in faster implementations of the

Hough transform, like the FHT (Fast Hough Transform),

since the modifications here proposed do not concern the

parametric space generation, but in the selection of the pair

) which define a straight line in the image.

The same procedure described in Section 2, but this time,

using as input the image presented in Fig. 11 (obtained using

tation method proposed in this paper), is then

used to obtain the parametric space of Hough shown in

. In this case, not just one accumulator, but a set of

by each column of the

ependently, is submitted to a fitting

window function, searching for the highest

amplitude window which represents it, and adopting as a

minimal width to the window ¾ of the estimated width of

the weld line, thus neglecting the isolated straight lines

whose thickness is incompatible with the weld line

dimensions. This procedure reduces the sensitivity to biased

is illustrated in Fig. 13 for two distinct vectors

The fitting process with a window function applied on two

In the previous step, a valid observation window is

determined to each vector θ. Then, the search for the window with the lower variance in the related values of ρ is

made, with the objective to find a similar voting for different

values of ρ in the same θi. The value of θ with the lower

variance in the observation window and that at the same

time has a minimum amplitude of considered voting is the θ

that better represents the weld line in the image, and the

corresponding value of ρ is the average point of the

observation window that got the lower variance in the

previously described analysis.

The straight line obtained applying the method described

in this section is shown in Fig.14.

IV. CONCLUDING REMARKS

This paper presented a new approach for the extraction of

the parameters that define the straight line that better

represents the weld line in an image. An image segmentation

procedure based on texture and a variation of the Hough

Transform was proposed to improve the system’s noise

immunity. The obtained results showed the effectiveness of

the proposed method, even for images with illumination

problems and histograms with unimodal characteristic, as it

is the case of the majority of the images from weld lines of

storage tanks, without the necessity of histogram

equalization, however, it is necessary to test the algorithm

for a bigger and different image data base, to verify the

method’s robustness and conclude about it generality.

The described method presents an improvement in the

algorithm execution time and computational cost due to the

fact that in the proposed segmentation, the used vector vσ has a dimension much smaller than the vector r, the gray

level vector of the image, used in the classic method that,

moreover, still needs a histogram equalization stage,

unnecessary in the considered method. On the other hand,

for the proposed method, it is enough to make use of a not

overlapped sampling rate of sub-regions, thus reducing the

time needed to find the threshold to be used. In both cases,

the global threshold is determined through the entropy

maximization of the described unidimensional histogram

[10]. The binary image, resultant of the entropy

maximization of the gray levels histogram, revealed itself

very sensitive to illumination problems and noisy images.

However the binarization from vector vσ presents more clear

results, good enough for correct weld line detection, as can

be noticed by comparing the results presented in Fig. 7

(Section 2) and Fig. 14 (Section 3).

Despite of the existence of alternative implementation

forms of the SHT, like the RHT and FHT [7], the Hough

transform still has a very high computational cost and is very

sensitive to imperfections in the image segmentation stage.

The proposed modifications reduces the sensitivity to

illumination problems and biased noisy, and also can be

used in faster implementations of the Hough transform, like

the FHT, for example, keeping its contribution and speeding

up the process even more.

As a future work, a mobile robot will be equipped with a

camera and the proposed approach should be applied to

acquire the necessary information to feed the control system

used to keep the track of the weld line when performing a

real inspection task.

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Fig. 14. The resultant straight line obtained using the proposed

method plotted over the original image.