2011 International Symposium on Intelligent Signal Processing and Communication Systems (lSPACS) December 7-9,2011

Air Bubbles Detecting on Ribbed Smoked Sheets Based On Fractal Dimension

Pattira Umyai, Pinit Kumhom, Kosin Chamnongthai

Department of Electronics and Telecommunication Engineering, Faculty of Engineering,

King Mongkut's University of Technology Thonburi 126 Prachautid Road, Bangmod, Tungkru , Bangkok

Abstract-Air bubble impurity is one of critical defects on ribbed smoked sheets (RSSs). Only RSSs with few or without air bubbles are allowed in top industries such as wheel-manufacturing.

However, air bubbles are difficult to detect because of their size, intensity and low contrast with the background. This paper proposes to apply the fractal dimension as a feature for RSS classification with regard to air bubble impurity. Images of RSS

are captured in a controlled environment using a fluorescent light source. These images are resized and divided into 16 smaller areas. Then, the fractal dimension values of these areas are calculated. Finally, the standard deviation of the 16 fractal

dimensions is computed for comparing with a predetermined threshold for classification. The experimentations with 500 images of RSSs of which half contains some air bubbles were conducted to verify the proposed method. The experimental

results showed the classification accuracy of 98.00 % using the expert grading as reference.

Keywords-air bubble deflect in RSS; rubber sheets; fractal dimension; ribbed smoked sheets, RSS

I. INTRODUCTION

Havea Brasiliensis is an important industrial crop in several

countries such as Thailand, Indonesia, and Philippines.

Because of its high turnover and increasing of exportation,

rubber manufacture industry has become an important

parameter for agricultural economy propulsion in those

countries. Thailand has been the 1 st ranking rubber exporter in

the world [1]. In 2010, Thailand had exported 2.73 million

tons of various forms of rubber products accounting for

approximately 39 % of the all rubber products of the world.

Most of exported rubber products from Thailand are in the

form of ribbed smoked sheets (RSS), whose prices depends on

their quallities. To this end, the current RSS grading practice,

follows the standard specified in the Green Book published by

the Rubber Manufacturers Association Inc,Washington by

using the human visual inspection. However, the common

limitations for the human visual inspection includes fatigues,

requirement of expertises, low throughput, and lack of

standardization.

978-1-4577-2166-3/11/$26.00 ©2011 IEEE

Recently, many researchers have spent great efforts to

develop image processing based grading systems for the

purpose of both improving the product quality and

standardization. To the quality end, the ability to reject defects

is essential in improving quality of a product. The several

methods for detecting defected products was proposed by

many researchers based on various methods such as Gabor

filter [2], thresholding segmentation [3]. In rubber

manufacturing, an RSS can be graded by level of impurities

including splinter, white mould, and air bubbles. At present,

RSSs are graded by grading experts with average time about

10 seconds per sheet.

To solve the problem of splinter impurity in an RSS, C.

Laowattana et al.[4] proposed an automatic punching machine

to remove splinters from censorial sheets based on image

processing inspection of splinters. The machine works well for

improving quality of products without wasting the whole

defected sheets. However, the applied image processing

technique could not detect white mould and air bubbles. As

mould on rubber sheets is one of important defects,

S.Unkeaw et al. [5] proposed a method to inspect white mould

on RSSs using a color characteristics threshold technique to

segment mould from whole area. P. Bumrungkul et al.[6]

proposed another method for mould detection and

classification using fuzzy technique. To classify ribbed

smoked sheets into 5 levels, C. Pornpanomchai et al.[7]

proposed a grading system based on color spectrum. These

methods can not detect air bubbles well enough due to the

similarity of color features of air bubbles to the background

color.

Therefore, this paper propose a method of detecting air

bubbles on RSS based on the fractal dimension technqiue.

The remainders of the paper are arranged as follows. Section

II describes the basic concept of the proposed method, which

is explained in details in Section III. Section IV shows the

2011 International Symposium on Intelligent Signal Processing and Communication Systems (lSPACS) December 7-9,2011

experimental results of applying the proposed methods on 500

RSSs. Finally, the results are discussed and concluded in

Section V and VI respectively.

II. BASIC CONCEPT

In the process of RSS manufacturing, a sheet is marked with grid textures while going through a water extraction machine. The grid textures on the sheet surface area impure with air bubbles are disappeared due to the bubbles. Based on this fact, we proposed to apply the fractal dimension technique [8] on the grid textures. That is a good RSS without air bubbles should give high fractal dimension value due to the repetitive shape of the grid textures as shown in Fig. I(a). On the other hand, the defected areas should results in low fractal dimension values because the bubbles have degraded the regularity of the grid textures as shown in Fig. I(b).

(a) (b)

Figure 1 (a) an example of regular grid texture in a good RSS, (b) an example of irregular grid texture due to air bubbles.

In addition, we propose to apply the fractal dimension technique to divided areas of an RSS. The advantages of this dividing technique are twofold. Firstly, it gives us a chance to identify which divided areas are defected. This can lead to the process of cutting out the defected area to save the good parts of the sheet. Secondly, the properties of fractal dimension values of divided areas such as continuity and linearity can be used to classify the sheet without reference templates.

III. PROPOSED METHODOLOGY

In this section, we present the details of our proposed method by distributing the content into 4 subsections as follows.

A. Image Acquisition Due to low contrast of the bubble images and the

background, light in the image acquisition is sensitive to the performance of the proposed technique. Based on the study in [9], we choose the fluorescence light as our light source because it can depict grid textures the clearest. The acquisition is controlled so that only the light from the light source through the sheet is captured. To allow the light to distribute equally throughout the sheet, the distance between the light source and the sheet is set to 40 - 50 cm. A compact camera with resolution of 8 million pixels was used to capture the sheet images.

B. Edge Detection From experiments, we have found that the red-plane show

the best contrast of the grid textures, bubbles images, and the background. Therefore, in the fIrst step of our proposed method, we extract only the red-plane image of the input images after resizing the image to 600x900 pixels. Then, the Prewitt's edge detection technique is applied based on the results in [10] - [12] and experimental comparison with the other edge detection techniques including Canny, Log, Sobel, and Robert.

C. Image Separation There are many possible ways to divide the sheet image

into equal areas. Although it is possible that this segmentation could affect the classifIcation performance, we choose to try the segmentation with only one constraint which is to divide it into n x n parts. We compared the results of n equals 2, 3, 4, and 5, and choose the 4x4.

D. Fractal Dimension The term fractal was coined by Benoit Mandelbrot [8]. In

the beginning, many researchers applied fractal dimension to determine patterns of natural surfaces such as mountain, forest and clouds. Furthermore, fractal dimension has been applied in medical image processing such as iris classifIcation[13, 14]. The most famous and popular method to evaluate the fractal dimension is the box-counting algorithm. In this paper, we adopt the 2D-box counting for calculation of fractal dimension values of the 4x4 divided parts as follows:

1. The input image is scaled to a square of RxR, which in turn is divided into small square grids of size SxS, starting from S = 1. Then, compute r = SIR.

2. For each sqaure grid r, compute nr following the equation (1)

nr = 1 - k + 1 (1) Where I and k are the maximum and minimum gray level within the grid r.

3. Compute Nr following the equation (2). Nr = Li,j Nr (2)

4. The fractal dimension can be estim ated as the slope of the line that best fIts the points (log (1. r) , log Nr)

E. Classification Our primary classifIcation method is based on deviation of

the fractal dimensions of all 16 divided parts. First, the standard deviation (SD) is calculated following the equation (3).

(3)

2011 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) December 7-9,2011

Where {Xl' XZ, ... , XN} are the observed values of the sample items and i is the mean value of these observations. Then, the SD of each RSS is compared with a threshold, predetermined from experiments.

IV. EXPERIMENTAL RESULT

Five hundred images of RSSs in which 250 sheets contain some air bubbles were captured based on the acquisition method described in Section III. Then, the fractal dimensions of separated parts of each RSS were calculated. Figure 2 shows two examples of divided RSSs. Figure 2(a) shows an RSS with air bubbles while Fig. 2(b) contains no bubbles

Figure 2 Examples of RSSs with divided 16 areas.

(a) RSS with air bubbles

(b) RSS without air bubbles

Table I illustrates the fractal dimensions of the two example RSSs shown in Fig. 2, which are plotted in Fig. 3.

TABLE I. CALCULATED FRACTAL DIMENSIONS OF TWO

RSSs WITH AND WITHOUT AIR BUBBLES.

Position Fractal Dimension Air-Bubbled Sheets Graded Sheets

zl 1.322583 1.326342 z2 1.322068 1.330905 z3 1.318249 1.327772 z4 1.311143 1.326400 z5 1.325165 1.324793 z6 1.325193 1.332348 z7 1.319305 1.329189 z8 1.299620 1.326957 z9 1.322924 1.326766

z10 1.323915 1.327714 zll 1.321393 1.329419 z12 1.291627 1.330007 z13 1.326919 1.326679 z14 1.321273 1.329232 z15 1.325700 1.328771 z16 1.289404 1.328164

mean 1.316655 1.328216

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Figure 3 The relation between fractal dimensions and each parts of the two examples.

Notice that the fractal dimensions of the RSS with bubbles have high deviation, which obviously gives a higher value of SD comparing with that of the RSS without bubbles. This result supports our simple classification using only the SD.

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Figure 5 Expanded graph of Figure 4 with the selected threshold

TABLE II. THE ACCURACY RATE OF CLASSIFICATION

Input Classification Result

Type of Input Number Bubbled Sheets Graded Sheets RiQhtness Fault

Bubbled Sheets 250 240 10 240 10 Graded Sheets 250 0 250 250 0

Total 500 240 260 490 10

Figure 5 shows the distribution of the fractal dimension SDs of RSSs. Figure 4 zooms into the boarding line between the two RSS groups. It shows the selected SD threshold is at

2011 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) December 7-9,2011

0.00086. Using this threshold for our simple classification, we can classify the RSS with 98 % accuracy as shown in Table 2.

Figure 6 An example of RSS with air bubbles distributed all over the sheet causing a classification error.

V. DISCUSSION

The experimental results showed that the fractal dimensions can be a very effective feature for RSS classification in terms of air bubble impurity. As shown in Table II, we can achieve very high accuracy with a very simple classification method. However, there are still some errors of the classification. These errors had occurred in the RSSs in which air bubbles were equally distributed all over the whole sheet which led to low fractal dimension SD as shown in Fig. 6.

VI. CONCLUSION

We applied fractal dimension of divided parts of an RSS as feature for RSS classification. A simple classification method using threshold technique of the standard deviations of the fractal dimensions in 4x4 divided areas is proposed to gauge the fractal dimension capability. The results showed that the fractal dimension has a very high potential in grading RSS based on the air bubble impurity. In the future, we will try to apply a better classification technique with the fractal dimension features, and also try to identify which defected parts so that they can be cut out from the sheet's good part.

REFERENCES

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[2] Hamid Alimohamadi, Alireza Ahmadyfard, "Detecting Skin Defect of Fruits Using Optimal Gabor Wavelet Filter", International Conference on Digital Image Processing IEEE 2009

[3] Abdelhak Mahmoudi, Fakhita Regragui, "Welding"

[4] Chit Laowattana et aI., "Rubber Punching Machine", Department of Mechanical Engineering, King Mongkut's university of technology Thonburi, 1999

[5] Sittichoke Unkeaw, Thanet Kaorapapong,"Inspection of White Mould on Surface Rubber Sheets Using Image Processing with Color Characteristics Threshold Technique", EECON-29, November 2006.

[6] Prachaya Bumrungkul, Kosin Chamnongthai, and Pinit Kumhom, "Rubber Sheet Quality Grading System by Fuzzy Logic Mehtod", ICESIT, Febuary 2008 Grand Mercure Fortune Hotel, Bangkok, Thailand,pp. 167-170.

[7] Chomtip Pompanomchai, Naret Chantharangsikul, "Ribbed Smoked Sheet Grading System (RSSGS)", ICEIE2010 Vol. 1

[8] Ankur Roy, Edmund Perfect, William M.dunne and larry Dmckay, "Fractal Characterization of Fractal Network: An Improvwd Box­Counting Technique." Journal of geophysical research, Vol.l12B12201.

[9] Arkom Kongmanee, "Classification of Ribbed Smoked Sheet using Light Absorption", Department of Physical, Prince of Songkla U., 2004

[10] Yuqian Zhao, Weihua Gui, Zhencheng Chen,"Edge Detechtion Based on Multi-Structure Elements Morphology", The 6th World Congress on Intelligent Control and Automation, June 21-23,2006,Dalian,

[11] M. Roushdy, "Comparative Study of Edge Detection Algorithm Apply on the Grayscale Noisy Image Using Morphological Filter", ICGST,Intemational Journal og graphics, Vision, and Image Processing GVIP, Vol.6 Issue4,pp.l7-23,Dec.2006

[12] Narisra Donpomtan, Sirapath Cheawchamwattana, Kumron Sunat, "A comparative Effiency of Edge Detection to Extract Features with PHOG for Image Classification" JCSSE 2010

[13] Ali Asghar, Beheshti Shirazi and Leila Nasseri, "A Novel Algorithm to ClassifY Iris Image Based on Differential of Fractal Dimension by Using Neural Network" 2008 International Conference on Advanced Computer Theory and Engineering.

[14] Li Yu, Kuanquan Wang, David Zhang, "Coarse Iris Classification Based on Box-Counting.