A SOLUTION FOR EVALUATING THE STOPPER QUALITY IN

THE CORK INDUSTRY

Beatriz Paniagua-Paniagua, Miguel A. Vega-Rodríguez, Juan A. Gómez-Pulido,

Juan M. Sánchez-Pérez

Dept. Informática, Univ. Extremadura, Escuela Politécnica, Campus Universitario s/n, 10071, Cáceres, Spain

Keywords: Stopper quality, cork industry, image processing, automated visual inspection system.

Abstract: In this paper we study a possible solution to a problem existing in the cork industry: the cork stopper/disk

classification according to their quality using a visual inspection system. Cork is a natural and

heterogeneous material, therefore, its automatic classification (usually, seven different quality classes exist)

is very difficult. The solution proposed in this paper shows all the stages made in our study: quality

discriminatory features selection and extraction, texture analysis, analysis of different (global and local)

automatic thresholding techniques and possible classifiers. In each stage we have given more importance to

the study of those aspects that we think could influence the cork quality. In this paper we attempt to evaluate

each of the stages in our solution to the problem of the cork classification in an industrial environment, and

therefore, finding a way to justify the design of our final classification system. In conclusion, our

experiments show that the best results are obtained by a system that works with the following features: total

cork area occupied by defects (thresholding with heuristic fixed value 69), textural contrast, textural entropy

and size of the biggest defect in the cork, all of them working in an Euclidean classifier. The obtained

results have been very encouraging.

1 INTRODUCTION

The most important industrial application of cork is

the production of stoppers and disks for sealing

champagnes, wines and liquors. In fact, according to

the experts, cork is the most effective product,

natural or artificial, for the sealed (Fortes, 1993). In

the cork industry, stoppers and disks are classified in

different quality classes based on a complex

combination of their defects and particular features.

Due to this, the classification process has been

carried out, traditionally, by human experts

manually.

At the moment, there are several models of

electronic machines for the classification of cork

stoppers and disks in the market. The performance

of these machines is acceptable for high quality

stoppers/disks, but for intermediate or low quality,

the number of samples classified erroneously is

large. In conclusion, the stoppers/disks should be re-

evaluated by human experts later. This slows down

and increases in price the process enormously. Think

that, on average, a human expert needs a minimum

training period of 6 months to attain a minimum

agility, although the learning process lasts years

(compare it with other experts: wine tasters, cured

ham tasters, etcetera). Another negative aspect is the

subjectivity degree added to the classification

process due to the necessary human re-evaluation.

We have to add to these antecedents the fact that

Spain is the 2

world producer of cork (CorkQC,

2006), only surpassed by Portugal, and that in

Extremadura (a south-western region of Spain), due

to its geographical situation, the cork industry is one

of its most important industries: it produces 10% of

the world cork (ICMC, 2006).

All these motivations have lead us to the

development of this research, whose main objective

is the construction of a computer vision system for

cork classification based on advanced methods of

image processing and feature extraction in order to

avoid the human evaluation in the quality

discrimination process.

The rest of the paper is organized as follows:

section 2 describes briefly the data used for the

development of our experiments. In section 3, we

present the features used by the classifiers. Then,

334

Paniagua-Paniagua B., A. Vega-Rodríguez M., A. Gómez-Pulido J. and M. Sánchez-Pérez J. (2006).

A SOLUTION FOR EVALUATING THE STOPPER QUALITY IN THE CORK INDUSTRY.

In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics, pages 334-339

DOI: 10.5220/0001209703340339

 SciTePress

section 4 shows our analysis of the different studied

classifiers. Finally, section 5 displays the statistical

evaluation of the final results obtained by the

proposed whole system, while section 6 exposes the

conclusions and future work.

2 DATA

The database used in our experiments consists in

700 images taken from 350 cork disks (we have

taken two images of each disk, for both heads).

There are seven different quality classes, 50 disks in

each class. The initial classification, in which this

study is based on, has been made by a human expert

from ASECOR (in Spanish: “Agrupación

Sanvicenteña de Empresarios del CORcho”, in

English: “Cork Company Group from San Vicente-

Extremadura”). We suppose this classification is

optimal/perfect and we want to design a system

which obtains the most similar classification results.

3 USED FEATURES

In order to develop our classifiers study, first,

different feature extraction methods have been

analysed: thresholding techniques, statistical texture

analysis and two other heuristic features.

3.1 Thresholding Techniques

The cork stoppers/disks are classified using their

defects. These defects can be obtained by means of

segmentation techniques, and more concretely, by

automatic thresholding techniques (Sonka, 1998). In

our study we evaluate several thresholding

techniques with the purpose of knowing which of

them is the best for this application field. In this

study, in order to classify a cork disk in a specific

class, we only use the feature related with the defect

area in relation to disk area.

For this comparative thresholding analysis we

have studied both global thresholding techniques

and local thresholding techniques (Sahoo, 1988).

The thirteen thresholding methods that have been

studied are the following: slope method (own

proposal) with different minimum slopes, Otsu

method (Otsu, 1978), histogram concavity analysis

method (Rosenfeld, 1983), first Pun method (Pun,

1980), second Pun method (Pun, 1981), Kapur-

Sahoo-Wong method (Kapur, 1985), Johannsen-

Bille method (Johannsen, 1982), moment-preserving

method (Tsai, 1985), statistical thresholding method

(Fisher, 2004) with different modifications, and

Chow-Kaneko method (Chow, 1972).

The results of this study have been obtained by

using for each thresholding method a classifier of

minimum Euclidean distance (Shapiro, 2001). This

classifier is based on the percentage of the defect

area in relation to stopper/disk area. Knowing the

average value of this feature for each class (cork

quality class), we calculate the percentage of defects

for each new stopper/disk and the Euclidean

distances of this to the mean of each class. The

stopper/disk will be classified in the class for which

the smallest Euclidean distance has been obtained.

The following equation shows the functionality of

this classifier.

It is possible that this classifier would not

produce absolutely satisfactory results, due to we

only use a single feature in order to classify the cork

stoppers/disks, but this classifier can indicate

certainly whether the classification tendency is

reasonable or not, that is, the capability as quality

discriminator of each of the thresholding methods.

Within global thresholding methods, we find that

the most suitable method for cork industry is the

moment-preserving method. Figure 1 shows the

results (wrong classification percentage) obtained by

the different global thresholding techniques. As we

can see, all the thresholding techniques have

obtained certain discriminatory information,

although the goodness of the obtained results widely

varies between some thresholding methods and

others.

Figure 1: Global thresholding techniques results.

However, we can say that according to the

experimental results the local thresholding

techniques are more suitable for discriminating cork

quality based on the stopper/disk defects, being the

statistical thresholding method which has given the

best results. Figure 2 displays the wrong

classification percentage obtained by the different

local thresholding methods.

A SOLUTION FOR EVALUATING THE STOPPER QUALITY IN THE CORK INDUSTRY

335

Figure 2: Local thresholding techniques results.

We finish this study comparing the best results

obtained by both the global thresholding methods

and the local thresholding methods, in order to select

the best thresholding method to obtain our first

quality discriminatory feature: the cork area

percentage occupied by defects. It is worthy to say

that, in addition to the studied thresholding methods,

we decided to check a static thresholding method

with a heuristically fixed threshold. The gray level

for the threshold was obtained by using a recursive

statistical study, testing what gray levels gave better

classification results. Finally, a gray level 69 has

been chosen as threshold.

Figure 3: Final results of the thresholding study.

In figure 3 we can observe all these results. In

conclusion, the local thresholding methods have

been more suitable than the global methods for the

solution of our problem. This has been due to they

are able to find better thresholds in unimodal

histograms. Nevertheless, the increase of the

computational cost can make them unsuitable for our

problem. Taking into account all these

considerations, the best of all these methods applied

to our problem has been the static thresholding

method with a heuristically fixed threshold in the

gray level 69.

3.2 Texture Analysis

We think cork texture can also be a powerful quality

discriminator for the cork stoppers/disks. Between

the main methods of texture analysis, structural

analysis and statistical analysis (Shapiro, 2001), we

have chosen the statistical approach due to the high

difficulty to look for given visual patterns (texels) in

the cork, since it is a heterogeneous material. The

work made in this study is based on second-order

gray level texture statistics, proposed by Haralick et

al. (Haralick, 1973). In this second study we

evaluate a great number of these statistical

discriminators based on textures with the purpose of

knowing which of them are most appropriate for the

resolution of our problem.

In order to classify a cork disk in a specific class,

we only use the corresponding textural discriminator

(stopper/disk texture). In our texture analysis we use

statistical quality discriminators based on the co-

occurrence matrix. The studied discriminators are

obtained by means of calculations using the rotation-

robust normalized co-occurrence matrix, and they

are the following: Energy (or Angular Second

Moment (Shah, 2004)), Contrast (or Inertia),

Homogeneity, Entropy, Inverse Difference Moment,

Correlation, Cluster Shade, Cluster Prominence and

Maximum Probability.

The results of this study have been obtained

using the same method that the one used in the

thresholding study (see section 3.1). Figure 4

presents the wrong classification percentage

obtained by the different statistical discriminators.

As we can observe in the graph, texture has certain

discriminatory information that improves the cork

classification according to its quality, although the

goodness of the obtained results widely varies

between some textural features and others, being the

best discriminatory features the textural contrast and

entropy.

Figure 4: Final results for the studied textural features.

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3.3 Other Features

After the previous features studies, we have

dedicated the last features study stage to the analysis

of other features and processes which could give

positive results in matter of cork classification. After

a deep observation of the classification parameters

used by the human experts in their classifications we

found some guidelines that were worthy to evaluate.

The two studied features have been the hole

existence in the cork area and the biggest defect size

in the cork:

• Hole study: It was observed that the cork

stoppers/disks with holes were relegated to low-

quality classes, in spite of their good cork

texture or their pore homogeneity. The followed

methodology has been to make the classification

by means of the usual Euclidean classifier, but

we have considered the number of hole pixels

when we have made the definitive

classification. This feature only has some

discrimination power in the low-quality cork

classes, concretely from class 4 to class 6 which

are those that begin to have some holes in their

area, reason why this feature must be combined

with another feature that has discrimination

power in the rest of classes. In this case, we

have chosen the defect area.

• The biggest defect size study: It was observed

that those cork stoppers/disks with big defects

also were classified in the lower classes, in spite

of the possible positive details that they could

have. The methodology followed in order to

obtain this feature has been making successive

binary erosions on the thresholded image, with a

5x5 structuring element (each iteration subtracts

two pixels from the defect perimeter). In each

iteration the remaining image percentage is

controlled. In this way, it is possible to obtain

easily the size that the biggest defect could

have, taking into account the number of

iterations required to leave the image in blank.

Finally, the optimum feature selected in this

study has been the biggest defect size in the cork

area.

4 CLASSIFIERS

In this last study, in order to classify a cork disk in a

specific class, we will use the corresponding

classification algorithm based on the four features

selected: defects area (using a static thresholding

method with a heuristically fixed threshold), texture

contrast, texture entropy, the biggest defect size. The

four classifiers chosen for this study are the

following (Shapiro, 2001) (Sonka, 1998): a Back-

Propagation neural network, a K-means classifier,

the K-nearest neighbours classification algorithm,

and a minimum Euclidean distance classifier:

• Back-Propagation neural classifier: The

network designed for this study has a 4x7x3

architecture. The weights associated to the

network interconnections are initialized

randomly and are adjusted during the learning.

The type of learning used by this neural network

is supervised.

• K-means classifier: This classification

algorithm makes reference to the existence of a

number of K classes or patterns, and therefore,

it is necessary to know the number of classes.

We know, a priori, that we have 7 classes,

reason why the algorithm is suitable for our

necessities.

• K-nearest neighbours classifier: This algorithm

is part of the methods group known as

correlations analysis methods. It consists in

classifying an unknown feature vector,

depending on the sample or K samples of the

training set that is/are more similar to it, or what

is the same, which is/are nearer to this vector in

terms of minimum distance. This is what we

know as rule of the nearest neighbours. The

classification algorithm of the K-nearest

neighbours even can be very efficient when the

classes have overlapping, and this is very

interesting for our problem (cork quality

classes). We have evaluated several K sizes (10,

20, 49,…), and the best size was 20.

• Euclidean classifier: The classification

algorithm supposes several classes with their

respective prototypes (centroids). Given an

unknown feature vector to classify, the

Euclidean classifier will associate this vector to

the class whose prototype is closest to it, that is,

the prototype whose Euclidean distance is the

smallest. Our study have been made for four

versions of the Euclidean distance: simple

Euclidean distance (see equation below),

Euclidean distance with prefiltrate (certain

corks were classified directly, without passing

the Euclidean classifier, to low-quality classes if

a hole in them was detected, that is, we used a

set of decision rules in addition to the Euclidean

classifier), scaled Euclidean distance (see

equation below) and modified scaled Euclidean

distance according to the standard deviation (see

A SOLUTION FOR EVALUATING THE STOPPER QUALITY IN THE CORK INDUSTRY

337

equation below). The best results were obtained

using the modified scaled Euclidean distance.

According to the experimental results we can say

that, in case of cork, there are more suitable

classifiers than others, although some of the studied

classifiers have been very near in their final results.

As conclusion, we can say that the Euclidean

classifier has been the more reliable in our

application field. Figure 5 presents the wrong

classification percentage obtained by the different

classifiers.

Figure 5: Final results for the studied classifiers.

5 RESULTS

Having made all these previous studies, we can

conclude that the best cork classification system is

the one based on an Euclidean classifier working

with the following quality discriminatory features:

the cork area occupied by defects (thresholding with

heuristic fixed value 69), the texture contrast, the

texture entropy and the size of the biggest defect in

the cork.

We present the final results obtained by this

system by means of a confusion matrix (Shapiro,

2001), due to its capability to show the conflicts

among the different quality categories. Therefore,

not only the definition of each class will be

displayed, but also the main confusions among them.

The obtained confusion matrix (table 1) presents

quite positive results (the main diagonal of the

matrix is clearly defined). Using a classifier based

on scaled Euclidean distances with the standard

deviation, we can also observe that class 6 acquires a

great power of absorption, that even affects class 4.

On the other hand, we can see a strong

discrimination of classes 0, 6 and 3, with a great

number of corks classified rightly in these classes.

Table 1: Confusion matrix for the final system.

C0 C1 C2 C3 C4 C5 C6

C0 33 12 4 1 0 0 0

C1 19 14 13 3 1 0 0

C2 6 9 15 18 2 0 0

C3 1 4 7 23 11 0 4

C4 2 0 1 10 13 3 21

C5 0 0 1 12 7 6 24

C6 1 0 1 7 7 3 31

The total results are shown in table 2, with a

final wrong classification percentage of 61.42%.

Table 2: Total results for the final system.

C0 C1 C2 C3 C4 C5 C6 TOT.

Wrong 17 36 35 27 37 44 19 215

Right 33 14 15 23 13 6 31 135

In addition to this experiment, which was made

on the complete image database, we have made two

additional experiments: one with a pre-selection of

40 cork disks per class (280 corks in total) and

another with a pre-selection of 20 cork disks per

class (140 corks in total). These tests were done

because there were corks that were classified badly

in a systematic way, therefore, we supposed that the

human expert who performed the first cork

classification (remember that we have based all our

work on this classification) could have made some

mistakes (wrong classifications), or that certain cork

images could have a very poor quality due to the

used acquisition system (camera, illumination, etc.).

The evolution of the obtained results can be seen

in figure 6. We can observe a clear decrease in the

wrong classification percentage, which makes us

think about the possible existence of some errors in

our image database. Observe that the results using an

image database pre-selected with 140 cork disks

(280 images) shows a wrong classification

percentage of 45% (very far from the 61.42%).

Figure 6: Final results of the database pre-selections.

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338

6 CONCLUSIONS

In this paper we have performed a deep survey to

conclude in the best classification system among all

the systems proposed. Many possible discriminatory

features have been studied in depth, as well as the

classifiers to work with them.

As conclusion, figure 7 presents the wrong

classification percentage obtained by our system for

the different image databases. This graph also

includes the wrong classification percentage that a

random classification would have obtained.

Figure 7: Final results for the studied system.

As we can observe in the previous graph, the

pre-selection with 20 disks per class in our proposed

system has produced the best results (45% of error

rate). Furthermore, the result obtained by the final

system highly improves the results obtained by a

random classification (around a 90% of error rate).

As future work we have planned to study other

classifiers like, for example, fuzzy-neural networks.

Also, we do not discard the inclusion and analysis of

other features that could improve the classification.

ACKNOWLEDGEMENTS

This work has been supported in part by the Spanish

Government under Grant TIN2005-08818-C04-03.

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