This paper presents a system, based on computer
vision techniques, that detects and quantifies different types of
defects in ceramic plates. It was developed in collaboration
with the industrial ceramic sector and consequently it was
focused on the defects that are considered more quality
depreciating by the Portuguese industry. They are of three
main types: cracks; granules and relief surface. For each type
the development was specific as far as image processing
techniques and illumination are concerned. The system was
tested in pre industrial conditions showing the efficiency of the
several developed algorithms and also revealing the
perspective of its evolution to an industrial automatic
inspection system

Ferreira, Manuel João Oliveira

Martins, Teresa

Santos, Cristina

Silveira, Joaquim

Universidade do Minho: RepositoriUM

Computer Vision Techniques Applied to the Quality Control of Ceramic Plates  Joaquim Silveira*, Manuel J. Ferreira*, Cristina Santos*, Teresa Martins**  *Industrial Electronics Department University of Minho Campus de Azurem – Guimarães, Portugal Tel: +351253510190; Fax: +351253510189 E-mail: jlms82@gmail.com; mjf@dei.uminho.pt; cristina@dei.uminho.pt  **Enermeter, Sistemas de Medição, SA Praceta das Agras, 26 Celeiros – Braga, Portugal Tel: +351253287237; Fax: +351253287238;  E-mail: tmartins@enermeter.pt   Abstract – This paper presents a system, based on computer vision techniques, that detects and quantifies different types of defects in ceramic plates. It was developed in collaboration with the industrial ceramic sector and consequently it was focused on the defects that are considered more quality depreciating by the Portuguese industry. They are of three main types: cracks; granules and relief surface. For each type the development was specific as far as image processing techniques and illumination are concerned. The system was tested in pre industrial conditions showing the efficiency of the several developed algorithms and also revealing the perspective of its evolution to an industrial automatic inspection system.  I. INTRODUCTION In the last years the ceramic industry has been subject to relevant developments related with the progression of automation. The plate manufacturing sector is one of the most innovative regarding the application of new technologies in the several production stages. However quality control remains as an exception since the detection of defects stills as a manual operation. Consequently the human influence is significant and results in a low quality inspection due to human subjectivity and fatigue. An automatic system can provide many economical and safety benefits such as: to reduce human presence in a difficult environments and human error; to increase efficiency and stability in the process; to promote faster and more economic inspection.  Computer vision techniques can make available an effective solution to this automatic quality control. This article presents such a computer vision system able to inspect ceramic plates. The plates have round shape with a maximum diameter of 17in, without patterns and uniform in what color concerns. The more relevant defects are classified in three categories: cracks; granules and relief surface. The cracks are rips throughout the plate surface that can be noticed as relief variations. They present variable length (few mm to some cm) and a width below 0,5mm. Granules result from the inclusion of impurities during production process. They have normally a circular shape and a variable dimension with at least 0.1mm of diameter. The relief surface presents a cluster of small bumps in a particular area of the plate. The bump is a round and raise area on the surface. The relief surface can have different forms and sizes according to the bumps agglomeration. Fig. 1 presents an example of each defect.            Fig. 1. Example of the three classes of plate defects: crack, granule and relief surface.  According to the performed bibliographic research there aren’t available computer vision solutions for plate inspection. In fact, the visual inspection by image processing and analysis is still an emerging technology in the global ceramic industry. The tiles production sector has been one of the most prominent sector concerning the research and development of vision techniques and prototypes [1-6]. Those techniques, based on adaptive segmentation and edge detection, were also used for the crack granule relief surfacedevelopment of the present work dedicated to plate inspection.   II. METHODS The detection of the defects comprises two phases: segmentation and identification (Fig. 2). The main purpose of the segmentation is to isolate the defect from the plate background. Then, the identification modules extract relevant blob features from the segmented image in order to classify the defect. For each type of defect a specific set of algorithms was developed and implemented.  Fig. 2. Block diagram of the detection procedure.  A. Algorithms for crack defect detection The segmentation for cracks is based on the Canny Edge detector algorithm [3,7,8] (Fig. 3). This algorithm imposes a first step (smoothing) for noise reduction through the application of a Gaussian filter. The next step (enhancement) starts by calculating the magnitude and direction of the image gradient.                     Fig. 3. Block diagram of the Canny edge detector.  After the directions are known, a non-maximum suppression verifies each point in the gradient direction to eliminate all the points that are not a maximum gradient. This operation results in an edge described by a thin line in the gradient image. Finally, the detection stage applies a threshold procedure controlled by two values (T1 and T2), to the maximum points, in order to reduce the number of false edge segments. Any pixel in the gradient image that has values greater than T1 is considered as a possible edge pixel. If any of these possible edge pixel has a value greater than T2, it is selected as an effective edge pixel. The described sequence of operations results in a contour image, concluding the segmentation phase (Fig. 4). The identification phase (Fig. 4) begins by dilating the segmented image. This procedure is applied to minimize the possibility of broken edges segments, which is a common problem arising from the thresholding. The Find Contour process, based on the chain code [9], returns a sequence with all the contours of the segmented image. Then, the Get Cracks algorithm distinguishes the cracks from other extracted contours (e.g. noise, granule).                      Fig. 4. Block diagram of crack defect detection algorithms: Segmentation algorithm; Identification algorithm.  Fig. 5 details the Get Cracks process. For each contour of the sequence, a 2D box is created in order to engage the minimal rectangle area of the contour, as shown in Fig. 6.  The box width and height permit the calculus of a ratio, which is the feature used to classify the crack defect. A common crack presents an elongated shape, resulting in a ratio typically higher than a pre established threshold (RT). When the system identifies a defect that is not a crack, that contour is eliminated from the sequence. At the end, the algorithm returns only the cracks contours.   B. Algorithms for granule defect detection Fig. 7 describes the Granule defect detection algorithms. The segmentation uses an adaptive thresholding algorithm [9,10]. This is a convenient approach taken in account the small size of these defects and the illumination conditions that are difficult to make completely uniform. In this method, the image is subdivided in regions, in such a way that the illumination is more uniform in each region. This Gaussian Filter  Gradient Filter  Image Smoothing Enhancement Non-Maximum Suppression   Thresholding Detection Contour Image Gray Scale Conversion Canny Edge  Dilate image Find Contours Get Cracks Original defect imageSegmented ImageCrack defect ContoursSegmentation algorithm  Identification algorithm Segmentation Identification Defect  Image    Defect Segmented  Image can be obtained by calculating a threshold for each pixel. The threshold was determined by examining the neighborhood and extracting a statistical value, specifically the mean value of the intensity dispersion. The size of the neighborhood is a compromise as it has to be large enough to get a significant statistical value and small enough to maintain the uniformity of the illumination. The result is a binary image, further submitted to an open morphological operator. That eliminates the noise remnant from the binarization, concluding the segmentation process.                        Fig. 5. Block diagram of the Get Cracks algorithm.               Fig. 6. Image of the Contours and a 2D Box of the defects.  The identification phase begins applying the segmented image to the identification algorithm developed for cracks (described previously). After extracting the contours of the potential cracks, they are drawn in a filled way, which results in a new image with black background and white filled contours. After a dilatation operator, the segmented image holding only granules is obtained by applying an OR logical operation between the segmented image (with all defects) and the potential crack image. In this way it is possible to exclude the crack areas of the original image. Finally the contours of the granules are obtained through the Find Contours process.                                Fig. 7. Block diagram of granule defect detection algorithms: Segmentation algorithm; Identification algorithm.   C. Algorithms for relief surface detection The detection of these defects is base on the principle of specular reflection [11]. When a very smooth surface is illuminated, it is possible to acquire a bright area, with the same color of the illuminator that is caused by specular reflection. This is dependent of the point of visualization. Fig. 8 illustrates this principle when it is applied to a plate with and without relief surface defects.         Fig. 8. Specular reflection at plate surfaces. Gray Scale Conversion Adaptive  Thresholding  Open  Operator Crack Defect Detection (Identification) Draw Filled Contours in new black image Dilate OR Find Contours Original defect image Segmented Image Granules defect Contours Segmentation algorithm  Identification algorithm  Crack defect Contours Last Contour? Box2D in contour Calculate ratio  ratio=box width / box height Not crack? ratio<RT Eliminate Contour Next Contour yes yes no no Defects Contours Crack defect Contours  Illumination rays Specular reflection  Surface without defect     Surface with defect Fig. 9 shows real illuminated images of plate surfaces, with and without relief, obtained using specular reflection. Since in the image with defect the surface is not smooth, the reflection will have a higher dispersion than in the image without defect. Therefore to make a distinction between these two situations it was necessary to define a region to process - region of interest (ROI), as shown in Fig. 9 between the two lines. The level of dispersion in this area gives indication about the presence and respective degree of relief.          Fig. 9. Image with (left) and without (right) relief surface defect, affected by specular reflection.  The detection algorithm comprises an approach that extracts statistical values from the binarized ROI image and an approach that extracts statistics from the gray level ROI image (Fig. 10).                              Fig. 10. Block diagram of relief surface detection Algorithm.  In the first approach, the binarization is accomplished by defining a static threshold value. From the segmented image it is possible to obtain statistical information used to quantify the relief dispersion, such as the number of pixels in the ROI that have zero value and a spatial mean of all these pixels.  In the second approach (without binarization), the mean and standard deviation of the intensity of the ROI pixels are calculated.  Statistics obtained by the two approaches must then be evaluated concerning their inclusion within a specific range. A plate will be considered without relief defect if all the values are outside the defined range.  III. RESULTS AND DISCUSSION The system comprises two different setups: one for the detection of cracks and granules and another for the relief surface evaluation. In both cases the plates are moving in a belt convey passing under the illumination system.  In the first case it is important to minimize problems, such as flicker and specular reflections. For that a specific illumination setup was projected. The illumination is based on LEDS light, in a frontal position. This must simulate a kind of cloudy day light, which was achieved with a white calotte (Fig. 11) in a diffuse material where fluorescent light points towards the calotte interior. The result is an image with a high degree of illumination uniformity on the plate. A global plate image is acquired by an Allied VC OSCAR F810C progressive camera (res: 3288x2470 pixels) with a 16mm lenses and a firewire acquisition board.  In the case of relief surface detection for each acquisition only a ROI of the image plate is inspected. To cover the entire plate several acquisitions must be performed, as in a linear acquisition process. The setup is based on a bar of LEDS and in an Allied VC MERLIN F-033C progressive camera (res: 656x494 pixels) with a 16mm lenses and a firewire acquisition board. The light source and the camera were positioned as illustrated in fig. 8. This configuration creates the desired specular reflection on the plate image. The application was developed with Microsoft Visual C++ and with the OpenCv Library [12] that includes several image processing and computer vision algorithms. Two modules were included: one to parameterize the system establishing the parameters for the image processing algorithms and a module to carry out the detection of the defects. Several supervised tests were performed with the described setups and using a set of known samples (500) representing the diversity of situations concerning types, dimensions and spatial distribution of defects. This allowed determining the parameters, for each algorithm, that best discriminate the three types of defects (Table I and II).     Set ROI Gray Scale Conversion Statistic values (Mean, Standard Deviation)Values are in a specific range? yes no Original defect image ROI with  defect ROI without  defect Binarization Statistic values (Count Zero, Spatial Mean)                        Fig. 11. Images of the calotte used on the setup for the detection of cracks and granules.  TABLE I SYSTEM PARAMETERS FOR CRACK AND GRANULE DETECTION. Canny Operator   T1 T2 Ratio Threshold (RT) Neighborhood Size Crack 42 126 2 -  Granule - - 2 35  TABLE II SYSTEM PARAMETERS FOR RELIEF SURFACE DETECTION.  Lower Limit Upper Limit ROI 141 197 Binarization Approach  Threshold Spatial Mean Count Zero Range (Fig. 10) 180 <140 >30000 Without Binarization Approach  Mean Standard deviation Range (Fig. 10) >160 >10  Figs. 12 and 13 exemplifies the application of the system to detect cracks and granules, respectively. In both, the final result is a sequence of image points that defines the contours of the defects.  Fig. 14 illustrates an application example for relief surface detection in accordance with the algorithm of fig. 10. This figure shows the original images for a defective (Fig. 14a) and a non defective (Fig. 14b) plate and the respective segmented images using the binarization process. Table III presents the statistic values that describe the ROI marked in the original images. Comparing these statistics with the values specified in table II it is possible to demonstrate the presence of the defective plate.                       Fig. 12. Sequence of images for the crack defect; from top: original, segmented and the defect contour drawn in the original image.   Fig. 13. Sequence of images for the granule defect; from top: original, segmented and the defect contour drawn in the original image.    Segmentation Identification Crack defect Crack defect Granules defect Binary Logic Operation Granules defect detected Segmentation Identification Crack defect detected Crack defect Granules  defect             Fig. 14. Original and segmented plate images. a) Defective surface; b) Non defective surface. Solid line defines the ROI. Dashed line represents the spatial mean.  TABLE III APPLICATION RESULTS FOR THE EXAMPLES FROM FIG. 14.  Defective Non Defective Mean 175 157 Standard Deviation 19 8 Count Zero 62276 25190 Spatial Mean 136 172  IV. CONCLUSIONS This paper describes a computer vision system dedicated to identify the more relevant defects that were found to depreciate the ceramic plates from Portuguese industry: cracks, granules and the presence of relief surfaces. For the different types of defects, specific image processing and analysis algorithms were implemented as well as laboratory setups representing industrial conditions, namely concerning illumination. For each defect a set of parameters were identified to allow a higher flexibility of the system.  The tests performed with the lab prototype suggest good performance. Therefore, this work demonstrated the viability for implementing an automatic quality control solution based on visual inspection, which will fulfill a technological demand that persists in this industrial sector.   REFERENCES [1] C. Boukouvalas, J. Kittler, R. Marik, M. Mirmehdi and M. Petrou, “Ceramic Tile Inspection for colour and structural defects”. in Proceedings of AMPT95 pages 390--399. August 1995. [2] Suzana Vasilic, Zeljko Hocenski, “The Edge Detecting Methods in Ceramic Tiles Defects Detection”. in IEEE International Symposium on Industrial Electronics, 2006, July 2006. [3] Zeljko Hocenski, Suzana Vasilic, Verica Hocenski, “Improved Canny Edge Detector in Ceramic Tiles Defect Detection”. in IEEE Industrial Electronics, IECON 2006 – 32nd Annual Conference, Novembro 2006. [4] Desoli, G.S.; Fioravanti, S.; Fioravanti, R.; Corso, D. “A system for automated visual inspection of ceramic tiles”. in International Conference on Industrial Electronics Control and Instrumentation, 1993. [5] Elbehiery, Hussam M., Hefnawy, Alaa A., Elewa, Muhammad T., “Visual Inspection for Fired Ceramic Tile's Surface Defects Using Wavelet Analysis”. in GVIP(05), No. V2, January 2005. [6] Peter Nold, Ralf Löbe, and Klaus Jean Frey, “Ceramic Industry – state-of-the-Art Processing”, Maschinenfabrik Gustav Eirich, Hardheim, Germany, unpublished. [7] William K. Pratt, “Digital Imaging Processing”, fourth edition, Wiley, 2007. [8] Bill Green, “Canny Edge Detection Tutorial”, web page visited on September 2007, www.pages.drexel.edu/~weg22/can_tut.html [9] Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, Second Edition, Prentice Hall, 2002.  [10] Bob Fisher, Simon Perkins Ashley Walker, Erik Wolfart, “Hyper Media Image Processing Reference – Adaptive Thresholding”, web page visited on October 2007. www.cee.hw.ac.uk/hipr/html/adpthrsh.html [11] Tinku Acharya, Ajoy K. Ray, “Image Processing – Principles and Applications”, Willey, 2005 [12] Intel, “Open Source Computer Vision Library” web page visited on September 2007. www.intel.com/technology/computing/opencv    

Computer vision techniques applied to the quality control of ceramic plates

Computer Vision Techniques Applied to the Quality 
Control of Ceramic Plates 
 
Joaquim Silveira*, Manuel J. Ferreira*, Cristina Santos*, Teresa Martins** 
 
*Industrial Electronics Department 
University of Minho 
Campus de Azurem – Guimarães, Portugal 
Tel: +351253510190; Fax: +351253510189 
E-mail: jlms82@gmail.com; mjf@dei.uminho.pt; cristina@dei.uminho.pt 
 
**Enermeter, Sistemas de Medição, SA 
Praceta das Agras, 26 
Celeiros – Braga, Portugal 
Tel: +351253287237; Fax: +351253287238;  
E-mail: tmartins@enermeter.pt 
 
 
Abstract – This paper presents a system, based on computer 
vision techniques, that detects and quantifies different types of 
defects in ceramic plates. It was developed in collaboration 
with the industrial ceramic sector and consequently it was 
focused on the defects that are considered more quality 
depreciating by the Portuguese industry. They are of three 
main types: cracks; granules and relief surface. For each type 
the development was specific as far as image processing 
techniques and illumination are concerned. The system was 
tested in pre industrial conditions showing the efficiency of the 
several developed algorithms and also revealing the 
perspective of its evolution to an industrial automatic 
inspection system. 
 
I. INTRODUCTION 
In the last years the ceramic industry has been subject to 
relevant developments related with the progression of 
automation. The plate manufacturing sector is one of the 
most innovative regarding the application of new 
technologies in the several production stages. However 
quality control remains as an exception since the detection 
of defects stills as a manual operation. Consequently the 
human influence is significant and results in a low quality 
inspection due to human subjectivity and fatigue. An 
automatic system can provide many economical and safety 
benefits such as: to reduce human presence in a difficult 
environments and human error; to increase efficiency and 
stability in the process; to promote faster and more 
economic inspection.  
Computer vision techniques can make available an 
effective solution to this automatic quality control. This 
article presents such a computer vision system able to 
inspect ceramic plates. The plates have round shape with a 
maximum diameter of 17in, without patterns and uniform in 
what color concerns. 
The more relevant defects are classified in three 
categories: cracks; granules and relief surface. The cracks 
are rips throughout the plate surface that can be noticed as 
relief variations. They present variable length (few mm to 
some cm) and a width below 0,5mm. Granules result from 
the inclusion of impurities during production process. They 
have normally a circular shape and a variable dimension 
with at least 0.1mm of diameter. The relief surface presents 
a cluster of small bumps in a particular area of the plate. 
The bump is a round and raise area on the surface. The 
relief surface can have different forms and sizes according 
to the bumps agglomeration. Fig. 1 presents an example of 
each defect.  
 
 
 
 
 
 
 
 
 
 
Fig. 1. Example of the three classes of plate defects: crack, granule and 
relief surface. 
 
According to the performed bibliographic research there 
aren’t available computer vision solutions for plate 
inspection. In fact, the visual inspection by image 
processing and analysis is still an emerging technology in 
the global ceramic industry. The tiles production sector has 
been one of the most prominent sector concerning the 
research and development of vision techniques and 
prototypes [1-6]. Those techniques, based on adaptive 
segmentation and edge detection, were also used for the 
crack 
granule 
relief 
surface
development of the present work dedicated to plate 
inspection.  
 
II. METHODS 
The detection of the defects comprises two phases: 
segmentation and identification (Fig. 2). The main purpose 
of the segmentation is to isolate the defect from the plate 
background. Then, the identification modules extract 
relevant blob features from the segmented image in order to 
classify the defect. For each type of defect a specific set of 
algorithms was developed and implemented. 
 
Fig. 2. Block diagram of the detection procedure. 
 
A. Algorithms for crack defect detection 
The segmentation for cracks is based on the Canny Edge 
detector algorithm [3,7,8] (Fig. 3). This algorithm imposes 
a first step (smoothing) for noise reduction through the 
application of a Gaussian filter. The next step 
(enhancement) starts by calculating the magnitude and 
direction of the image gradient.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 3. Block diagram of the Canny edge detector. 
 
After the directions are known, a non-maximum 
suppression verifies each point in the gradient direction to 
eliminate all the points that are not a maximum gradient. 
This operation results in an edge described by a thin line in 
the gradient image. Finally, the detection stage applies a 
threshold procedure controlled by two values (T1 and T2), 
to the maximum points, in order to reduce the number of 
false edge segments. Any pixel in the gradient image that 
has values greater than T1 is considered as a possible edge 
pixel. If any of these possible edge pixel has a value greater 
than T2, it is selected as an effective edge pixel. The 
described sequence of operations results in a contour image, 
concluding the segmentation phase (Fig. 4). 
The identification phase (Fig. 4) begins by dilating the 
segmented image. This procedure is applied to minimize the 
possibility of broken edges segments, which is a common 
problem arising from the thresholding. The Find Contour 
process, based on the chain code [9], returns a sequence 
with all the contours of the segmented image. Then, the Get 
Cracks algorithm distinguishes the cracks from other 
extracted contours (e.g. noise, granule).  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 4. Block diagram of crack defect detection algorithms: Segmentation 
algorithm; Identification algorithm. 
 
Fig. 5 details the Get Cracks process. For each contour of 
the sequence, a 2D box is created in order to engage the 
minimal rectangle area of the contour, as shown in Fig. 6.  
The box width and height permit the calculus of a ratio, 
which is the feature used to classify the crack defect. A 
common crack presents an elongated shape, resulting in a 
ratio typically higher than a pre established threshold (RT). 
When the system identifies a defect that is not a crack, that 
contour is eliminated from the sequence. At the end, the 
algorithm returns only the cracks contours.  
 
B. Algorithms for granule defect detection 
Fig. 7 describes the Granule defect detection algorithms. 
The segmentation uses an adaptive thresholding algorithm 
[9,10]. This is a convenient approach taken in account the 
small size of these defects and the illumination conditions 
that are difficult to make completely uniform. In this 
method, the image is subdivided in regions, in such a way 
that the illumination is more uniform in each region. This 
Gaussian 
Filter  
Gradient 
Filter  
Image 
Smoothing 
Enhancement 
Non-Maximum 
Suppression 
 
 
Thresholding Detection 
Contour 
Image 
Gray Scale Conversion 
Canny Edge  
Dilate image 
Find Contours 
Get Cracks 
Original 
defect image
Segmented 
Image
Crack defect 
Contours
Segmentation algorithm  
Identification algorithm 
Segmentation 
Identification 
Defect  
Image 
 
 
 
Defect 
Segmented  
Image 
can be obtained by calculating a threshold for each pixel. 
The threshold was determined by examining the 
neighborhood and extracting a statistical value, specifically 
the mean value of the intensity dispersion. The size of the 
neighborhood is a compromise as it has to be large enough 
to get a significant statistical value and small enough to 
maintain the uniformity of the illumination. The result is a 
binary image, further submitted to an open morphological 
operator. That eliminates the noise remnant from the 
binarization, concluding the segmentation process.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 5. Block diagram of the Get Cracks algorithm. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 6. Image of the Contours and a 2D Box of the defects. 
 
The identification phase begins applying the segmented 
image to the identification algorithm developed for cracks 
(described previously). After extracting the contours of the 
potential cracks, they are drawn in a filled way, which 
results in a new image with black background and white 
filled contours. After a dilatation operator, the segmented 
image holding only granules is obtained by applying an OR 
logical operation between the segmented image (with all 
defects) and the potential crack image. In this way it is 
possible to exclude the crack areas of the original image. 
Finally the contours of the granules are obtained through 
the Find Contours process. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 7. Block diagram of granule defect detection algorithms: 
Segmentation algorithm; Identification algorithm.  
 
C. Algorithms for relief surface detection 
The detection of these defects is base on the principle of 
specular reflection [11]. When a very smooth surface is 
illuminated, it is possible to acquire a bright area, with the 
same color of the illuminator that is caused by specular 
reflection. This is dependent of the point of visualization. 
Fig. 8 illustrates this principle when it is applied to a plate 
with and without relief surface defects. 
 
 
 
 
 
 
 
 
Fig. 8. Specular reflection at plate surfaces. 
Gray Scale Conversion 
Adaptive  
Thresholding  
Open  
Operator 
Crack Defect 
Detection 
(Identification) 
Draw Filled Contours 
in new black image 
Dilate 
OR 
Find Contours 
Original 
defect image 
Segmented 
Image 
Granules defect 
Contours 
Segmentation algorithm  
Identification algorithm  
Crack defect 
Contours 
Last Contour? 
Box2D in contour 
Calculate ratio  
ratio=box width / box height 
Not crack? 
ratio<RT 
Eliminate Contour 
Next 
Contour 
yes 
yes 
no 
no 
Defects 
Contours 
Crack defect 
Contours 
 
Illumination rays 
Specular reflection  
Surface without defect     Surface with defect 
Fig. 9 shows real illuminated images of plate surfaces, 
with and without relief, obtained using specular reflection. 
Since in the image with defect the surface is not smooth, the 
reflection will have a higher dispersion than in the image 
without defect. Therefore to make a distinction between 
these two situations it was necessary to define a region to 
process - region of interest (ROI), as shown in Fig. 9 
between the two lines. The level of dispersion in this area 
gives indication about the presence and respective degree of 
relief.  
 
 
 
 
 
 
 
 
Fig. 9. Image with (left) and without (right) relief surface defect, affected 
by specular reflection. 
 
The detection algorithm comprises an approach that 
extracts statistical values from the binarized ROI image and 
an approach that extracts statistics from the gray level ROI 
image (Fig. 10).  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 10. Block diagram of relief surface detection Algorithm. 
 
In the first approach, the binarization is accomplished by 
defining a static threshold value. From the segmented image 
it is possible to obtain statistical information used to 
quantify the relief dispersion, such as the number of pixels 
in the ROI that have zero value and a spatial mean of all 
these pixels.  
In the second approach (without binarization), the mean 
and standard deviation of the intensity of the ROI pixels are 
calculated.  
Statistics obtained by the two approaches must then be 
evaluated concerning their inclusion within a specific range. 
A plate will be considered without relief defect if all the 
values are outside the defined range. 
 
III. RESULTS AND DISCUSSION 
The system comprises two different setups: one for the 
detection of cracks and granules and another for the relief 
surface evaluation. In both cases the plates are moving in a 
belt convey passing under the illumination system.  
In the first case it is important to minimize problems, 
such as flicker and specular reflections. For that a specific 
illumination setup was projected. The illumination is based 
on LEDS light, in a frontal position. This must simulate a 
kind of cloudy day light, which was achieved with a white 
calotte (Fig. 11) in a diffuse material where fluorescent 
light points towards the calotte interior. The result is an 
image with a high degree of illumination uniformity on the 
plate. A global plate image is acquired by an Allied VC 
OSCAR F810C progressive camera (res: 3288x2470 pixels) 
with a 16mm lenses and a firewire acquisition board.  
In the case of relief surface detection for each acquisition 
only a ROI of the image plate is inspected. To cover the 
entire plate several acquisitions must be performed, as in a 
linear acquisition process. The setup is based on a bar of 
LEDS and in an Allied VC MERLIN F-033C progressive 
camera (res: 656x494 pixels) with a 16mm lenses and a 
firewire acquisition board. The light source and the camera 
were positioned as illustrated in fig. 8. This configuration 
creates the desired specular reflection on the plate image. 
The application was developed with Microsoft Visual 
C++ and with the OpenCv Library [12] that includes several 
image processing and computer vision algorithms. Two 
modules were included: one to parameterize the system 
establishing the parameters for the image processing 
algorithms and a module to carry out the detection of the 
defects. 
Several supervised tests were performed with the 
described setups and using a set of known samples (500) 
representing the diversity of situations concerning types, 
dimensions and spatial distribution of defects. This allowed 
determining the parameters, for each algorithm, that best 
discriminate the three types of defects (Table I and II).  
 
 
 
Set ROI 
Gray Scale Conversion 
Statistic values 
(Mean, Standard 
Deviation)
Values are in a 
specific range? 
yes no 
Original 
defect image 
ROI with  
defect 
ROI without  
defect 
Binarization 
Statistic values 
(Count Zero, Spatial 
Mean) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 11. Images of the calotte used on the setup for the detection of cracks 
and granules. 
 
TABLE I 
SYSTEM PARAMETERS FOR CRACK AND GRANULE DETECTION. 
Canny Operator   
T1 T2 
Ratio 
Threshold (RT) 
Neighborhood 
Size 
Crack 42 126 2 -  
Granule - - 2 35 
 
TABLE II 
SYSTEM PARAMETERS FOR RELIEF SURFACE DETECTION. 
 Lower Limit Upper Limit 
ROI 141 197 
Binarization Approach 
 Threshold Spatial Mean Count Zero 
Range (Fig. 10) 180 <140 >30000 
Without Binarization Approach 
 Mean Standard deviation 
Range (Fig. 10) >160 >10 
 
Figs. 12 and 13 exemplifies the application of the system 
to detect cracks and granules, respectively. In both, the final 
result is a sequence of image points that defines the 
contours of the defects.  
Fig. 14 illustrates an application example for relief 
surface detection in accordance with the algorithm of fig. 
10. This figure shows the original images for a defective 
(Fig. 14a) and a non defective (Fig. 14b) plate and the 
respective segmented images using the binarization process. 
Table III presents the statistic values that describe the ROI 
marked in the original images. Comparing these statistics 
with the values specified in table II it is possible to 
demonstrate the presence of the defective plate. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 12. Sequence of images for the crack defect; from top: original, 
segmented and the defect contour drawn in the original image. 
 
 
Fig. 13. Sequence of images for the granule defect; from top: original, 
segmented and the defect contour drawn in the original image. 
 
 
 
Segmentation 
Identification 
Crack defect 
Crack defect 
Granules 
defect 
Binary Logic 
Operation 
Granules defect 
detected 
Segmentation 
Identification 
Crack defect 
detected 
Crack defect 
Granules  
defect 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 14. Original and segmented plate images. a) Defective surface; b) Non 
defective surface. Solid line defines the ROI. Dashed line represents the 
spatial mean. 
 
TABLE III 
APPLICATION RESULTS FOR THE EXAMPLES FROM FIG. 14. 
 Defective Non Defective 
Mean 175 157 
Standard Deviation 19 8 
Count Zero 62276 25190 
Spatial Mean 136 172 
 
IV. CONCLUSIONS 
This paper describes a computer vision system dedicated 
to identify the more relevant defects that were found to 
depreciate the ceramic plates from Portuguese industry: 
cracks, granules and the presence of relief surfaces. 
For the different types of defects, specific image 
processing and analysis algorithms were implemented as 
well as laboratory setups representing industrial conditions, 
namely concerning illumination. For each defect a set of 
parameters were identified to allow a higher flexibility of 
the system.  
The tests performed with the lab prototype suggest good 
performance. Therefore, this work demonstrated the 
viability for implementing an automatic quality control 
solution based on visual inspection, which will fulfill a 
technological demand that persists in this industrial sector.  
 
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http://repositorium.sdum.uminho.pt/bitstream/1822/16574/1/C27-Computer%20Vision%20Techniques%20Applied%20to%20the%20Quality%20Control%20of%20Ceramic%20Plates.pdf

Computer vision techniques applied to the quality control of ceramic plates

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Universidade do Minho: RepositoriUM