**4. Artifacts study by intelligent image processing**

#### **4.1 Case study 1: tablet from Tartaria**

Discovered in 1961 on an archeological site in the town of Tărtăria in Alba County, Romania, the objects known as the tablets from Tărtăria represent three ceramic pieces (made of loam).

The pieces, which were dated around 5300 BC by German researcher Harald Haarmann [13] have similar symbols to the Vinča culture, being the subject of numerous and controversial polemics among archeologists everywhere, since (in some opinion) the tablet is the oldest form of writing in the world. One of the tablets, the one of discoidal form, comprises four groups of signs, separated by lines. It is considered the closest to a true script with ancient symbols. Much of the signs contained in it are found in the letters contained in the Greek archaic inscriptions (but also in the Phoenician, Etruscan, Old Italian, Iberian writings). We have chosen for our study this engraved disc-shaped lenticular tablets with a diameter of 6 cm.

#### *4.1.1 Preliminary analysis of chromatic components*

It started from an RGB-captured image with the resolution 483 × 478 pixels under certain lighting conditions, available on the web [14]. The three-dimensional RGB data structure is accessible by reading the image with an application program and allowing it to be displayed as shown in **Figure 3**. Pixel values in terms of base chromatic components depend on lighting conditions so they include the effect of ambient light and any additional light sources. In this case, we are dealing with a three-dimensional object over which ambient light and especially additional sources produce significant effects on the image obtained. Therefore, we note first that color information is relative and uncertain as long as the exact pattern of illumination is unknown. Secondly, we notice effects on the perception of the real shape of the object and some shape details under the influence of the particular illumination pattern. Thus, uneven illumination, partial shading, glare as the effect of local dispersion on irregular surfaces, and the effect of roughness and texture add uncertainty to the perception of the object's appearance on the basis of optical means.

Under these conditions, the image analysis can be applied with the following features:


A first quantitative analysis of the color composition in the image can be done with the instrument called the image *histogram* applied to the RGB base components. This is the distribution of the values of all the pixels in the image on each color component. For the studied artifact, the histogram of its image on the three components is shown in **Figure 4**.

It can be easily observed by evaluating dimensional graphs that red is the component with the highest weight, and blue with the smallest. This translates into the brightness (intensity) of the image in planes R, G, and B, as can be seen in **Figure 5**.

**39**

**Figure 5.**

**Figure 4.**

**Figure 3.**

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination*

*DOI: http://dx.doi.org/10.5772/intechopen.80615*

*First tablet from Tartaria. Picture (483 × 478 pixels).*

*Histogram of the image on the base components.*

*Images separated on RGB base components.*

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination DOI: http://dx.doi.org/10.5772/intechopen.80615*

**Figure 3.** *First tablet from Tartaria. Picture (483 × 478 pixels).*

*Advanced Methods and New Materials for Cultural Heritage Preservation*

Discovered in 1961 on an archeological site in the town of Tărtăria in Alba County, Romania, the objects known as the tablets from Tărtăria represent three

The pieces, which were dated around 5300 BC by German researcher Harald Haarmann [13] have similar symbols to the Vinča culture, being the subject of numerous and controversial polemics among archeologists everywhere, since (in some opinion) the tablet is the oldest form of writing in the world. One of the tablets, the one of discoidal form, comprises four groups of signs, separated by lines. It is considered the closest to a true script with ancient symbols. Much of the signs contained in it are found in the letters contained in the Greek archaic inscriptions (but also in the Phoenician, Etruscan, Old Italian, Iberian writings). We have chosen for our study this engraved disc-shaped lenticular tablets with a diameter of

It started from an RGB-captured image with the resolution 483 × 478 pixels under certain lighting conditions, available on the web [14]. The three-dimensional RGB data structure is accessible by reading the image with an application program and allowing it to be displayed as shown in **Figure 3**. Pixel values in terms of base chromatic components depend on lighting conditions so they include the effect of ambient light and any additional light sources. In this case, we are dealing with a three-dimensional object over which ambient light and especially additional sources produce significant effects on the image obtained. Therefore, we note first that color information is relative and uncertain as long as the exact pattern of illumination is unknown. Secondly, we notice effects on the perception of the real shape of the object and some shape details under the influence of the particular illumination pattern. Thus, uneven illumination, partial shading, glare as the effect of local dispersion on irregular surfaces, and the effect of roughness and texture add uncertainty

to the perception of the object's appearance on the basis of optical means.

Under these conditions, the image analysis can be applied with the following

ii.Using multiple images taken from different perspectives on the object.

i.Under controlled and uniform lighting conditions on stands specific to pho-

iii.Performing analysis on image portions with particular adjustment/selection

A first quantitative analysis of the color composition in the image can be done with the instrument called the image *histogram* applied to the RGB base components. This is the distribution of the values of all the pixels in the image on each color component. For the studied artifact, the histogram of its image on the three

It can be easily observed by evaluating dimensional graphs that red is the component with the highest weight, and blue with the smallest. This translates into the brightness (intensity) of the image in planes R, G, and B, as can be seen in **Figure 5**.

**4. Artifacts study by intelligent image processing**

**4.1 Case study 1: tablet from Tartaria**

*4.1.1 Preliminary analysis of chromatic components*

tometric measurements.

components is shown in **Figure 4**.

of parameters for the investigated area.

ceramic pieces (made of loam).

6 cm.

features:

**38**

**Figure 4.** *Histogram of the image on the base components.*

**Figure 5.** *Images separated on RGB base components.*

Other qualitative information provided by histograms refer to their shape that tells us that the image does not contain pure green and blue components (value 255 is not reached), and their maximum concentrations are grouped at low (below 50), so with relatively low brightness.

Also, the red component is predominantly in relation to the other two at high brightness values (over 200), which confirms the chromatic aspect to the "hot" components of the object in **Figure 3**. It is obvious that in this statistic the pixels in the dark background, which do not belong to the object, also mattered. These can be largely eliminated through a level threshold filter and by matching the object within a suitable round frame. The histogram can be taken into consideration as an element in the formation of the structural-chromatic pattern of the artifact.

The second image analysis is done in the perceptual color space, which reveals the three essential characteristics specific to human visual analyzer: hue or color tones, color saturation or purity, and their brightness or illuminance. To begin with, convert the primary color image into the perceptual color space, for example, in the HSV system. The result of these transformations is shown in **Figure 6** by representing each of the hue, saturation, and value components in the corresponding images. The analysis is useful both qualitatively and quantitatively at the equivalent numerical data calculated by transforming RGB → HSV and interpreted in the coordinate system of **Figure 2**. To begin with, the image is poor in hues, supported by the uniformity of the numerical data of the hue parameter, which are massively grouped in the vicinity of the S-axis, so around the red component. Secondly, there is a saturation of gray-level typical components, the average calculated for the whole image being 0.5826 and a standard deviation of 0.014, which confirms insignificant information provided by saturation. Finally, the value illuminance component provides perceptually the most complete information, comparable to the red image plan (mean 0.5310 and standard deviation 0.0795).

We continue to focus the analysis on a distinct portion virtually cropped from the original image of the artifact, shown in **Figure 3**. The selected portion shows significant details of the artifact and is more uniform in lighting and chromaticity. The histograms in **Figure 7a** show a better concentration of RGB components around the maxima and the disappearance of the low luminance components compared to the histograms of the whole image of the artifact. The hue information H is insignificant, and in this case, the numerical values are very small (mean 0.0891) and the extremely small standard deviation (0.0208). Saturation is slightly higher than the whole image with an average of 0.6311, but the standard deviation is only 0.0103; thus, there is enough information uncertainty, which can also be seen from the S-plane image of **Figure 7b**. Finally, the perceptual luminance component with a higher average than the previous one of 0.6912 and the standard deviation 0.0415 provides the correct information, similar to the chromatic components red and green.

**41**

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination*

For three-dimensional artifacts, estimating forms is a complex problem characterized in most cases by uncertainty. The uncertainty comes mainly from two sources: (i) data accuracy, which in the case of images is affected by nonhomogeneous illumination, respectively and (ii) the lack of specific processing models and algorithms. In practice of authentication and especially restoration preservation, the physical presence of objects is indispensable. However, in the stage of elaboration of the structural-chromatic pattern of artifacts, the extraction of some form characteristics and their quantification may be reliable provided that image acquisi-

In the following we will show how to evaluate some shape property measure-

• The image obtained is filtered, for example, by applying, for instance, media-

• The new image converts to binary (black-and-white image) based on a thresh-

b.An algorithm is used to identify pixel regions that enjoy the same property by

• Check the connectivity of the pixels with the neighbors in variant "4-connect"

• Label the linked regions as the list of component objects of the image.

The method involves three steps, which are described as follows:

a.Preliminary processing in the color space by following the next steps:

tion is made in optimal and reproducible conditions.

• The original image is converted to grayscale.

tion filter of order 2.

following the following steps:

old of intensity.

or "8-connect."

ments for already studied tablet from Tărtăria.

*DOI: http://dx.doi.org/10.5772/intechopen.80615*

*4.1.2 Shape analysis*

*Color analysis of a detail.*

**Figure 7.**

**Figure 6.** *Image components in the hue-saturation-value perceptual system.*

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination DOI: http://dx.doi.org/10.5772/intechopen.80615*

**Figure 7.** *Color analysis of a detail.*

*Advanced Methods and New Materials for Cultural Heritage Preservation*

in the formation of the structural-chromatic pattern of the artifact.

the red image plan (mean 0.5310 and standard deviation 0.0795).

with relatively low brightness.

Other qualitative information provided by histograms refer to their shape that tells us that the image does not contain pure green and blue components (value 255 is not reached), and their maximum concentrations are grouped at low (below 50), so

Also, the red component is predominantly in relation to the other two at high brightness values (over 200), which confirms the chromatic aspect to the "hot" components of the object in **Figure 3**. It is obvious that in this statistic the pixels in the dark background, which do not belong to the object, also mattered. These can be largely eliminated through a level threshold filter and by matching the object within a suitable round frame. The histogram can be taken into consideration as an element

The second image analysis is done in the perceptual color space, which reveals the three essential characteristics specific to human visual analyzer: hue or color tones, color saturation or purity, and their brightness or illuminance. To begin with, convert the primary color image into the perceptual color space, for example, in the HSV system. The result of these transformations is shown in **Figure 6** by representing each of the hue, saturation, and value components in the corresponding images. The analysis is useful both qualitatively and quantitatively at the equivalent numerical data calculated by transforming RGB → HSV and interpreted in the coordinate system of **Figure 2**. To begin with, the image is poor in hues, supported by the uniformity of the numerical data of the hue parameter, which are massively grouped in the vicinity of the S-axis, so around the red component. Secondly, there is a saturation of gray-level typical components, the average calculated for the whole image being 0.5826 and a standard deviation of 0.014, which confirms insignificant information provided by saturation. Finally, the value illuminance component provides perceptually the most complete information, comparable to

We continue to focus the analysis on a distinct portion virtually cropped from the original image of the artifact, shown in **Figure 3**. The selected portion shows significant details of the artifact and is more uniform in lighting and chromaticity. The histograms in **Figure 7a** show a better concentration of RGB components around the maxima and the disappearance of the low luminance components compared to the histograms of the whole image of the artifact. The hue information H is insignificant, and in this case, the numerical values are very small (mean 0.0891) and the extremely small standard deviation (0.0208). Saturation is slightly higher than the whole image with an average of 0.6311, but the standard deviation is only 0.0103; thus, there is enough information uncertainty, which can also be seen from the S-plane image of **Figure 7b**. Finally, the perceptual luminance component with a higher average than the previous one of 0.6912 and the standard deviation 0.0415 provides the correct information, similar to the chromatic components red

**40**

**Figure 6.**

*Image components in the hue-saturation-value perceptual system.*

and green.

#### *4.1.2 Shape analysis*

For three-dimensional artifacts, estimating forms is a complex problem characterized in most cases by uncertainty. The uncertainty comes mainly from two sources: (i) data accuracy, which in the case of images is affected by nonhomogeneous illumination, respectively and (ii) the lack of specific processing models and algorithms. In practice of authentication and especially restoration preservation, the physical presence of objects is indispensable. However, in the stage of elaboration of the structural-chromatic pattern of artifacts, the extraction of some form characteristics and their quantification may be reliable provided that image acquisition is made in optimal and reproducible conditions.

In the following we will show how to evaluate some shape property measurements for already studied tablet from Tărtăria.

The method involves three steps, which are described as follows:


Thus, for our artifact, after the application of steps (a) and (b), a number of 107 component objects resulted in the binary image of **Figure 8**. These objects were detected as image regions above the conversion intensity threshold, and so all the pixels belonging to them were changed to 1 (white). Therefore, the image is analyzed for brighter areas. In step (c), it was decided to select the objects by filtering according to the Area\_object > 200 criterion, and so only five regions corresponding to objects highlighted and numbered in the order of their extraction by the algorithm were retained. These objects are marked in **Figure 8** by their centers of weight designated (see red points) and labels. The list of calculated properties of the five components considered is presented in **Table 4**.

For the analysis of the engraved symbols on the artifact, the darker areas will be those of interest. Therefore, it is necessary to perform a reversal of the binary image by the complementary operation so that the regions of interest (the darker depth) pass into 1 (white) and can be evaluated as in the previous situation. **Figure 9** shows the result of complementing the initial image and marking the detected forms with the position of the calculated center of weight. In the middle image, all regions detected are marked (with blue dot), most of which belongs to light scattering small areas. In the picture on the right, the number of interest regions was dramatically reduced by applying the area filter (Area\_object > 200). Most of the parasitic areas were removed, and the remaining ones belong to the symbols of interest and were marked in the red points. It is true that based on the image available for processing, not all of the symbols have been detected. It can easily be noticed that due to uneven illumination, the shaded area on the lower-left side of the image requires separate treatment.

**43**

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination*

**Region no. Area Perimeter Orientation Eccentricity Center of weight**

 111,222 4065.5 57.88 0.5486 201.64 220.55 222 100.6 36.87 0.9665 312.93 441.57 427 115.6 −74.43 0.9270 313.74 390.27 313 187.2 55.68 0.9769 356.02 400.18 611 206.4 85.96 0.9641 370.17 332.94

**Xc Yc**

Data obtained through shape analysis complements the information on the structure of the artifact. They can serve as a basis for comparison in the process of authenticating artifacts or evaluating their alteration (degradation) over time. The usefulness of these data depends on the uniformity of image acquisition conditions,

Famous painted works of art are most commonly reproduced for commercial purposes. Combating illicit activities with paintings implies the development of the most effective methods for examining them for the purposes of authenticating them and detecting counterfeits. The restoration in terms of rehabilitation of the paintings, in the sense of intervention on the painting itself, does not apply to the easel works than in some exceptional cases in accordance with national legislation and the European convention for cultural heritage protection. Reconditioning and even reproduction (restoration) are fully practiced in the case of monumental paintings, most often in the case of frescoes. In both categories of problems, knowing the original is essential for obtaining the necessary reference data in the authentication process or reproduction for restoration purposes. To exemplify the proposed methods, we chose from the easel painting category the Claude Monet's work from 1897 to 1899 entitled "Water Lilies and Japanese Bridge." The original painting is oil on canvas with the dimensions of 89.7 × 90.5 cm and is exhibited at the Princeton University Art Museum at the Department of Modern and Contemporary Art. To apply the method, we chose the original jpeg file with the 1031 × 1001 pixel resolution shown in **Figure 10** [15]. The preliminary analysis reveals from the chromatic histograms (see **Figure 11**) that the predominant luminosity is green, but as a

**4.2 Case study 2: C. Monet's paint "Water Lilies and Japanese Bridge"**

*DOI: http://dx.doi.org/10.5772/intechopen.80615*

**Table 4.**

**Figure 9.**

*Numerical shape properties.*

the most sensitive factor being illumination.

*Detecting the engraved forms on the complementary image.*

**Figure 8.** *Image of the position of selected objects.*


*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination DOI: http://dx.doi.org/10.5772/intechopen.80615*

**Table 4.** *Numerical shape properties.*

*Advanced Methods and New Materials for Cultural Heritage Preservation*

artifact that have a larger area than a required threshold.

area on the lower-left side of the image requires separate treatment.

• A list of shape property measurements is made.

components considered is presented in **Table 4**.

c.Calculate the different properties of the component objects in the image:

• Additional filters for object selection apply, for example, regions detected on

Thus, for our artifact, after the application of steps (a) and (b), a number of 107 component objects resulted in the binary image of **Figure 8**. These objects were detected as image regions above the conversion intensity threshold, and so all the pixels belonging to them were changed to 1 (white). Therefore, the image is analyzed for brighter areas. In step (c), it was decided to select the objects by filtering according to the Area\_object > 200 criterion, and so only five regions corresponding to objects highlighted and numbered in the order of their extraction by the algorithm were retained. These objects are marked in **Figure 8** by their centers of weight designated (see red points) and labels. The list of calculated properties of the five

For the analysis of the engraved symbols on the artifact, the darker areas will be those of interest. Therefore, it is necessary to perform a reversal of the binary image by the complementary operation so that the regions of interest (the darker depth) pass into 1 (white) and can be evaluated as in the previous situation. **Figure 9** shows the result of complementing the initial image and marking the detected forms with the position of the calculated center of weight. In the middle image, all regions detected are marked (with blue dot), most of which belongs to light scattering small areas. In the picture on the right, the number of interest regions was dramatically reduced by applying the area filter (Area\_object > 200). Most of the parasitic areas were removed, and the remaining ones belong to the symbols of interest and were marked in the red points. It is true that based on the image available for processing, not all of the symbols have been detected. It can easily be noticed that due to uneven illumination, the shaded

**42**

**Figure 8.**

*Image of the position of selected objects.*

**Figure 9.** *Detecting the engraved forms on the complementary image.*

Data obtained through shape analysis complements the information on the structure of the artifact. They can serve as a basis for comparison in the process of authenticating artifacts or evaluating their alteration (degradation) over time. The usefulness of these data depends on the uniformity of image acquisition conditions, the most sensitive factor being illumination.
