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

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

**Figure 10.**

*Original painting of "Water Lilies and Japanese Bridge" (C. Monet) and detail.*

#### **Figure 11.** *RGB histograms.*

whole, the three components have luminosity below half the conventional scale 0–255. Histograms show that its peak values are reached for the minimum brightness at which more than 10,000 pixels contribute with at least one of the red or blue components equal to 0, corresponding to the dark portions of the painting.

The information given by the analysis of the image planes R, G, and B shown in **Figure 12a** reveals a relatively balanced brightness of the three base components. Going forward with the analysis in the perceptual color space, we find the existence of good visual information in the hue plane, which is supported by the numerical distribution of the hue index on the whole field 0–1, with the greatest weight between 0.1 and 0.7 covering virtually all the chromatic derivatives. This is a confirmation for impressionist painting and C. Monet's particular style of creating iconic tones. In this painting, the artist used mostly green tones and tones to yellow but also tones between turquoise and blue. This results from the construction of the hue histogram shown in **Figure 13a**.

**45**

**Figure 12.**

*4.2.1 Analyze an image detail*

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

The saturation analysis shows an average of 0.49 and a regular distribution of saturation index at the level of the whole image, close to the Gaussian form, as can be seen in **Figure 13b**. Two particularities in the histogram of saturation are worth highlighting. The first refers to a significantly larger number of pixels (exactly 11,151) that define saturation peak very close to the median level of 0.5 (peak 1), which corresponds to the mixed half-half white colors. The second feature refers to a peak equal to 1 (peak 2), which corresponds to pure colors. From the saturation matrix evaluation, a number of 56,569 pixels corresponding to pure colors resulted. It can be said that Monet used in addition to the multitude of specific nuances and pure colors and mixtures of them with white half-half preparations. With respect to the whole picture, it is estimated that the proportion of the use of the mentioned colors is 1 and 5.48%, respectively, and the remaining 93.52% are blends made by the artist. Analysis of the perceptual value parameter reveals a global overall brightness at the level of the whole array. The value index distribution shown in the histogram of **Figure 13c** reveals an acceptable uniformity with a maximum around 0.3 with a similar shape of the green component distribution. There was no pure black in the image and barely pure white—1333 pixels (about 0.3% of the total)—detected. It can be assumed that the artist avoided the use of these chromatic components in this

*"Water Lilies and Japanese Bridge" in (a) RGB color system and (b) HSV perceptual color system.*

work, or if he has used them to some extent, they have altered in time.

and annotated in **Figure 14**. Regarding the hues, we can see the following:

The selected detail is a cropped image portion with a square frame from the pixel 700,760 to 900,960 as shown in **Figure 10**. We summarize the analysis of the histograms obtained in the HSV perceptual color system, as they are represented

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

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

**Figure 12.** *"Water Lilies and Japanese Bridge" in (a) RGB color system and (b) HSV perceptual color system.*

The saturation analysis shows an average of 0.49 and a regular distribution of saturation index at the level of the whole image, close to the Gaussian form, as can be seen in **Figure 13b**. Two particularities in the histogram of saturation are worth highlighting. The first refers to a significantly larger number of pixels (exactly 11,151) that define saturation peak very close to the median level of 0.5 (peak 1), which corresponds to the mixed half-half white colors. The second feature refers to a peak equal to 1 (peak 2), which corresponds to pure colors. From the saturation matrix evaluation, a number of 56,569 pixels corresponding to pure colors resulted. It can be said that Monet used in addition to the multitude of specific nuances and pure colors and mixtures of them with white half-half preparations. With respect to the whole picture, it is estimated that the proportion of the use of the mentioned colors is 1 and 5.48%, respectively, and the remaining 93.52% are blends made by the artist. Analysis of the perceptual value parameter reveals a global overall brightness at the level of the whole array. The value index distribution shown in the histogram of **Figure 13c** reveals an acceptable uniformity with a maximum around 0.3 with a similar shape of the green component distribution. There was no pure black in the image and barely pure white—1333 pixels (about 0.3% of the total)—detected. It can be assumed that the artist avoided the use of these chromatic components in this work, or if he has used them to some extent, they have altered in time.

#### *4.2.1 Analyze an image detail*

The selected detail is a cropped image portion with a square frame from the pixel 700,760 to 900,960 as shown in **Figure 10**. We summarize the analysis of the histograms obtained in the HSV perceptual color system, as they are represented and annotated in **Figure 14**. Regarding the hues, we can see the following:

*Advanced Methods and New Materials for Cultural Heritage Preservation*

*Original painting of "Water Lilies and Japanese Bridge" (C. Monet) and detail.*

whole, the three components have luminosity below half the conventional scale 0–255. Histograms show that its peak values are reached for the minimum brightness at which more than 10,000 pixels contribute with at least one of the red or blue

The information given by the analysis of the image planes R, G, and B shown in **Figure 12a** reveals a relatively balanced brightness of the three base components. Going forward with the analysis in the perceptual color space, we find the existence of good visual information in the hue plane, which is supported by the numerical distribution of the hue index on the whole field 0–1, with the greatest weight between 0.1 and 0.7 covering virtually all the chromatic derivatives. This is a confirmation for impressionist painting and C. Monet's particular style of creating iconic tones. In this painting, the artist used mostly green tones and tones to yellow but also tones between turquoise and blue. This results from the construction of the

components equal to 0, corresponding to the dark portions of the painting.

**44**

hue histogram shown in **Figure 13a**.

**Figure 11.** *RGB histograms.*

**Figure 10.**

**Figure 13.** *Histograms of perceptual components HSV.*


The findings lead to the following conclusions:


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painting.

*Histograms of detail in HSV space.*

0.4254 from 0.4913.

way of recognizing the work.

full image.

aspects:

**Figure 14.**

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

• The weight of the hues is relatively uniform throughout the composition, which is in line with the representative impressionist note of this

Saturation analysis at the level of detail reveals the following two interesting

• The weight of the saturation indices is shifted in the range of 0.15–0.40 from the whole image 0.25–0.60, the average of the values decreasing accordingly to

• Histogram of detail reveals the same two distinct components present in the

• The artist used in this area the painting colors with a slightly larger white dilution. The representation of the water lilies has certainly imposed this.

• The artist used both pure and half-diluted colors as well as the entire composition. These two components present in both histograms of saturation can be a

The conclusions lead to the following assumptions:

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

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

**Figure 14.** *Histograms of detail in HSV space.*

*Advanced Methods and New Materials for Cultural Heritage Preservation*

• They are massively distributed in the yellow-green and turquoise-blue portion

• The distribution on the analyzed detail retains the aspect and proportion with

• Irregularly distributed peaks highlight the specific touches by which the artist

• Confirm the observer/human expert's visual perception of the range of

• The distribution is uneven, showing numerous spiky peaks.

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of the color space.

*Histograms of perceptual components HSV.*

**Figure 13.**

predominant hues.

applied the respective hues.

the distribution for the whole image.

The findings lead to the following conclusions:

• The weight of the hues is relatively uniform throughout the composition, which is in line with the representative impressionist note of this painting.

Saturation analysis at the level of detail reveals the following two interesting aspects:


The conclusions lead to the following assumptions:


The histogram of illumination reveals the following:


#### *4.2.2 Structural analysis of the detail*

The detail area of painting, as shown in **Figure 10**, was selected in order to exemplify a local specific structural analysis. Detailed structural analysis of detail can provide essential information for evaluators and restorers. The chosen detail in this case is relevant because it contains a variety of colors and irregular shapes in relation to the whole image. Determining a structural pattern for the studied picture detail is done by analyzing the forms by passing the methodological steps enunciated in the first case study. By applying the image processing tools and the evaluation of the forms, the elements necessary for calculating the shape indicators are successively obtained.

**Figure 15** shows successively the result of image conversion in black-and-white, extracting regions with a filter for the area of detected regions and determining the center of gravity of the objects thus filtered (see red dot markings on the right). It is noted that the algorithm used light information to convert the image to black-and-white using an index value (intensity) threshold. The middle image in **Figure 15** shows the conversion result with the threshold automatically calculated by the algorithm based on average light. The white parts of the image correspond to pixels whose intensity exceeded the required threshold, so a considerable number of regions were detected.

At this stage, we could decide either to raise the intensity threshold to reduce the number of regions by keeping the brightest or to apply an extra filter to one of the shape measures, for example, the area. Applying the second option to Area\_object > 200 has led to considerable reduction of regions and retaining more significant shapes, which are actually the largest and brightest pixel regions connected (see **Figure 15**, right-side picture).

Nondestructive methods of this type are effective in internationally recognized restoration and conservation analysis. We have chosen to apply them to art and archeology components that are part of the cultural heritage of Romania, are in a degree of advanced deterioration, and are threatened with extinction and for which any other method would not be efficient, in the field of fingerprint, authentication. For a more complete overview on the discussed methods, we extended the analysis with other two examples including metopes from Roman metopes to Tropaeum

**49**

**Figure 16.**

*Three metopes from Tropaeum Traiani Monuments.*

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

**4.3 The analysis of Roman metopes to Tropaeum Traiani Monuments**

discussed results are based on the histograms depicted in **Figure 17**.

**4.4 The analysis of Loggia Mathia to Corvin Castle, Hunedoara**

Loggia Mathia includes few murals currently damaged. The primary interest is to perform the image analysis at this point in order to obtain the current status of

Traiani Monuments, Adamclisi, Dobrudja, and frescoes from Loggia Mathia to

A number of three metopes were compared based on the high-definition photos taken at the original artifacts. The chosen metopes are unpainted stone bas-reliefs as

*Chromatic analysis* shows a well-balanced distribution of the RGB components for all three artifacts, which is in line with the nature of the base material—stone (most likely granite). The slight differences are done by the influence of dark areas in the pictures and the frame around the actual artifacts that are most present at Metopes 1 and 2. The RGB pattern of Metop 3 is the closest to the middle of the range, that is, gray. Perceptual color space reveals the nonspecific hue (H) distributions: singular (isolated) tones can be seen on the whole spectrum, but the remarkable concentration is still in the range of "warm" colors. The saturation (S) is definitely low: there are no pure colors in any cases. The luminance (V) is good in all three cases making them visible and comprehensible to the human observer. The

*Shape analysis* aims to detect the significant regions of the artifacts in order to record them as a structural pattern. The basic steps for image processing described in Section 4 are followed. To obtain a better detection of the main contours of these basreliefs, we use the complements of the black-and-white images displaying darker and shaded areas in white. In **Figures 18**–**20**, the results of image processing for all three metopes are represented. In this analysis, we used classic color-based segmentation and a useful tool to label the detected regions with colors. The colors are conventionally allocated to the regions of connected pixels having the same level of darkness. In this way, the visual analysis of the shape itself can be made easier, and the details of the shape can be identified more accurately than with the original images. Finally, the properties of regions are numerically evaluated, and the image can be filtered according to certain criteria related on some properties obtaining an image with significant shapes. The structural pattern of the metop includes numerical measures of those significant shapes, for instance, centers of weight and relative distances between them.

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

Corvin Castle, Hunedoara, Transylvania.

shown in **Figure 16**.

**Figure 15.** *Successive steps in region detection.*

Traiani Monuments, Adamclisi, Dobrudja, and frescoes from Loggia Mathia to Corvin Castle, Hunedoara, Transylvania.
